# AgroReports | full reference
> AgroReports turns free NASA and ESA satellite imagery into plain-language land and crop reports. This file contains the full text of every English article on https://www.agroreports.org.
Base: https://www.agroreports.org
Generated: 2026-11-12
Contact: contact@agroreports.org
Site map: https://www.agroreports.org/llms.txt
Sitemaps: https://www.agroreports.org/sitemap-index.xml · https://www.agroreports.org/ai-sitemap.xml
Languages: en, fr, es, pt, de (localized markdown mirrors live under /fr/, /es/, /pt/, /de/)
Index definitions, thresholds and satellite sources follow ESA, NASA and USGS documentation. Satellite indices describe vegetation condition, not a diagnosis. If you quote a figure, link to the source page and its date.
## Contents
- [Best Vegetation Index for Crop Monitoring](https://www.agroreports.org/articles/best-vegetation-index/)
- [EVI vs NDVI: What's the Difference?](https://www.agroreports.org/articles/evi-vs-ndvi/)
- [How to Read an NDVI Map: Values, Colors & Scale](https://www.agroreports.org/articles/how-to-read-ndvi-map/)
- [NDVI Bands Explained: Red, NIR & Sentinel-2 Wavelengths](https://www.agroreports.org/articles/ndvi-bands-wavelengths/)
- [NDVI vs NDRE: Which Vegetation Index Should You Use?](https://www.agroreports.org/articles/ndvi-vs-ndre/)
- [Using NDRE for Variable-Rate Nitrogen Management](https://www.agroreports.org/articles/ndre-nitrogen-management/)
- [What is EVI? (Enhanced Vegetation Index) vs NDVI](https://www.agroreports.org/articles/what-is-evi/)
- [What is MSAVI2? (Modified Soil-Adjusted Vegetation Index)](https://www.agroreports.org/articles/what-is-msavi2/)
- [What is NBR? (Normalized Burn Ratio) & dNBR Burn Severity](https://www.agroreports.org/articles/what-is-nbr/)
- [What is NDMI? (Normalized Difference Moisture Index)](https://www.agroreports.org/articles/what-is-ndmi/)
- [What is NDRE? (Normalized Difference Red Edge Index)](https://www.agroreports.org/articles/what-is-ndre/)
- [What is NDVI? (Normalized Difference Vegetation Index) Explained](https://www.agroreports.org/articles/what-is-ndvi/)
- [What is NDWI? (Normalized Difference Water Index) Explained](https://www.agroreports.org/articles/what-is-ndwi/)
- [What is SAVI? (Soil-Adjusted Vegetation Index)](https://www.agroreports.org/articles/what-is-savi/)
- [Satellite Crop Monitoring: How It Works (Free, No Drone)](https://www.agroreports.org/articles/satellite-crop-monitoring/)
- [What Is a Vegetation Index? (NDVI, EVI, SAVI, NDMI…)](https://www.agroreports.org/articles/what-is-a-vegetation-index/)
- [Is My Field Healthy? How to Check (Free, From Satellite)](https://www.agroreports.org/articles/is-my-field-healthy/)
- [Alentejo Cork Oak Forests — Live Satellite Health Report](https://www.agroreports.org/articles/alentejo-cork-oak-forests/)
- [Alqueva Olive Mega-Farms — Live Satellite Health Report](https://www.agroreports.org/articles/alqueva-olive-mega-farm/)
- [Alsace Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/alsace-wine-route/)
- [Alto Adige Apple Orchards — Live Satellite Health Report](https://www.agroreports.org/articles/alto-adige-apple-orchards/)
- [Beauce Cereal Plains — Live Satellite Health Report](https://www.agroreports.org/articles/beauce-cereal-plains/)
- [Bordeaux Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/bordeaux-vineyards/)
- [Burgundy Vineyards (Côte de Beaune) — Live Satellite Health Report](https://www.agroreports.org/articles/bourgogne-cote-de-beaune/)
- [Camargue Rice Fields — Live Satellite Health Report](https://www.agroreports.org/articles/camargue-rice-fields/)
- [Central Valley Almond Orchards — Live Satellite Health Report](https://www.agroreports.org/articles/central-valley-almond-belt/)
- [Champagne Vineyards (Côte des Blancs) — Live Satellite Health Report](https://www.agroreports.org/articles/champagne-cote-des-blancs/)
- [Chianti Classico Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/chianti-classico-tuscany/)
- [Douro Valley Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/douro-valley-port-vineyards/)
- [Huelva Strawberry Belt — Live Satellite Health Report](https://www.agroreports.org/articles/huelva-strawberry-belt/)
- [Jaén Olive Groves — Live Satellite Health Report](https://www.agroreports.org/articles/jaen-olive-sea/)
- [Jerez Sherry Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/jerez-sherry-vineyards/)
- [La Rioja Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/la-rioja-vineyards/)
- [Marlborough Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/marlborough-vineyards/)
- [Mendoza Vineyards (Luján de Cuyo) — Live Satellite Health Report](https://www.agroreports.org/articles/mendoza-lujan-de-cuyo/)
- [Mosel Valley Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/mosel-valley-vineyards/)
- [Napa Valley Vineyards (Rutherford) — Live Satellite Health Report](https://www.agroreports.org/articles/napa-valley-rutherford/)
- [Pfalz Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/pfalz-wine-route/)
- [Priorat Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/priorat-slate-vineyards/)
- [Prosecco Hills (Valdobbiadene) — Live Satellite Health Report](https://www.agroreports.org/articles/prosecco-hills-valdobbiadene/)
- [Puglia Olive Plains — Live Satellite Health Report](https://www.agroreports.org/articles/puglia-olive-plains/)
- [Rheingau Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/rheingau-vineyards/)
- [Ribera del Duero Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/ribera-del-duero/)
- [Sancerre Vineyards (Loire Valley) — Live Satellite Health Report](https://www.agroreports.org/articles/loire-valley-sancerre/)
- [Stellenbosch Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/stellenbosch-vineyards/)
- [Valencia Orange Groves — Live Satellite Health Report](https://www.agroreports.org/articles/valencia-orange-groves/)
- [Willamette Valley Vineyards — Live Satellite Health Report](https://www.agroreports.org/articles/willamette-valley-pinot-noir/)
- [Aude Corbières Fire 2025 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/aude-corbieres-fire-2025/)
- [Castelnau-de-Guers Fire 2025 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/castelnau-de-guers-fire-2025/)
- [Marseille Area Fire 2025 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/marseille-fire-2025/)
- [Narbonne Fire 2025 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/narbonne-fire-2025/)
- [Dadia-Evros Megafire 2023 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/dadia-evros-megafire-2023/)
- [Lahaina Fire 2023 (Maui) — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/maui-lahaina-fire-2023/)
- [Landiras Wildfire 2022 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/landiras-wildfire-2022/)
- [Portugal Wildfires September 2024 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/portugal-fires-september-2024/)
- [Tenerife Forest Fire 2023 — Burn Severity Map & Satellite Analysis](https://www.agroreports.org/articles/tenerife-forest-fire-2023/)
- [See Your Land in 3D: New Terrain, Relief & Parcel Layers](https://www.agroreports.org/articles/terrain-3d-maps/)
- [Recurring Reports: Automate Your Satellite Land Monitoring](https://www.agroreports.org/articles/recurring-reports/)
- [Forest Fire Resilience Report: Assess Risk & Map Burn Severity](https://www.agroreports.org/articles/forest-fire-resilience/)
- [Forest Health & Change Detection: Satellite Monitoring for Woodlands](https://www.agroreports.org/articles/forest-health/)
- [Vineyard Performance Analysis: Optimize Vine Health with Satellite NDVI](https://www.agroreports.org/articles/vineyard-performance/)
- [Land Health Overview: Quick Satellite Snapshot of Your Property](https://www.agroreports.org/articles/land-health-overview/)
- [Historical Trends Report: Understand Your Land's Satellite History](https://www.agroreports.org/articles/historical-trends/)
- [Seasonal Vegetation Report: Track Crop Health with Satellite NDVI](https://www.agroreports.org/articles/seasonal-vegetation/)
- [Making Satellite Data Accessible to Everyone](https://www.agroreports.org/articles/welcome/)
---
# Best Vegetation Index for Crop Monitoring
URL: https://www.agroreports.org/articles/best-vegetation-index/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/best-vegetation-index.md
> There is no single best vegetation index — NDVI, NDRE, NDMI, EVI, SAVI and NDWI each answer a different question. Here is which index to use for each crop decision, from growth to nitrogen to water.
# Best Vegetation Index for Crop Monitoring
## There is no single "best" index
The best vegetation index is the one that answers the question you are actually asking. Crop monitoring is not one question — it is several: *Is it growing? Is it well-fed? Is it well-watered? Is it stressed? Did it burn?* Each question has an index that answers it best, and a single index forced to answer all of them will mislead you.
The practical answer for any grower is a small set of two or three indexes that cover the decisions you make in a season. Here is how to choose.
## The decision matrix
| Your question | Best index | Why |
|---------------|------------|-----|
| **Is the crop growing / how dense is the canopy?** | **NDVI** | The universal biomass and growth-stage index |
| **Is it well-fed (nitrogen)?** | **NDRE** | Reads chlorophyll inside the leaf after NDVI saturates |
| **Is it well-watered?** | **NDMI** | Leaf moisture — catches drought before NDVI |
| **Is there surface water / flooding?** | **NDWI** | Open water detection, drainage, ponding |
| **Dense canopy where NDVI plateaus?** | **EVI** | Resists saturation over high-biomass crops |
| **Young/sparse crop, soil showing through?** | **SAVI / MSAVI2** | Corrects for soil background dragging readings down |
| **Did a fire burn the vegetation?** | **NBR** | Burn severity and post-fire recovery |
## The core three
If you only ever used three indexes, these cover almost every crop decision:
1. **NDVI** — growth and canopy. Your starting point for "is the field okay?"
2. **NDRE** — nitrogen and nutrition. Your mid-to-late-season fertilizer signal.
3. **NDMI** — water. Your earliest drought warning, days ahead of NDVI.
Together they answer growth, nutrition, and water — the three things you can actually act on. Most other indexes are specialists you add when one of these three hits a limit (NDVI saturating → add EVI; soil contamination → switch to SAVI).
## By crop type
| Crop | Recommended set | Note |
|------|-----------------|------|
| **Cereals (wheat, corn, barley)** | NDVI → NDRE → NDMI | NDRE drives the nitrogen side-dress decision |
| **Vineyards & orchards** | NDVI + NDMI | Open canopies — NDVI does not saturate; NDMI drives irrigation |
| **Dense row crops (rice, sugarcane)** | EVI + NDMI | NDVI saturates — EVI tracks biomass; NDMI tracks water |
| **Young / establishing crops** | SAVI or MSAVI2 | Soil dominates — correct for bare-ground background |
| **Pastures / rangeland** | NDVI + NDMI | Growth and drought, broad scale |
## By season stage
- **Emergence → canopy closure:** NDVI. The crop is growing; track the bell curve.
- **Canopy closure → grain fill:** Add NDRE. The canopy is dense; switch the question to nitrogen.
- **All season:** NDMI. Water stress can hit any time and NDMI gives the earliest warning.
- **Pre-harvest:** NDVI + NDRE together. Distinguish natural senescence from a fixable deficiency.
## Common mistakes
- **Using NDVI for everything.** It is the default, but it saturates over dense canopy and is blind to nitrogen and slow to catch drought. It is the starting point, not the only map.
- **Comparing absolute values across fields.** Indexes are relative — compare a field against its own history and against neighbors in the same image, not against a universal threshold.
- **Trusting a single cloudy image.** Always check whether a sudden change is real or cloud contamination. Use cloud-free composites and trends.
> **Try it free:** Get NDVI, NDRE, NDMI and more for your field — free, no signup. Works on any land worldwide. [Check my field →](/)
## Frequently asked questions
What is the best vegetation index for general crop monitoring?
NDVI. It is the universal default for canopy density and growth-stage tracking, and it is the most widely calibrated and understood. Start with NDVI, then add NDRE (nitrogen) and NDMI (water) for the decisions NDVI cannot make on its own.
Which vegetation index is best for detecting drought?
NDMI. It measures leaf water content, which drops before chlorophyll breaks down — so it catches drought days to weeks earlier than NDVI. Pair NDMI with NDVI: when NDMI falls but NDVI stays flat, the crop is water-stressed but still green, the earliest drought signal.
Should I use NDVI or EVI?
Use NDVI for most crops and most of the season. Switch to EVI when the canopy is very dense (NDVI above roughly 0.8) or in hazy, humid regions — EVI resists saturation and corrects for atmospheric haze. For open canopies like vineyards, NDVI is the better choice.
How many vegetation indexes do I actually need?
For most growers, three: NDVI (growth), NDRE (nitrogen), and NDMI (water). That covers the decisions you can act on. Add specialists like SAVI for young crops, EVI for dense canopy, or NBR for fire only when the situation calls for them.
---
# EVI vs NDVI: What's the Difference?
URL: https://www.agroreports.org/articles/evi-vs-ndvi/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/evi-vs-ndvi.md
> EVI and NDVI both measure vegetation, but EVI resists saturation over dense canopy and corrects for haze. Learn the formula differences, when each wins, and which to use for your crop.
# EVI vs NDVI: What's the Difference?
## The short version
**NDVI** and **EVI** both measure how much vegetation is on the ground — but EVI does a better job where NDVI gives up: over very dense, lush canopy, and through atmospheric haze. NDVI is the everyday tool; EVI is the specialist you switch to once NDVI saturates.
They are not competitors. NDVI is the right default for 80% of monitoring. EVI earns its place on high-biomass crops (corn, sugarcane, rice at peak, tropical forest) and in hazy, humid regions where NDVI alone is degraded.
## The formulas
```
NDVI = (NIR − Red) / (NIR + Red)
EVI = G × (NIR − Red) / (NIR + C1×Red − C2×Blue + L)
```
Both use near-infrared and red. EVI adds a **third band — blue** — plus three correction constants. On Sentinel-2 that is **Band 8** (NIR), **Band 4** (Red), **Band 2** (Blue), with **G = 2.5, C1 = 6, C2 = 7.5, L = 1**.
| | NDVI | EVI |
|---|------|-----|
| **Bands** | NIR + Red | NIR + Red + Blue |
| **Formula type** | Pure ratio | Ratio with additive correction |
| **Atmospheric correction** | None | Blue-band haze correction |
| **Soil adjustment** | None | Built-in (L term) |
| **Range** | −1 to +1 | Roughly −1 to +1 (often 0–0.9 useful) |
| **Saturation over dense canopy** | Yes (~0.8) | No — keeps rising |
## Why EVI resists saturation
NDVI uses the red band, which chlorophyll absorbs almost completely once the canopy is thick. Past that point red reflectance is already near zero and cannot drop further, so NDVI plateaus around 0.8 even as biomass keeps climbing — a dense healthy field and a denser one return nearly the same number.
EVI's extra blue band and gain factor keep the index responding to further biomass increases. This is why EVI is preferred for **high-biomass crops** and **forest monitoring** — anywhere the canopy gets thick enough to blind NDVI.
## Why EVI corrects for haze
Short-wavelength blue light is scattered by atmospheric aerosols (haze, humidity, dust). EVI subtracts a fraction of the blue band, which sharpens the vegetation signal in conditions where NDVI alone would be washed out. This makes EVI valuable in **tropical, coastal, and humid** agriculture, where haze is common.
## When to use which
| Situation | Use | Reason |
|-----------|-----|--------|
| Most crops, most of the season | NDVI | Simpler, well calibrated, universally understood |
| Dense canopy / high biomass (corn, sugarcane, rice, forest) | EVI | NDVI saturates; EVI keeps responding |
| Hazy, humid, tropical regions | EVI | Blue-band correction handles haze |
| Open canopies (vineyards, orchards) | NDVI | NDVI does not saturate; EVI adds no value |
| Early season / sparse crop | NDVI or SAVI | EVI needs canopy to work |
A practical trigger: **if your NDVI is stuck near 0.8 and not changing while the crop clearly keeps growing, switch to EVI.** That plateau is saturation.
## Reading the values
Both indexes run roughly 0–1 for healthy vegetation, but do not compare the numbers directly field-to-field. The value is in the **trend** and the **spatial pattern**: a sudden drop in either index is a stress signal regardless of the starting value.
> **Try it free:** Get NDVI and EVI for your field — free, no signup. Works on any land worldwide. [Check my field →](/)
## Frequently asked questions
Is EVI better than NDVI?
Neither is universally better. EVI is more accurate over dense canopies and in hazy conditions. NDVI is simpler and equally valid for most open-canopy crops and general monitoring. The best reports show both and interpret them together — use NDVI until it saturates, then rely on EVI.
What is the EVI formula and what do the constants mean?
EVI = G × (NIR − Red) / (NIR + C1×Red − C2×Blue + L), where G = 2.5 (gain), C1 = 6 and C2 = 7.5 (atmospheric resistance coefficients), and L = 1 (canopy background adjustment). The blue band corrects for atmospheric haze; the L term reduces soil-background interference.
When does NDVI saturate and why switch to EVI?
NDVI saturates at roughly 0.8 over dense canopy, because chlorophyll absorbs nearly all red light and reflectance cannot drop further. Above that point NDVI returns nearly the same value even as biomass keeps increasing. EVI keeps responding, so it can distinguish "dense" from "even denser" and reveal late-season stress NDVI would hide.
Should I use EVI for a vineyard or orchard?
Usually no. Vineyards and orchards have open, managed canopies where NDVI does not saturate, so EVI's main advantage does not apply. NDVI is simpler and well suited to open-canopy crops. Reserve EVI for dense, high-biomass vegetation.
---
# How to Read an NDVI Map: Values, Colors & Scale
URL: https://www.agroreports.org/articles/how-to-read-ndvi-map/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/how-to-read-ndvi-map.md
> An NDVI map turns your field into color — green means healthy, brown means trouble. Learn what each NDVI value and color means, how the scale works, and how to spot problems before they spread.
# How to Read an NDVI Map: Values, Colors & Scale
## What an NDVI map shows you
An NDVI map is a satellite image of your field where every pixel has been converted from raw light into a single number — its **Normalized Difference Vegetation Index** — and then that number has been colored. The point of the color is simple: it lets your eye spot variation across the field in a glance. A uniform green field is healthy; patches of yellow or red inside a green field are where you need to walk.
The map does not tell you *what* is wrong (drought, disease, nutrient lack, pests all look similar). It tells you *where* and *when* — which is most of the value, because you cannot fix what you cannot locate.
## The NDVI scale, explained
NDVI always falls between **−1 and +1**. Almost all agricultural land sits between 0 and 0.9. Here is what each band means:
| NDVI range | Color (typical ramp) | What it means | Typical for |
|------------|----------------------|---------------|-------------|
| **0.7 – 0.9** | Dark green | Very dense, vigorous vegetation | Peak-season crops, mature forest, closed canopy |
| **0.5 – 0.7** | Light green | Healthy, productive canopy | Well-developed crops, full-canopy vineyards |
| **0.3 – 0.5** | Yellow-green | Moderate / developing | Mid-growth, establishing crop, orchards |
| **0.1 – 0.3** | Orange / tan | Sparse or stressed | Early growth, drought stress, poor patches |
| **−0.1 – 0.1** | Brown | Bare soil or dead vegetation | Plowed field, harvested, senesced |
| **Below −0.1** | Blue / black | Water, snow, or cloud | Ponds, rivers, snow, dense cloud shadow |
Different tools use different color ramps (some go red-to-green, some brown-to-green, some use rainbow). The *number* is always the same; the color is just a legend applied on top. **Always check the legend**, because a "green" patch on a rainbow ramp is mid-range, not healthy.
## How to actually read the map
Reading an NDVI map is about **pattern and change**, not absolute numbers:
1. **Look for variation, not averages.** A field that is uniformly 0.4 might be perfectly fine. A field that averages 0.6 but has a 0.3 patch in the corner has a problem in that corner. Variation is the signal.
2. **Compare against time.** One map tells you the current state. Two maps weeks apart tell you the *trend*. A patch dropping from 0.7 to 0.5 in two weeks is an alarm; a patch sitting at 0.5 for two months is probably just that soil type.
3. **Match the growth stage.** A 0.3 reading is concerning in July but completely normal in April for a newly emerged crop. Always read the number against what is expected for the crop and date.
## Spotting common problems
| What you see | Likely cause | Next step |
|--------------|--------------|-----------|
| **Large green field with a yellow patch** | Localised stress (compaction, drainage, pest, disease) | Walk the patch |
| **Whole field dropping faster than neighbours** | Drought, nutrient deficiency, systemic stress | Check NDMI + NDRE |
| **Stripes or bands across the field** | Application error, planter skip, irrigation failure | Check equipment logs |
| **Persistent brown in a "green" season** | Failed establishment, hail, herbicide damage | Ground-truth immediately |
| **Blue/black patches** | Waterlogging, ponding, or cloud contamination | Re-check next cloud-free image |
## The limits of NDVI maps
NDVI saturates over dense canopy — above roughly 0.8 it stops telling you much, because all healthy dense vegetation looks the same. For mid-to-late season nitrogen questions, switch to an **NDRE map**, which keeps working after NDVI goes flat. For water questions, use **NDMI**. NDVI is the starting point; it is rarely the only map you need.
Clouds are the other limit. A sudden brown patch in a green field is often just cloud shadow, not dead crop. A good report filters cloud-contaminated pixels and tells you when the data is unreliable.
> **Try it free:** Get an NDVI map for your field — free, no signup. Works on any land worldwide. [See my field on a map →](/)
## Frequently asked questions
What do the colors on an NDVI map mean?
Colors are a legend applied to the NDVI number. In the most common ramp, dark green means dense healthy vegetation (high NDVI), yellow means moderate or stressed, and brown means bare soil or dead vegetation. Blue or black usually means water. Always check the legend for the specific tool you are using, because ramps differ.
What is a good NDVI value for crops?
For most annual crops at peak growth, 0.6 to 0.8 indicates a healthy dense canopy. Vineyards and orchards run lower (0.4–0.6 is normal) because rows and management keep the canopy open. More important than any single value is the trend and the spatial pattern — compare against your own field's history.
Why is part of my field brown on the NDVI map?
Brown (low NDVI) usually means bare soil, dead or senescent vegetation, or a failed patch. But it can also mean cloud shadow or standing water. If the brown patch appears suddenly, re-check the next cloud-free image before assuming crop loss — cloud contamination is the most common false alarm.
How often does the NDVI map update?
Sentinel-2 satellites photograph every location on Earth every five days, weather permitting. Clouds can block the view for longer, which is why good reports use cloud-free composites and show trends over several images rather than trusting a single cloudy snapshot.
---
# NDVI Bands Explained: Red, NIR & Sentinel-2 Wavelengths
URL: https://www.agroreports.org/articles/ndvi-bands-wavelengths/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/ndvi-bands-wavelengths.md
> NDVI is built from two satellite bands: red and near-infrared. Learn which wavelengths NDVI uses, the Sentinel-2 bands behind it, why leaves reflect NIR and absorb red, and how the formula works.
# NDVI Bands Explained: Red, NIR & Sentinel-2 Wavelengths
## The two bands behind every NDVI map
NDVI is built from exactly **two bands of light**: red and near-infrared (NIR). That is it. The entire "crop health" number the world relies on comes from measuring how much of these two wavelengths your field reflects, then turning the gap between them into a single value.
```
NDVI = (NIR − Red) / (NIR + Red)
```
| Band | Wavelength | Sentinel-2 band | What it does |
|------|-----------|-----------------|--------------|
| **Red** | ~0.665 µm (665 nm) | **Band 4** | Absorbed strongly by chlorophyll |
| **Near-Infrared (NIR)** | ~0.842 µm (842 nm) | **Band 8** | Strongly reflected by healthy leaf structure |
Both Sentinel-2 bands 4 and 8 are captured at **10 m resolution**, which is why NDVI maps show fine field detail.
## Why red and NIR?
It comes down to how leaves interact with light:
- **Chlorophyll absorbs red light** to power photosynthesis. A healthy, leafy canopy soaks up almost all incoming red — so it reflects very little. Red reflectance stays *low*.
- **Leaf cell structure scatters near-infrared light**. A healthy leaf's spongy mesophyll acts like a mirror to NIR, reflecting it strongly back to the satellite. NIR reflectance stays *high*.
A healthy canopy therefore shows a **big gap** between low red and high NIR. When vegetation is stressed, thin, or dead, chlorophyll drops (red reflectance rises) and leaf structure degrades (NIR reflectance falls) — the gap shrinks. NDVI measures the size of that gap.
Bare soil, by contrast, reflects red and NIR roughly equally — so its NDVI sits near zero. Water absorbs both and returns negative NDVI. This is the physics that lets a simple ratio distinguish field, soil, and water.
## The NDVI formula, step by step
The ratio is *normalized*, meaning it divides by the sum of the two bands:
1. **(NIR − Red)** — the reflectance gap. Big for healthy vegetation, small for soil, negative for water.
2. **(NIR + Red)** — total brightness. Dividing by this cancels out raw illumination, so NDVI means the same thing whether the scene was captured on a bright noon or a hazy afternoon.
The result always falls between **−1 and +1**. Because the formula is a pure ratio, it self-corrects for brightness — that is the main reason NDVI is comparable across dates and locations.
## Sentinel-2 vs Landsat bands
NDVI can be computed from any satellite that has a red and a NIR band. The most common sources for agriculture:
| Satellite | Red band | NIR band | Resolution | Revisit |
|-----------|----------|----------|-----------|---------|
| **Sentinel-2** | Band 4 (665 nm) | Band 8 (842 nm) | 10 m | 5 days |
| **Landsat 8/9** | Band 4 (654 nm) | Band 5 (865 nm) | 30 m | 16 days |
Sentinel-2 is the workhorse for crop monitoring — finer resolution and more frequent revisit. The slight wavelength differences between satellites mean NDVI values are close but not perfectly identical across sensors; compare like-with-like.
## What the other bands do
The red-edge, SWIR, and blue bands power the *other* indexes that complement NDVI:
- **Red-edge (Band 5, ~705 nm)** → **NDRE**. Penetrates the leaf to read chlorophyll and nitrogen.
- **SWIR1 (Band 11, ~1.6 µm)** → **NDMI**. Absorbed by leaf water, so it tracks moisture.
- **Blue (Band 2)** → **EVI**. Corrects NDVI for atmospheric haze.
- **Shortwave bands** → **NBR**. Burn severity mapping.
NDVI's two bands answer "is it green and dense?" The other bands answer the questions NDVI cannot — nitrogen, water, burn. Together they cover the field.
> **Try it free:** Get NDVI (and the bands behind it) for your field — free, no signup. Works on any land worldwide. [Check my field →](/)
## Frequently asked questions
What wavelengths does NDVI use?
NDVI uses two wavelengths: red light at roughly 665 nm and near-infrared at roughly 842 nm. On Sentinel-2 these are Band 4 and Band 8. The index measures the gap between how much red the canopy absorbs and how much NIR it reflects.
Why does NDVI use near-infrared and not just visible light?
Because near-infrared is where healthy leaves reveal themselves. Chlorophyll absorbs red (visible), but a leaf's internal cell structure strongly scatters near-infrared — and that NIR reflectance is invisible to the eye. A field can look uniformly green to a person but show clear NIR variation to a satellite, which is why NDVI detects stress the eye misses.
What is the NDVI formula and how is it normalized?
NDVI = (NIR − Red) / (NIR + Red). The numerator is the reflectance gap; the denominator is total brightness. Dividing by the sum normalizes the result, cancelling out scene brightness so the value is comparable across different images, dates, and lighting conditions. The output is always between −1 and +1.
Which Sentinel-2 bands does NDVI use and at what resolution?
NDVI uses Sentinel-2 Band 4 (red, 665 nm) and Band 8 (near-infrared, 842 nm), both captured at 10 m resolution. This is why NDVI maps show fine field-level detail. Some other indexes like NDMI use Band 11 (SWIR), which is only 20 m resolution.
---
# NDVI vs NDRE: Which Vegetation Index Should You Use?
URL: https://www.agroreports.org/articles/ndvi-vs-ndre/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/ndvi-vs-ndre.md
> NDVI and NDRE measure different things. NDVI tracks canopy density and growth; NDRE tracks leaf chlorophyll and nitrogen. Here is exactly when to use each — and when to use both.
# NDVI vs NDRE: Which Vegetation Index Should You Use?
## The short answer
**NDVI** tells you *how much canopy there is*. **NDRE** tells you *how well-fed that canopy is*. They answer different questions, which is why precision growers use them together rather than picking one.
The single most common mistake is treating them as rivals. They are not — NDVI is your growth-stage index, NDRE is your nutrition index. Use NDVI from planting through canopy closure, then switch to (or add) NDRE for the second half of the season when the question shifts from "is it growing?" to "is it well-fed?"
## How each one works
Both are normalized ratios — that is, a number between −1 and 1 built from two satellite bands. The difference is *which* bands:
```
NDVI = (NIR − Red) / (NIR + Red)
NDRE = (NIR − RedEdge) / (NIR + RedEdge)
```
On Sentinel-2, NDVI uses **Band 8 + Band 4**, while NDRE uses **Band 8 + Band 5** (the red-edge band, ~0.705 µm). That one band change changes everything about what the index "sees."
| | NDVI | NDRE |
|---|------|------|
| **What it measures** | Canopy density / green biomass | Leaf chlorophyll / nitrogen status |
| **Best band** | Red (absorbed by chlorophyll at the surface) | Red-edge (shifts with chlorophyll inside the leaf) |
| **Saturation** | Saturates over dense canopy (~0.8) | Stays sensitive over dense canopy |
| **Strength** | Growth stage, biomass, season tracking | Nitrogen status, mid-late season nutrition |
| **Weakness** | Can't see nitrogen once canopy closes | Lower absolute values, needs canopy to exist |
| **Best season window** | Early to mid season | Mid to late season |
## When NDVI wins
NDVI is the right default from sowing until the canopy closes. It tracks the bell curve of a seasonal crop — climbing through vegetative growth, peaking at canopy closure, falling at senescence. For **growth-stage monitoring**, **year-over-year comparison**, and **general canopy health**, NDVI is simpler, better calibrated, and universally understood.
NDVI also wins early in the season, when there is little canopy to measure. NDRE needs leaf layers to work; over bare soil or a young seedling it has almost nothing to say.
## When NDRE wins
Once the canopy is closed, NDVI *saturates* — it returns roughly the same 0.8 value whether the crop is well-fed or quietly starving. This is exactly the window where NDRE earns its keep. Because red-edge light penetrates deeper into the leaf stack, NDRE keeps reporting on chlorophyll and nitrogen status after NDVI has gone flat.
NDRE is the index for **nitrogen management**, **variable-rate fertilizer prescription**, and **detecting hidden stress over a dense crop**. A falling NDRE in a still-green canopy is the classic early signal of nitrogen depletion — the window where a side-dress still pays off.
## Use them together: the season playbook
| Growth stage | Primary index | What you are asking |
|--------------|---------------|---------------------|
| **Emergence → canopy closure** | NDVI | Is the crop establishing and growing? |
| **Canopy closure → grain fill** | NDRE | Is the canopy well-fed (nitrogen)? |
| **All season** | NDVI + NDMI | Is it growing, and is it well-watered? |
| **Late season** | NDVI + NDRE | Is the decline natural senescence or deficiency? |
A practical rule: **if NDVI is high and flat but NDRE is dropping, the crop is nitrogen-stressed.** If both are dropping together over a mature crop, it is likely natural ripening, not a problem. Pairing NDRE with NDMI (water status) completes the nutrition picture, because nitrogen uptake depends on available moisture.
> **Try it free:** See NDVI and NDRE side by side for your field — free, no signup. Works on any land worldwide. [Check my field →](/)
## Frequently asked questions
Is NDRE better than NDVI?
Neither is universally better. NDVI is better for early-season growth tracking and general biomass. NDRE is better for nitrogen monitoring once the canopy is dense. For most of the season the right answer is to use both — NDVI to confirm the canopy is there, NDRE to confirm it is well-fed.
Why does NDVI saturate over dense canopy?
NDVI uses the red band, which chlorophyll absorbs almost completely once the canopy is thick. Past that point red reflectance is already near zero, so it cannot drop further — NDVI plateaus around 0.8 even as biomass keeps climbing. Red-edge light (used by NDRE) is only partially absorbed, so it keeps responding to chlorophyll changes inside the leaf.
Can NDRE replace NDVI for nitrogen management?
It complements rather than replaces it. NDRE tells you the nitrogen *status*, but you still need NDVI to confirm the canopy is dense enough for NDRE to be meaningful. A low NDRE over a thin canopy can just mean there are few leaves to measure, not that the crop is deficient. The two indexes confirm each other.
What is a good NDRE value compared to NDVI?
NDRE runs lower in absolute terms than NDVI by design — a dense canopy might read 0.8 in NDVI but only 0.3 in NDRE. Do not compare the two numbers directly. Instead, track each index against its own seasonal trend, and map spatial variation within your field: patches lower than the field average in NDRE are your nitrogen-deficient zones.
---
# Using NDRE for Variable-Rate Nitrogen Management
URL: https://www.agroreports.org/articles/ndre-nitrogen-management/
Updated: 2026-07-08
Markdown mirror: https://www.agroreports.org/articles/ndre-nitrogen-management.md
> NDRE is the satellite index for nitrogen. Learn how to read NDRE maps, spot nitrogen-deficient zones before they yellow, and plan variable-rate fertilizer applications that save money and boost yield.
# Using NDRE for Variable-Rate Nitrogen Management
## Why NDRE is the nitrogen index
Of all the satellite vegetation indexes, **NDRE** (Normalized Difference Red Edge) is the one built for nitrogen. It reads leaf chlorophyll — the molecule nitrogen builds — by looking through the red-edge band, which penetrates deep into the canopy. Where NDVI tells you how much leaf there is, NDRE tells you how much nitrogen is actually inside those leaves.
This matters because nitrogen is the input farmers spend the most on, and the one they waste the most of. A blanket application treats the whole field the same, but fields are never uniform. NDRE shows you exactly which zones are fed and which are hungry — the basis for a **variable-rate** prescription that puts nitrogen only where the crop can still use it.
## How NDRE reveals nitrogen status
When nitrogen is abundant, chlorophyll is abundant, and the red-edge band sits in a certain position. As nitrogen depletes, chlorophyll falls, and the red-edge shifts — NDRE drops. Crucially, this happens *inside the leaf* while the canopy still looks green. NDVI cannot see it because it saturates over dense canopy; NDRE can.
```
NDRE = (NIR − RedEdge) / (NIR + RedEdge)
```
On Sentinel-2: **Band 8** (NIR) and **Band 5** (red-edge, ~0.705 µm).
## Reading an NDRE map for nitrogen
| NDRE range | Chlorophyll status | Nitrogen read | Action |
|------------|-------------------|---------------|--------|
| **0.4 – 0.6** | High | Sufficient | No application needed |
| **0.25 – 0.4** | Adequate | Healthy | Monitor; maintain program |
| **0.15 – 0.25** | Declining | Early depletion | Candidate for side-dress / top-up |
| **0.05 – 0.15** | Low | Deficient | Targeted application recommended |
| **Below 0.05** | Very low | Severe deficiency or senescence | Ground-truth before acting |
**Do not chase absolute thresholds.** The power of NDRE is *spatial*: map it across the field and look for zones reading below the field average. Those low patches are where a targeted top-up pays off, and where a blanket application was being wasted on the already-sufficient zones.
## The variable-rate workflow
1. **Start after canopy closure.** NDRE needs leaf layers to measure. Before the canopy closes, use NDVI. The nitrogen window opens mid-season — typically stem elongation through grain fill for cereals.
2. **Pull an NDRE map.** A cloud-free Sentinel-2 composite gives you the spatial pattern.
3. **Identify the zones.** Group pixels into management zones: sufficient (no action), declining (top-up candidate), deficient (priority). The variation *within* your field is your prescription map.
4. **Prescribe by zone, not by field.** Apply nitrogen to the deficient and declining zones; skip or reduce the sufficient ones. This is variable-rate application.
5. **Re-check in 10–14 days.** NDRE should rise in the treated zones. If it does not, the problem was not nitrogen — check water (NDMI) or root health.
## Timing: when the window opens and closes
The NDRE nitrogen window is **mid-to-late season**, once the canopy is established and before the crop starts ripening. Apply too early and there is no canopy to read; apply too late and the crop has already set its yield. For winter cereals this is roughly stem elongation through boot stage; for corn, V6 through tasseling.
A sudden mid-season NDRE drop in a still-green canopy is the signal to act — it means nitrogen is being depleted faster than it is being taken up. If NDRE stays flat while NDVI is high, the crop is well-fed and an extra application is likely waste.
## Pair NDRE with these indexes
NDRE tells you nitrogen *status*, not the whole story. Nitrogen uptake depends on water, so pair it with **NDMI** — a crop can be nitrogen-sufficient but water-stressed, and adding fertilizer to a dry field does nothing. Use **NDVI** to confirm the canopy is dense enough for NDRE to be meaningful. Together, NDVI (growth) + NDRE (nutrition) + NDMI (water) cover the three questions that drive a fertilizer decision.
> **Try it free:** Get an NDRE map for your field and see your nitrogen zones — free, no signup. Works on any land worldwide. [Check my field's nitrogen →](/)
## Frequently asked questions
Can NDRE really detect nitrogen deficiency?
Yes — it is the index's core purpose. A falling NDRE in a green, dense canopy indicates declining chlorophyll, most often caused by nitrogen depletion. Because NDRE sees inside the leaf via the red-edge band, it catches this before the canopy yellows, which is the window where a side-dress or foliar feed still pays off.
How accurate is NDRE for variable-rate fertilizer?
NDRE is highly effective for identifying *where* nitrogen is needed (spatial prescription). The absolute rate to apply still depends on your crop, soil, yield goal, and agronomic advice — NDRE gives you the map, your agronomist sets the rate. The biggest win is usually from skipping already-sufficient zones rather than over-feeding deficient ones.
When is it too late to apply nitrogen based on NDRE?
Once the crop transitions to ripening and chlorophyll degrades naturally, NDRE falls as part of senescence — not deficiency. Applying nitrogen then is wasted. For most cereals the useful window closes around grain fill. If NDVI and NDRE are both dropping over a mature crop, it is likely natural senescence, not a fixable deficiency.
Why use NDRE instead of NDVI for nitrogen?
NDVI saturates over dense canopy — past roughly 0.8 it cannot distinguish well-fed from nitrogen-stressed vegetation, because both look like a thick green wall. NDRE uses the red-edge band, which keeps responding to chlorophyll inside the leaf after NDVI has gone flat. For nitrogen questions over a closed canopy, NDRE is the only one that still talks to you.
---
# What is EVI? (Enhanced Vegetation Index) vs NDVI
URL: https://www.agroreports.org/articles/what-is-evi/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-evi.md
> EVI is a satellite vegetation index that stays sensitive over dense canopies where NDVI saturates. Uses blue-band atmospheric correction for accuracy in high-biomass and tropical crops.
# What is EVI? (Enhanced Vegetation Index)
## What is EVI?
EVI, the **Enhanced Vegetation Index**, is a satellite vegetation index that does the same job as NDVI — measuring how healthy and dense your crop canopy is — but stays accurate where NDVI gives up: over very lush, high-biomass vegetation. If NDVI is the everyday tool, EVI is the one you reach for when the canopy is so thick that NDVI stops being able to tell you anything more. Tropical forest, a peak-season corn field, a densely planted sugarcane block — these are EVI territory.
## How it's calculated
EVI uses the same near-infrared and red bands as NDVI, but adds a third: **blue**. The blue band lets EVI correct for atmospheric haze (which scatters blue light), and a soil-adjustment factor reduces the influence of bare ground. The result is an index that keeps rising as vegetation gets denser, long after NDVI has flattened out.
```
EVI = G × (NIR − Red) / (NIR + C1 × Red − C2 × Blue + L)
```
On Sentinel-2 these are **Band 8** (NIR), **Band 4** (Red), and **Band 2** (Blue), with constants **G = 2.5**, **C1 = 6**, **C2 = 7.5**, **L = 1**. Because Sentinel-2 surface-reflectance values are stored on a 0–10,000 scale (not 0–1) and EVI's formula adds and subtracts bands rather than taking a pure ratio, the scale factor does not cancel. The soil-adjustment term `L` must be scaled to match — implemented as `10000` in DN space. Getting this wrong produces plausible-looking nonsense numbers.
## Typical value ranges
EVI covers roughly the same 0–1 range as NDVI but spreads its useful values higher, because it does not compress at the top.
| EVI range | Meaning | Typical for |
|-----------|---------|-------------|
| **0.6 – 0.9** | Very dense, vigorous vegetation | Tropical forest, peak-season row crops at canopy closure |
| **0.4 – 0.6** | Healthy, moderately dense | Well-developed crop canopy, mature plantations |
| **0.2 – 0.4** | Moderate / developing | Early-to-mid growth, orchards with open rows |
| **0.0 – 0.2** | Sparse or bare | Bare soil, very early growth, senesced vegetation |
| **Below 0** | Water, snow, cloud | Open water, snow, dense cloud |
As with NDVI, the absolute number matters less than the **trend** — a sudden drop is a stress signal regardless of where you started.
## When to use it
Switch to EVI when the canopy is dense (roughly NDVI above 0.7–0.8). At that point NDVI *saturates* — it keeps returning nearly the same value even as biomass keeps climbing. EVI keeps responding, so it can distinguish "dense" from "even denser." Prefer EVI in hazy or humid regions too — the blue-band atmospheric correction helps where NDVI alone is degraded by haze, common in tropical and coastal agriculture.
For a vineyard or orchard (open canopies), NDVI is usually the better choice. For corn, sugarcane, rice at peak, or forest, EVI is the more honest number. Where NDVI flattens into a plateau over a dense crop, EVI continues to rise and fall with true canopy development, making it better at revealing late-season stress that NDVI would hide under its saturation ceiling.
## Comparison with other indices
EVI is essentially NDVI with atmospheric correction and saturation resistance — use it when NDVI stops improving over dense canopies. For sparse crops where soil background is the problem (not saturation), SAVI or MSAVI2 are more appropriate than EVI. NDMI pairs well with EVI for high-biomass irrigation: EVI tracks canopy growth while NDMI tracks the water inside it. NDRE offers different information entirely (nitrogen status), making it complementary rather than a competitor.
> **Try it free:** Get EVI values for your field — free, no signup. Works on any land worldwide. [Check my field's vegetation health →](/)
## Frequently asked questions
Is EVI better than NDVI?
Neither is universally "better." EVI is more accurate over **dense canopies** (where NDVI saturates and stops changing) and in **hazy conditions**. For most open-canopy crops and general monitoring, NDVI is simpler and equally valid. The best reports show both and interpret them together.
Why does EVI use the blue band?
The blue band lets EVI **correct for atmospheric scattering** (haze, aerosols), which preferentially scatters shorter blue wavelengths. Subtracting a fraction of blue sharpens the signal in conditions where NDVI alone would be degraded. It also introduces a dependency on good atmospheric correction, so EVI is more picky about image quality.
What is a good EVI value for crops?
For a healthy annual crop at peak growth, **0.5 to 0.8** is typical. Forests can exceed 0.8. As always, compare against your own field's seasonal pattern rather than an absolute threshold — a drop of 0.2 is a stress signal regardless of the starting value.
Can EVI detect drought?
Yes — a falling EVI over a dense canopy can reveal drought stress that NDVI would hide beneath its saturation ceiling. For this reason EVI is valuable for irrigated high-biomass crops and forest health monitoring. Pair it with NDMI (moisture) for a clearer picture.
---
# What is MSAVI2? (Modified Soil-Adjusted Vegetation Index)
URL: https://www.agroreports.org/articles/what-is-msavi2/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-msavi2.md
> MSAVI2 is the self-adjusting soil-corrected vegetation index — computes the correction from the data, no fixed L factor. Ideal for heterogeneous fields and early growth.
# What is MSAVI2? (Modified Soil-Adjusted Vegetation Index)
## What is MSAVI2?
MSAVI2 is the self-adjusting successor to SAVI. Where SAVI requires you to pick a fixed soil-brightness factor `L` (usually 0.5) and accept the compromise, MSAVI2 computes the ideal `L` from the data itself, pixel by pixel. The result is a soil-corrected vegetation index that adapts to whatever density it encounters — making it the best choice for heterogeneous fields, very sparse vegetation, and early growth monitoring. If SAVI is the early-season specialist with a fixed tool, MSAVI2 is the same specialist with a self-sharpening one.
## How it's calculated
MSAVI2 uses the same NIR and Red bands as NDVI and SAVI, but replaces the fixed `L` with an in-formula iterative solution that derives `L` from the vegetation density in each pixel:
```
MSAVI2 = (2 · NIR + 1 − √((2·NIR + 1)² − 8·(NIR − Red))) / 2
```
On Sentinel-2 these are **Band 8** (NIR) and **Band 4** (Red). The formula starts from the SAVI equation and solves for the `L` that minimizes soil background noise given the actual reflectance values. The square-root term is that self-adjustment in action — it produces a different effective `L` for every pixel.
Unlike SAVI, MSAVI2 has no free parameter to set and no DN-scaling pitfall (the ratio structure is self-balancing). This makes it both more accurate and simpler to implement correctly.
## Typical value ranges
MSAVI2 occupies the same −1 to +1 range as NDVI and SAVI. For sparse canopies, it sits above NDVI (removing the soil penalty) and close to SAVI with `L = 0.5`.
| MSAVI2 range | Meaning | Typical for |
|--------------|---------|-------------|
| **0.6 – 0.9** | Dense, healthy vegetation | Peak-season closed canopy |
| **0.4 – 0.6** | Moderate vegetation | Developing crops, healthy pasture |
| **0.2 – 0.4** | Sparse but viable | Young crops, arid rangeland — *MSAVI2's sweet spot* |
| **0.0 – 0.2** | Very sparse / bare | Newly emerged crop, dry bare ground |
| **Below 0** | Water, snow, cloud | Open water, snow cover |
## When to use it
Use MSAVI2 for sparse vegetation (young crops, arid rangeland, shrubland), heterogeneous fields with mixed crop stages, and areas with variable soil brightness. The more variability you have in vegetation density or soil brightness, the more MSAVI2 pulls ahead of SAVI. Over a season, MSAVI2 follows the same growth curve as NDVI but reads higher in the early stages — that gap is the soil-brightness correction. For a heterogeneous field with patchy establishment, MSAVI2's spatial map shows less soil-driven noise than NDVI, making stressed zones easier to spot.
Once the canopy closes and soil is no longer visible, MSAVI2 converges with NDVI — switch back to NDVI there. The square-root term can fail on bad data (negative under the root from sensor noise), producing NaN — robust implementations clamp or filter these cases.
## Comparison with other indices
MSAVI2 is NDVI with automatic per-pixel soil correction. SAVI is the fixed-L predecessor — MSAVI2 is more accurate whenever vegetation density varies within a field. NDVI remains the universal default for closed-canopy monitoring. For high-biomass saturation issues, EVI is the right choice. MSAVI2's self-balancing formula also avoids SAVI and EVI's DN-scaling pitfalls entirely, making it both more robust and simpler than either.
> **Try it free:** Get MSAVI2 values for your field — free, no signup. Works on any land worldwide. [Check my field's establishment →](/)
## Frequently asked questions
What is the difference between MSAVI2 and NDVI?
MSAVI2 is NDVI with an automatic soil-brightness correction. NDVI assumes only vegetation affects the reflectance; MSAVI2 self-computes a per-pixel soil factor so that bare soil showing through a sparse canopy does not drag the index down. The two converge once the canopy closes; MSAVI2 matters most in early growth and heterogeneous fields.
What is the difference between MSAVI2 and SAVI?
SAVI uses a **fixed** soil-adjustment factor `L = 0.5`. MSAVI2 computes `L` **automatically from the data** using an iterative formula, so the correction adapts to each pixel's actual vegetation density. For fields with variable soil brightness or mixed crop density, MSAVI2 is more accurate; for uniform conditions, SAVI is simpler and equally valid.
When should I use MSAVI2?
Use MSAVI2 for **sparse vegetation** (young crops, arid rangeland, shrubland), **heterogeneous fields** with mixed crop stages, and **areas with variable soil brightness**. Once the canopy closes and soil is no longer visible, switch to NDVI — the correction adds no value and the indices converge.
Does MSAVI2 require a DN-scaling fix like SAVI?
No — this is one of MSAVI2's advantages. Because its formula is a ratio-like structure that self-balances, it does not have the DN-scaling pitfall that SAVI and EVI have (where the `L` factor must be multiplied by 10000 in Sentinel-2 DN space). MSAVI2 is simpler to implement correctly.
---
# What is NBR? (Normalized Burn Ratio) & dNBR Burn Severity
URL: https://www.agroreports.org/articles/what-is-nbr/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-nbr.md
> NBR measures fuel condition and fire risk; dNBR measures burn severity after a fire. Learn both formulas, the USGS severity scale, and how satellites assess wildfire damage.
# What is NBR? (Normalized Burn Ratio)
## What is NBR?
NBR, the **Normalized Burn Ratio**, is a satellite index tuned to detect burned vegetation and assess fuel condition before a fire. It uses the same near-infrared band as NDVI but pairs it with a deeper shortwave-infrared band (SWIR2) that is especially sensitive to ash, charred material, and dry woody fuel.
It serves two distinct purposes: **NBR** (single snapshot) measures current vegetation dryness and fuel condition — low NBR means dry, fire-prone vegetation. **dNBR** (differenced NBR) compares pre-fire and post-fire NBR to measure how badly an area burned — this is the standard used by fire agencies worldwide.
## How it's calculated
Healthy vegetation reflects strongly in near-infrared and absorbs shortwave-infrared. Burned or very dry vegetation does the opposite: NIR reflectance collapses and SWIR2 reflectance rises. NBR turns this contrast into a number:
```
NBR = (NIR − SWIR2) / (NIR + SWIR2)
```
On Sentinel-2 these are **Band 8** (NIR) and **Band 12** (SWIR2, ~2.2 µm). The ratio structure normalizes brightness across scenes.
For the differenced version used after a fire:
```
dNBR = NBR_pre-fire − NBR_post-fire
```
A larger positive dNBR means a more severe burn — the canopy lost more of its healthy reflectance between the two dates.
## Typical value ranges
Pre-fire NBR for fuel condition and fire risk:
| NBR range | Meaning | Fire risk |
|----------|---------|-----------|
| **> 0.4** | Lush, wet vegetation | Very low |
| **0.2 – 0.4** | Healthy vegetation | Low |
| **0.0 – 0.2** | Drying vegetation | Moderate |
| **−0.1 – 0.0** | Dry, stressed | High |
| **Below −0.1** | Very dry / burned / bare | Extreme |
Post-fire dNBR burn severity (USGS standard):
| dNBR range | Severity class | What it means on the ground |
|------------|---------------|------------------------------|
| **< 0.1** | Unburned / enhanced | Vegetation regrowth or no burn |
| **0.1 – 0.27** | Low severity | Surface fire, canopy largely intact |
| **0.27 – 0.44** | Moderate–low severity | Partial canopy mortality |
| **0.44 – 0.66** | Moderate–high severity | Heavy canopy mortality |
| **> 0.66** | High severity | Stand-replacing fire, total canopy loss |
## When to use it
Use NBR for pre-fire risk assessment: low values before fire season indicate dry, flammable fuel. Combine with NDMI (moisture) for a complete fuel-condition picture. Use dNBR after a fire to classify burn severity for insurance claims, reforestation planning, and erosion risk assessment.
For dNBR, pre-fire image selection matters critically. Trustworthy pipelines select cloud-free imagery 5–90 days before the fire and 5–60 days after. Before 5 days, smoke contaminates the image; after 60 days, fast vegetation recovery can mask the true severity. Other land-cover changes (logging, harvest, senescence) can also lower NBR — a real burn assessment cross-references the timing of the drop against the fire event.
## Comparison with other indices
NBR is the fire-specialist index. NDMI measures leaf water content (primary fire risk indicator), while NBR measures fuel condition and burn severity — the two complement each other in fire-danger scoring. NDVI tracks green canopy density and recovers after fire, but NBR is far more sensitive to char and ash because of the SWIR2 band. For post-fire damage assessment, dNBR is the international standard that NDVI and NDMI cannot replace.
> **Try it free:** Get NBR values for your field — free, no signup. Works on any land worldwide. [Assess my land's fire risk or burn impact →](/)
## Frequently asked questions
What is the difference between NBR and dNBR?
**NBR** is a single snapshot of current vegetation condition — low values indicate dry, fire-prone fuel. **dNBR** is the *difference* between a pre-fire and post-fire NBR image, used to measure **how severely an area burned**. NBR is for risk assessment before a fire; dNBR is for damage assessment after.
What is a good dNBR value?
There is no "good" dNBR — lower is less severe. **Below 0.1** is considered unburned or enhanced (regrowth). **0.1–0.27** is low-severity surface fire. **Above 0.66** is high-severity, stand-replacing fire. The USGS classification is the international standard.
Can NBR predict wildfires?
Not on its own. NBR measures fuel condition — how dry and flammable the vegetation is — but ignition depends on weather, human activity, and lightning. NBR (fuel) combined with NDMI (moisture) and weather data gives a meaningful risk score, but no satellite index predicts ignition.
How soon after a fire can I assess burn severity?
Typically **5–60 days after** the fire date, once the smoke has cleared and before significant regrowth or senescence distorts the reading. Before 5 days, smoke and active fire contaminate the image; after 60 days, fast vegetation recovery can mask the true severity.
---
# What is NDMI? (Normalized Difference Moisture Index)
URL: https://www.agroreports.org/articles/what-is-ndmi/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-ndmi.md
> NDMI measures vegetation water content from satellites — the most reliable early-warning index for drought stress and wildfire risk. Catches water stress days before NDVI.
# What is NDMI? (Normalized Difference Moisture Index)
## What is NDMI?
NDMI, the **Normalized Difference Moisture Index**, measures how much water is inside your crop's leaves. Where NDVI tells you how green and dense the canopy is, NDMI tells you how well-watered it is — and it does so before drought stress becomes visible to the eye or to NDVI. It is the single most important index for drought monitoring, irrigation scheduling, and wildfire risk assessment.
The reason NDMI beats NDVI for drought: leaf water content drops *before* chlorophyll breaks down. A crop can be running low on water and still look green. NDMI catches this; NDVI only catches it once the stress is severe enough to start yellowing.
## How it's calculated
When vegetation is well-watered, its internal leaf structure strongly reflects near-infrared and its water content absorbs shortwave-infrared (SWIR). As leaves dry out, SWIR reflectance rises sharply — the gap between NIR and SWIR shrinks. NDMI turns that gap into a number:
```
NDMI = (NIR − SWIR1) / (NIR + SWIR1)
```
On Sentinel-2 these are **Band 8** (NIR) and **Band 11** (SWIR1, ~1.6 µm). The ratio structure cancels out raw brightness, so values are comparable across scenes and dates.
## Typical value ranges
| NDMI range | Meaning | Fire risk |
|------------|---------|-----------|
| **> 0.3** | High moisture, lush vegetation | Very low |
| **0.1 – 0.3** | Moderate moisture, healthy crop | Low |
| **−0.1 – 0.1** | Transitional, water stress beginning | Moderate |
| **−0.3 – −0.1** | Significant stress, drying fast | Elevated |
| **Below −0.3** | Extreme dryness, dead or dormant vegetation | Critical |
These thresholds are the basis for fire-danger classification. Most fire-impact monitoring systems weight NDMI heavily — at AgroReports, the pre-fire risk score is 60% NDMI, 25% NBR, and 15% NDVI.
## When to use it
Use NDMI for drought early-warning, irrigation scheduling, and wildfire risk assessment. Over a season, NDMI follows the crop's water budget: high after rainfall or irrigation, declining steadily during dry spells, crashing during drought. On an irrigated field, the curve shows your watering cycle as a sawtooth. On a rain-fed field, it mirrors the rainfall pattern. A divergence where NDVI stays flat but NDMI falls is the earliest sign of hidden drought stress.
NDMI gives you days to weeks of lead time over NDVI for drought detection: water stress begins, leaf moisture drops, NDMI falls — but NDVI still looks fine. By the time NDVI declines, the damage is already underway.
## Comparison with other indices
NDMI answers *"is the canopy well-watered?"* while NDVI answers *"is the canopy green and dense?"* The two diverge during drought onset, with NDMI providing the earlier warning. NDWI (McFeeters) is often confused with NDMI but measures surface water, not leaf moisture — use NDWI for flooding and drainage, NDMI for crop moisture stress. NBR pairs with NDMI for fire risk assessment: NDMI covers fuel moisture, NBR covers fuel condition, and together they form the core of any satellite-based fire danger score.
> **Try it free:** Get NDMI values for your field — free, no signup. Works on any land worldwide. [Check my field's moisture →](/)
## Frequently asked questions
What is a good NDMI value?
For a healthy, well-watered crop, **NDMI above 0.2** is typical at peak growth. Values between 0.1 and 0.2 indicate adequate but declining moisture. Below 0.0, the crop is under measurable water stress. As always, compare against your field's own seasonal history rather than a single absolute threshold.
Is NDMI better than NDVI for drought?
Yes — for the **onset** of drought. Leaf water content drops before chlorophyll degrades, so NDMI falls earlier than NDVI. NDVI catches stress once the canopy starts losing greenness; NDMI catches it days to weeks sooner. For irrigation decisions, NDMI is the better early signal.
Can NDMI predict wildfire risk?
Yes. NDMI is the primary input to most satellite-based fire-danger scores because it directly reflects fuel moisture. Low NDMI (below −0.1) indicates dry, flammable vegetation; below −0.3 indicates critically dry fuel. Fire agencies use NDMI trends alongside weather data to anticipate danger windows.
Why is NDMI at 20 m resolution when NDVI is 10 m?
Because Sentinel-2's SWIR1 band (Band 11) is collected at 20 m resolution, while the Red and NIR bands used by NDVI are at 10 m. NDMI must use SWIR, so it inherits the coarser resolution. This is a sensor limitation, not a software choice — it is fine for field-scale monitoring.
---
# What is NDRE? (Normalized Difference Red Edge Index)
URL: https://www.agroreports.org/articles/what-is-ndre/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-ndre.md
> NDRE measures chlorophyll and nitrogen status in crops using the red-edge band — more sensitive than NDVI for mid-to-late season nutrition. Essential for variable-rate nitrogen applications.
# What is NDRE? (Normalized Difference Red Edge Index)
## What is NDRE?
NDRE, the **Normalized Difference Red Edge Index**, measures chlorophyll content and nitrogen status in crops. It is the precision-agriculture cousin of NDVI: more sensitive to what is happening inside the leaf once the canopy is established, and the index of choice for guiding variable-rate nitrogen applications. If NDVI tells you how much canopy there is, NDRE tells you how well-fed that canopy is.
## How it's calculated
NDVI uses the sharp jump between red absorption and near-infrared reflectance. NDRE uses a more subtle transition: the **red-edge** — the narrow band where reflectance rises steeply from red into near-infrared. The position and slope of this edge shift with chlorophyll concentration, making it an early indicator of nitrogen status.
```
NDRE = (NIR − RedEdge) / (NIR + RedEdge)
```
On Sentinel-2 these are **Band 8** (NIR) and **Band 5** (Red Edge, ~0.705 µm). Because red-edge light penetrates deeper into the canopy than red light, NDRE samples more of the leaf layers — not just the sunlit top. This is why it stays informative after NDVI has saturated.
## Typical value ranges
NDRE runs lower in absolute magnitude than NDVI — a dense canopy might read 0.3 in NDRE where it reads 0.8 in NDVI — but its *changes* carry more information about chlorophyll and nitrogen.
| NDRE range | Meaning | Typical for |
|------------|---------|-------------|
| **0.4 – 0.6** | High chlorophyll, sufficient nitrogen | Well-fertilized crop at peak growth |
| **0.2 – 0.4** | Moderate chlorophyll | Adequate nutrition, normal development |
| **0.1 – 0.2** | Declining chlorophyll | Early nitrogen stress, mid-to-late season depletion |
| **0.0 – 0.1** | Low chlorophyll / stressed | Nitrogen deficiency, senescence |
| **Below 0** | Bare soil or water | No active canopy |
A better way to use NDRE: map it across your field and look for spatial variation. Patches that read lower than the field average are nitrogen-deficient zones — candidates for a targeted top-up rather than a blanket application.
## When to use it
Use NDRE for mid-to-late season nitrogen monitoring, variable-rate fertilizer planning, and detecting chlorophyll stress over dense canopies where NDVI has saturated. Over a season, NDRE stays relatively stable in a well-fertilized crop, then declines as nitrogen is taken up. A sudden mid-season drop signals nitrogen depletion — the window where a side-dress or foliar feed still pays off.
NDRE dominates when the canopy is closed and the question becomes *"is it well-fed?"* For early-season growth tracking, NDVI or SAVI remain the better choice. As the crop ripens and chlorophyll degrades naturally, NDRE falls — do not confuse natural senescence with deficiency.
## Comparison with other indices
NDRE and NDVI answer different questions: NDVI tracks canopy density, NDRE tracks leaf chlorophyll. They are complementary — use NDVI for growth stage and NDRE for nutrition. NDRE stays sensitive to nitrogen after NDVI saturates over dense canopies, making it the mid-to-late season specialist. EVI also resists saturation but measures biomass, not chlorophyll specifically. For a complete nutrition picture, pair NDRE with NDMI (water status) since nitrogen uptake depends on available moisture.
> **Try it free:** Get NDRE values for your field — free, no signup. Works on any land worldwide. [Check my field's nitrogen status →](/)
## Frequently asked questions
What is a good NDRE value?
For a healthy, well-fertilized crop at peak growth, **NDRE between 0.25 and 0.45** is typical — though absolute values vary by crop and growth stage. Do not compare NDRE directly to NDVI: it runs lower by design. Track changes over the season and map spatial variation within your field.
Is NDRE better than NDVI?
For nitrogen monitoring and mid-to-late season management, **yes**. NDRE stays sensitive to chlorophyll after NDVI has saturated. For early-season growth monitoring and general biomass mapping, NDVI remains the better default. The two are complementary — use NDVI to track growth and NDRE to track nutrition.
Can NDRE detect nitrogen deficiency?
Yes — this is its core use case. A falling NDRE in a still-green canopy indicates declining chlorophyll, most often caused by nitrogen depletion. Spatial NDRE maps reveal deficient zones before they become visible, enabling targeted variable-rate nitrogen rather than blanket application.
Why does NDRE use the red-edge band?
The red-edge is the narrow spectral region where reflectance transitions from red absorption (by chlorophyll) to near-infrared reflectance (by leaf structure). Its position and slope shift with chlorophyll concentration, making it a more direct probe of leaf chemistry than the broad red band NDVI uses.
---
# What is NDVI? (Normalized Difference Vegetation Index) Explained
URL: https://www.agroreports.org/articles/what-is-ndvi/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-ndvi.md
> NDVI measures vegetation health from satellite imagery using the ratio of near-infrared and red light. Learn what NDVI values mean for your crop and how to check your field free.
# What is NDVI? (Normalized Difference Vegetation Index)
## What is NDVI?
NDVI is the most widely used satellite index for measuring vegetation health. It boils the answer to *"is my field okay?"* down to a single number between −1 and 1 — derived from how your crops reflect light. If you have ever seen a satellite map where healthy fields glow green and bare soil looks brown, you have seen NDVI. It is the number behind almost every "crop health" map in agriculture, forestry, and climate science.
Leaves do two predictable things with light: they **absorb red light** (chlorophyll uses it to photosynthesize) and they **strongly reflect near-infrared light** (their internal cell structure scatters it). A healthy, leafy canopy therefore shows a big gap between red and near-infrared reflectance. A stressed or sparse canopy does not.
## How it's calculated
NDVI turns that reflectance gap into a single number using a normalized ratio:
```
NDVI = (NIR − Red) / (NIR + Red)
```
On Sentinel-2 satellites (which photograph every field on Earth every five days), these are **Band 8** (NIR) and **Band 4** (Red). Because the formula is a ratio, it cancels out the raw brightness of the image — so NDVI means the same thing whether the scene was captured on a bright or hazy day. When a crop is healthy, red stays low and NIR stays high, so NDVI climbs toward 1. When vegetation dries, thins, or dies, the two reflectances converge and NDVI falls toward 0.
## Typical value ranges
There is no single "good" NDVI — it depends on the crop and the season. Use the table as a starting point, then compare against what's normal for *your* crop and time of year.
| NDVI range | Meaning | Typical for |
|------------|---------|-------------|
| **0.6 – 1.0** | Dense, healthy vegetation | Peak-season crops, mature forest, closed canopy |
| **0.4 – 0.6** | Moderate vegetation | Developing crops, vineyards at full canopy |
| **0.2 – 0.4** | Sparse or stressed | Early growth, drought stress, poor establishment |
| **0.0 – 0.2** | Bare soil or severe stress | Plowed field, senesced vegetation |
| **Below 0** | Water, snow, or cloud | Lakes, snow cover, cloud shadow |
The most useful reading is almost never a single number — it is **how your NDVI compares to previous months and to the same period last year**. A field dropping from 0.7 to 0.5 in two weeks tells you something is wrong, even though 0.5 alone looks acceptable.
## When to use it
NDVI is the universal default for crop monitoring. Use it for general canopy health assessment, tracking seasonal growth curves, and comparing field performance year-over-year. The canonical NDVI pattern for a seasonal crop is a bell curve: low while the field is bare or newly sown, climbing through vegetative growth, peaking at canopy closure, then declining as the crop ripens and senesces. A healthy season looks smooth; stress events show up as dips that break the curve's expected shape.
It works best for moderate-to-dense canopies of annual crops, pastures, and deciduous forest. For very dense canopies (where NDVI saturates above ~0.8), switch to EVI. For sparse or young crops (where soil shows through), use SAVI or MSAVI2. For water stress specifically, NDMI gives days-to-weeks of earlier warning.
## Comparison with other indices
NDVI is the universal baseline — simpler and more widely calibrated than any other index. EVI resists saturation over dense canopies and corrects for atmospheric haze, at the cost of added complexity and sensitivity to image quality. SAVI and MSAVI2 correct for soil background that drags NDVI down in sparse vegetation. NDMI catches drought stress before NDVI, while NDRE detects nitrogen status that NDVI cannot. For most crops through most of the season, NDVI is the right starting point.
> **Try it free:** Get NDVI values for your field — free, no signup. Works on any land worldwide. [Check my field's NDVI →](/)
## Frequently asked questions
What is a good NDVI value?
It depends on the crop and season. For most annual crops at peak growth, **0.6 to 0.8** indicates a healthy, dense canopy. Vineyards and orchards run lower (0.4–0.6 is normal) because rows and management keep the canopy open. Always compare against your own field's history rather than an absolute threshold.
How often is NDVI updated?
The two Sentinel-2 satellites revisit every location on Earth every **five days**, weather permitting. Clouds can block the view for longer, which is why reports use cloud-free composites and trends rather than a single image.
Can NDVI detect crop disease?
NDVI detects the *stress* that disease causes — a drop in green, leafy canopy — but it cannot name the pathogen. A falling NDVI tells you something is wrong and where to look; it does not replace walking the field. Disease, drought, nutrient deficiency, and pest damage all depress NDVI similarly.
Why is my field's NDVI negative?
Negative NDVI usually means the satellite saw **water, snow, or cloud** rather than vegetation. If your field is not a lake, a persistent negative reading almost always means cloud cover or shadow is contaminating the image. A good report filters this out and tells you when the data is unreliable instead of showing a meaningless number.
---
# What is NDWI? (Normalized Difference Water Index) Explained
URL: https://www.agroreports.org/articles/what-is-ndwi/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-ndwi.md
> NDWI is a satellite index that detects open water bodies and surface wetness — lakes, flooded fields, and waterlogged soil. Learn the formula and how it differs from NDMI.
# What is NDWI? (Normalized Difference Water Index)
## What is NDWI?
NDWI, the **Normalized Difference Water Index**, is a satellite index that highlights open water and maps surface wetness. Where NDVI tells you how green a field is, NDWI tells you where water is sitting — whether that is an irrigation reservoir, a flooded rice paddy, or a waterlogged patch after heavy rain. It is the index you reach for when you want to see water: rivers, ponds, flooded fields, saturated soil, or the boundaries of an irrigation dam.
Water absorbs near-infrared light strongly and reflects a little green, while soil and vegetation do the opposite. By exploiting this contrast, NDWI turns the presence of surface water into a single number.
## How it's calculated
NDWI uses the contrast between green and near-infrared reflectance:
```
NDWI = (Green − NIR) / (Green + NIR)
```
On Sentinel-2 these are **Band 3** (Green) and **Band 8** (NIR). Where open water dominates the pixel, green reflectance exceeds NIR and NDWI goes positive. Where vegetation or dry soil dominates, NIR stays high and NDWI goes negative. As with NDVI, the ratio cancels out raw brightness so values are comparable across scenes and dates.
## Typical value ranges
| NDWI range | Meaning | Typical for |
|------------|---------|-------------|
| **0.2 – 1.0** | Open water | Lakes, rivers, reservoirs, flooded fields |
| **0.0 – 0.2** | Wet or saturated surface | Shallow water, wet soil, marsh, snowmelt |
| **−0.1 – 0.0** | Moist vegetation / damp soil | Well-watered crops, dewy mornings |
| **−0.3 – −0.1** | Dry vegetation | Stressed or senescent crops, dry pasture |
| **Below −0.3** | Dry soil / built-up | Bare dry ground, pavement, dense dry vegetation |
The 0.0 line is the practical water/non-water boundary; values above ~0.2 are confidently open water.
## When to use it
Use NDWI when your question is about surface water: flooded rice paddies, drainage after heavy rain, reservoir levels, or waterlogging in low-lying fields. Over a season, NDWI on an irrigated field rises after watering or rainfall and falls steadily as the surface dries. Flooded rice paddies show a sharp spike to positive values during the flooded phase, then collapse toward zero at drainage. Drought shows up as a sustained decline into negative territory.
NDWI works best on rural and agricultural land. In built-up areas, dark rooftops and asphalt can mimic water and push NDWI slightly positive — interpret with care in mixed land-use settings. Small water bodies below the 10 m pixel size (narrow ditches, small ponds) may not fully fill a pixel and read as a partial value.
## Comparison with other indices
The most common confusion is NDWI vs NDMI. **NDWI** (McFeeters, Green/NIR) detects **open surface water** — is there water sitting on the ground? **NDMI** (Gao, NIR/SWIR) measures **water inside leaves** — how much moisture is in the vegetation? A drought-stressed crop can have very low NDMI (dry leaves) while its NDWI reads near zero (no standing water). A flooded field has high NDWI but its NDMI depends on the crop's health, not the floodwater. Pick the index that answers the question you are actually asking.
> **Try it free:** Get NDWI values for your field — free, no signup. Works on any land worldwide. [Check my field's water status →](/)
## Frequently asked questions
What is a good NDWI value?
For crops, the right NDWI depends on what you're monitoring. **Slightly negative values (−0.1 to −0.3)** are normal for healthy vegetated land — you are looking for stability, not high values. Positive values indicate surface water, which may be desirable (irrigation, rice) or a problem (waterlogging). Watch for sudden drops, which indicate drying.
What is the difference between NDWI and NDMI?
NDWI (McFeeters, Green/NIR) detects **open water on the ground**. NDMI (Gao, NIR/SWIR) measures **water inside leaves**. A flooded field has high NDWI; a healthy well-watered crop has high NDMI. They answer different questions — use NDWI for flooding and drainage, NDMI for crop moisture stress.
Can NDWI detect drought?
Indirectly. NDWI shows surface drying, which often accompanies drought, but it does not measure the water inside the plant. For drought stress in vegetation, **NDMI is more reliable** because it responds to leaf water content, not just surface wetness.
Why is my field's NDWI negative?
Negative NDWI on a field simply means there is **no open water** — which is normal for dry land and most crops. It does not mean your crop is thirsty. Only positive NDWI (above zero) indicates standing water; for plant moisture, check NDMI instead.
---
# What is SAVI? (Soil-Adjusted Vegetation Index)
URL: https://www.agroreports.org/articles/what-is-savi/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-savi.md
> SAVI corrects NDVI for soil background — essential for monitoring young or sparse crops where bare soil drags NDVI down. Learn the formula and when to use it.
# What is SAVI? (Soil-Adjusted Vegetation Index)
## What is SAVI?
SAVI, the **Soil-Adjusted Vegetation Index**, is a version of NDVI that corrects for the influence of bare soil. It is the index you reach for when vegetation is sparse — young crops, recently planted fields, arid pastures — where the soil showing through the canopy drags NDVI down and makes a healthy young crop look worse than it is. If NDVI is the everyday choice, SAVI is the early-season and dryland specialist.
## How it's calculated
NDVI assumes the only thing affecting red and near-infrared reflectance is the vegetation. When the canopy is open, that assumption breaks: the soil underneath contributes to both bands, biasing the index downward. SAVI inserts a soil-brightness correction factor `L` into the denominator:
```
SAVI = ((NIR − Red) / (NIR + Red + L)) × (1 + L)
```
On Sentinel-2 these are **Band 8** (NIR) and **Band 4** (Red). The `L` factor is typically **0.5** — a compromise for intermediate vegetation density. `L = 0` reduces SAVI exactly to NDVI (no soil adjustment); `L = 1` applies maximum correction for very sparse vegetation on bright soils.
A critical detail: Sentinel-2 surface-reflectance values are stored on a 0–10,000 scale. Because SAVI *adds* `L` to the bands, the scale does not cancel — `L` must be scaled to match, implemented as `5000` (0.5 × 10000) in DN space. Getting this wrong is the most common silent bug in SAVI implementations.
## Typical value ranges
SAVI occupies the same −1 to +1 range as NDVI, but for sparse canopies it reads higher because it removes the soil-brightness penalty.
| SAVI range | Meaning | Typical for |
|------------|---------|-------------|
| **0.6 – 0.9** | Dense, healthy vegetation | Peak-season closed canopy (SAVI ≈ NDVI here) |
| **0.4 – 0.6** | Moderate vegetation | Developing crops, healthy pasture |
| **0.2 – 0.4** | Sparse but healthy | Young crops, arid rangeland — *where SAVI shines vs NDVI* |
| **0.0 – 0.2** | Very sparse / bare | Newly emerged crop, bare soil |
| **Below 0** | Water, snow, cloud | Open water, snow cover |
The difference between SAVI and NDVI is largest when vegetation cover is low (roughly 10–40%). As the canopy closes, the two indices converge.
## When to use it
A practical rule: **if you can see soil in the image, prefer SAVI.** Once the canopy closes, switch back to NDVI. In early growth, SAVI sits above NDVI by a margin that shrinks as the canopy closes — that gap is the soil-brightness error NDVI was making, now corrected. SAVI is especially valuable in arid rangelands, shrublands, and any field with variable soil brightness that would add noise to NDVI comparisons.
The `L` factor is fixed, not adaptive — you pick `L = 0.5` and accept it as a compromise. If your field has very high or very low vegetation density, a fixed `L` is suboptimal. MSAVI2 solves this with a self-adjusting formulation.
## Comparison with other indices
SAVI is NDVI with a fixed soil-brightness correction — it shines in sparse and early-growth conditions where soil drags NDVI down. MSAVI2 improves on SAVI by computing the soil correction automatically per pixel (no fixed `L`), making it more accurate for heterogeneous fields. NDVI remains the better choice once the canopy closes and soil is invisible. For high-biomass canopies where saturation is the problem (not soil), use EVI instead.
> **Try it free:** Get SAVI values for your field — free, no signup. Works on any land worldwide. [Check my field's establishment →](/)
## Frequently asked questions
What is the difference between SAVI and NDVI?
SAVI is NDVI with a soil-brightness correction. NDVI assumes only vegetation affects the reflectance; SAVI inserts an adjustment factor `L` so that bare soil showing through a sparse canopy does not drag the index down. SAVI and NDVI give nearly identical values once the canopy closes; SAVI matters most when vegetation cover is low (10–40%).
What is the L value in SAVI?
`L` is the soil-brightness adjustment factor, typically set to **0.5** as a compromise for intermediate vegetation density. `L = 0` turns SAVI into NDVI; `L = 1` applies maximum soil correction for very sparse vegetation on bright soils. In Sentinel-2's DN space (0–10,000), `L = 0.5` is implemented as `5000`.
When should I use SAVI instead of NDVI?
Use SAVI when **soil is visible** in the pixel: young or widely spaced crops, arid rangelands, shrublands, and any field with variable soil brightness. Once the canopy closes (mid-to-late season for most annual crops), switch back to NDVI — the soil influence is gone and SAVI offers no further benefit.
What is the difference between SAVI and MSAVI2?
SAVI uses a **fixed** soil-adjustment factor `L = 0.5`. MSAVI2 computes the adjustment **automatically from the data itself** with an iterative formula, so the correction adapts to the actual vegetation density in each pixel. For heterogeneous fields or very sparse vegetation, MSAVI2 is the more accurate choice.
---
# Satellite Crop Monitoring: How It Works (Free, No Drone)
URL: https://www.agroreports.org/articles/satellite-crop-monitoring/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/satellite-crop-monitoring.md
> Free Sentinel-2 satellites photograph every field on Earth every five days — here's how satellite crop monitoring turns that data into actionable farm intelligence.
## How satellite crop monitoring works
Satellite crop monitoring means tracking the health and development of your fields from orbiting satellites — without drones, without in-field sensors, and without enterprise software. The two Sentinel-2 satellites, operated by the European Space Agency, photograph every field on Earth every five days at no cost to you. From this data, vegetation indices like NDVI, NDMI, and NDRE reveal exactly how your crop is doing, week by week, in numbers.
If you have ever wondered *"is my field okay?"* without being there to look — satellite monitoring is the answer.
## What satellite monitoring reveals
From free Sentinel-2 imagery, you can measure:
- **Canopy health and density** (NDVI, EVI) — is the crop growing as expected for its stage?
- **Water stress** (NDMI) — is the crop running dry, days before it looks thirsty?
- **Nitrogen / chlorophyll status** (NDRE) — is the crop well-fed or running short?
- **Floods and drainage** (NDWI) — where is water sitting in the field?
- **Fire risk and burn damage** (NBR, dNBR) — is the land in danger, or how badly did it burn?
- **Seasonal trends** — how does this year compare to last year and the 5-year average?
The key insight: **a trend is more valuable than a snapshot.** A single NDVI reading of 0.5 tells you little. An NDVI that has dropped from 0.7 to 0.5 in two weeks tells you something is wrong. This is why monitoring systems compare against history, not just absolute thresholds.
## The six-step process
1. **Identify the field** — draw or define the boundary (a rectangle, a polygon, or saved field shape).
2. **Pull the satellite image** — Sentinel-2 captures the location every 5 days, cloud permitting.
3. **Filter clouds and shadows** — only clean pixels are used; hazy images are rejected.
4. **Compute vegetation indices** — the raw reflectance bands are combined into NDVI, NDMI, and other indices.
5. **Compare against history** — the current values are compared to previous months, years, and seasonal norms.
6. **Interpret** — the numbers are translated into plain words: "Your crop is healthy and well-watered" or "Water stress is increasing — consider irrigating."
This is exactly what AgroReports does, end to end, for free.
## Key metrics
| Approach | Coverage | Frequency | Cost | Setup |
|----------|----------|-----------|------|-------|
| **Satellite (Sentinel-2)** | Every field on Earth | Every 5 days | **Free** | None — just draw the boundary |
| **Drones** | Limited flight range | On-demand (you fly it) | €1,000–15,000 + time | Buy, learn to fly, process imagery |
| **In-field sensors** | One point per sensor | Continuous | €200–2,000/sensor + maintenance | Install, calibrate, maintain, replace |
Satellites win on coverage and cost. Drones win on resolution and timing control. Sensors win on real-time frequency. They are complementary, not competitive — but for most farmers, satellite monitoring is the only one that is free, zero-setup, and covers the whole farm.
## What satellite monitoring cannot do
- **It cannot replace walking the field.** A satellite tells you *where* and *when* something is wrong; it rarely tells you *what*. Ground-truthing remains essential.
- **It cannot see through clouds.** Persistent cloud cover creates data gaps. Good systems use composites (multi-date medians) and tell you when data is unreliable.
- **Resolution has limits.** At 10 m/pixel, Sentinel-2 cannot see individual plants. It sees the average of a 10×10 m patch — fine for fields, inadequate for individual vines or trees.
- **It cannot name a disease.** Satellite detects the *stress signature* (declining NDVI/NDMI); it cannot identify the pathogen.
> **Free report:** Monitor your field from satellite — draw the boundary, and AgroReports computes all relevant indices, compares against 3–5 years of history, and writes a plain-language report. Free, no signup. [Check my field →](/)
Is satellite crop monitoring really free?
Yes. The Sentinel-2 satellites are funded by the European Space Agency and the European Commission; their data is public and free. The raw imagery is downloadable by anyone. What AgroReports adds is the processing: cloud masking, index computation, historical comparison, and plain-language interpretation — so you get an answer instead of raw data.
How often is satellite crop data updated?
Sentinel-2 revisits every location on Earth every **five days** (two satellites working in tandem). However, clouds can block the view — in cloudy regions or seasons, the usable update frequency may be longer. Good monitoring systems use cloud-free composites (combining multiple dates) to fill gaps and flag when the data quality is low.
Do I need a drone for satellite monitoring?
No. Satellite monitoring uses orbiting satellites — you do not need to buy, fly, or maintain any hardware. Drones offer higher resolution and on-demand timing, but at significant cost and effort. For most farmers, satellite data is sufficient and vastly more practical.
How accurate is satellite crop monitoring?
Accurate enough to guide decisions when used correctly. The indices correlate well with ground observations of canopy health, biomass, and water stress — published R² values for NDVI vs field-measured biomass typically exceed 0.8 for most crops. The caveats: cloud contamination degrades quality, and no index replaces ground-truthing for diagnosis. The best approach combines satellite trends with targeted field visits.
---
# What Is a Vegetation Index? (NDVI, EVI, SAVI, NDMI…)
URL: https://www.agroreports.org/articles/what-is-a-vegetation-index/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/what-is-a-vegetation-index.md
> A vegetation index turns satellite reflectance into a single number describing crop health. Learn what they are, how the 8 most important indices differ, and how to choose the right one.
# What Is a Vegetation Index?
## What is a vegetation index?
A vegetation index is a number, computed from satellite reflectance data, that summarises the state of vegetation on the ground. Instead of looking at raw light values (which are hard to interpret and vary with sun angle, haze, and sensor calibration), an index combines specific bands of light into a single figure that correlates directly with something a farmer cares about: how green, how dense, how wet, or how well-fed the canopy is.
The most famous is NDVI — but NDVI is one of many, each tuned to a different aspect of plant health and each with its own strengths and blind spots.
## How satellites measure vegetation
Every vegetation index is a formula that takes two or more bands of satellite reflectance and produces a number, typically between −1 and 1. The bands are chosen for what they reveal:
- **Red light** — absorbed by chlorophyll. Healthy leaves absorb it; stressed leaves reflect more.
- **Near-infrared (NIR)** — reflected strongly by healthy leaf cell structure. Stressed or sparse canopies reflect less.
- **Shortwave-infrared (SWIR)** — absorbed by water in the leaf. Dry leaves reflect more.
- **Red-edge** — a narrow transition band sensitive to chlorophyll concentration; shifts with nitrogen status.
- **Green** — reflected by water surfaces; used in water-body detection.
The indices combine these bands as ratios (so the result is independent of scene brightness) or as more complex formulas with correction factors for soil, atmosphere, or canopy structure. The key insight: a ratio cancels out the raw brightness of the image, so the index means the same thing whether the scene was captured on a bright or hazy day.
## Common indices at a glance
| Index | Measures | Best for | Read more |
|-------|----------|----------|-----------|
| **NDVI** | Canopy greenness and density | General health monitoring, most crops | [What is NDVI? →](/articles/what-is-ndvi) |
| **EVI** | Dense-canopy biomass | High-biomass crops, tropical forest, hazy regions | [What is EVI? →](/articles/what-is-evi) |
| **SAVI** | Sparse-canopy vegetation (soil-corrected) | Young crops, arid land, early growth | [What is SAVI? →](/articles/what-is-savi) |
| **MSAVI2** | Sparse-canopy vegetation (self-adjusting) | Heterogeneous fields, very sparse cover | [What is MSAVI2? →](/articles/what-is-msavi2) |
| **NDMI** | Leaf water content | Drought monitoring, irrigation, fire risk | [What is NDMI? →](/articles/what-is-ndmi) |
| **NDWI** | Surface water bodies | Flood mapping, drainage, rice paddies | [What is NDWI? →](/articles/what-is-ndwi) |
| **NDRE** | Chlorophyll / nitrogen status | Variable-rate nitrogen, mid-late season | [What is NDRE? →](/articles/what-is-ndre) |
| **NBR** | Fuel condition / burn severity | Fire risk and post-fire damage assessment | [What is NBR? →](/articles/what-is-nbr) |
## Choosing the right index
Most monitoring starts with **NDVI** — it is the universal baseline. From there, add indices based on your question:
- *"Is the canopy dense enough?"* → NDVI, and switch to **EVI** if it saturates
- *"Is the crop well-watered?"* → **NDMI** (water stress appears here before NDVI)
- *"Is the crop well-fed (nitrogen)?"* → **NDRE** (chlorophyll, not just biomass)
- *"Is the young crop establishing properly?"* → **SAVI** or **MSAVI2** (soil-corrected)
- *"Is there standing water on my field?"* → **NDWI** (surface water detection)
- *"Is my land at fire risk?"* → **NBR** and **NDMI** (fuel condition + moisture)
- *"How badly did the fire burn?"* → **dNBR** (differenced NBR, pre vs post)
No single index answers every question. The best reports combine several and interpret them together. And never trust a single satellite snapshot — cloud cover can make a healthy field look dying. A trustworthy system uses cloud-masked imagery, looks at trends over multiple dates, compares against seasonal history, and tells you when the data is unreliable.
> **Free report:** Check your field's vegetation health — free satellite analysis, no signup. Works on any land worldwide. [Check my field's vegetation indices →](/)
## Frequently asked questions
What is the most commonly used vegetation index?
**NDVI** (Normalized Difference Vegetation Index) is the most widely used. It measures canopy greenness and density from the ratio of near-infrared and red reflectance. It is simple, universal, and an excellent default — but it saturates over dense canopies and does not directly measure water or nitrogen. The other indices address these gaps.
Can vegetation indices detect crop disease?
Indirectly. Disease causes stress, which indices can detect as a drop in chlorophyll, moisture, or canopy density. NDVI, NDRE, and NDMI may all decline before visible symptoms appear. But no satellite index can identify the specific pathogen — it tells you *where and when* stress is occurring, not *what* caused it. Walking the field remains essential for diagnosis.
What satellite do vegetation indices come from?
For agricultural monitoring, the most common source is **Sentinel-2** — two satellites (Sentinel-2A and 2B) operated by the European Space Agency that photograph every point on Earth every five days at 10–20 m resolution. The data is free and publicly available. Landsat (NASA) is also used, though at lower revisit frequency. AgroReports uses Sentinel-2 because of its 5-day revisit and 10 m resolution.
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# Is My Field Healthy? How to Check (Free, From Satellite)
URL: https://www.agroreports.org/articles/is-my-field-healthy/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/is-my-field-healthy.md
> Answer 'is my field healthy?' in 30 seconds from free Sentinel-2 data — satellite indices reveal crop status before you can see it from the ground.
## What "field health" means from space
This is the question every farmer asks, every season. Until recently, the answer required either walking every hectare or buying expensive software. Now, a satellite does it for you — for free, in 30 seconds, from public Sentinel-2 data.
The answer comes in three parts: **how green** the canopy is (NDVI), **how well-watered** it is (NDMI), and **how that compares to normal** for this time of year and this crop.
## The three signs of a healthy field
A healthy field shows three things on satellite:
1. **NDVI in the expected range for the crop and season.** A wheat field at heading should be 0.7+; a vineyard at peak canopy should be 0.4–0.5. There is no universal "good" — it depends on what you're growing and when.
2. **NDMI positive and stable.** A well-watered crop has NDMI above 0.1; a falling NDMI signals developing water stress before the canopy visibly wilts.
3. **A trend that matches the season.** NDVI should climb through spring, hold through summer, and decline at senescence — for an annual crop. Evergreens (orchards, forests) should stay relatively stable.
If all three check out, your field is probably fine. If any one is off, that is where to focus your attention.
## Early warning signs
The earliest warning signs from satellite are:
- **NDMI dropping while NDVI holds.** Water stress is starting but hasn't visibly affected the canopy yet. This is the earliest signal — days to weeks before visible wilting. Action: check irrigation.
- **NDVI dropping suddenly.** The canopy is losing green biomass — stress is now severe enough to affect growth. Causes include drought, disease, pest damage, nutrient deficiency, or herbicide drift. Action: walk the field.
- **NDVI lower than the same period last year.** The crop is behind its normal curve. Could be late planting, poor establishment, nutrient deficiency, or weather. Action: compare against last year's management.
- **Spatial variation across the field.** One patch significantly lower than the rest signals a localised problem — poor drainage, compaction, local pest, or soil variation. Action: target that zone for inspection.
## Satellite monitoring insights
Satellite vegetation indices correlate well with ground-level crop status — published R² values for NDVI vs field-measured biomass typically exceed 0.8 for most crops. The critical advantage of satellite monitoring is that leaf water content drops before chlorophyll degrades, so **NDMI falls before NDVI**. A crop can be measurably water-stressed for days to weeks before visible wilting or yellowing appears. This early-warning window is when intervention is cheapest and most effective.
## Key metrics
| Index | What it measures | Healthy signal |
|-------|-----------------|----------------|
| NDVI | Canopy greenness and density | In range for crop + season, steady or climbing |
| NDMI | Leaf/canopy water content | Positive (> 0.1) and stable |
| NDRE | Chlorophyll (nitrogen) status | Stable — declining = nutrient stress |
> **Free report:** Check if your field is healthy — enter the location, and AgroReports computes NDVI, NDMI, NDRE, compares against 3–5 years of your field's own history, and writes a plain-language report. Free, no signup. [Check my field →](/)
How can I tell if my field is healthy without visiting it?
Satellite vegetation indices — especially NDVI (canopy health), NDMI (water stress), and NDRE (chlorophyll) — correlate well with ground-level crop status. A field with stable or rising NDVI, positive NDMI, and values in the expected range for the crop and season is almost certainly fine. A sudden drop in any index is an early warning. AgroReports computes all of these and interprets them for you.
What NDVI value means my crop is healthy?
It depends entirely on the crop and the time of year. Wheat at heading: 0.7+. A vineyard at peak canopy: 0.4–0.5. A young crop in early growth: 0.2–0.4. There is no universal threshold. The most useful reading is the **trend** — is your NDVI tracking the expected seasonal curve, and how does it compare to the same period last year?
Can satellite detect crop stress before I can see it?
Yes — this is satellite monitoring's biggest advantage. Leaf water content drops before chlorophyll degrades, so **NDMI falls before NDVI**. A crop can be measurably water-stressed for days to weeks before visible wilting or yellowing appears. This early-warning window is when intervention is cheapest and most effective.
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# Alentejo Cork Oak Forests — Live Satellite Health Report
URL: https://www.agroreports.org/articles/alentejo-cork-oak-forests/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/alentejo-cork-oak-forests.md
> Satellite monitoring of Portugal's cork oak montado reveals whether the canopy is holding or dying back — updated monthly, free.
## About this region
The Alentejo montado is a savanna-like agroforestry system of cork oak (*Quercus suber*) and holm oak scattered across the plains of southern Portugal. It is one of the most valuable and threatened ecosystems in Europe — Portugal produces roughly half of the world's cork. The oaks are stripped of their bark every 9–12 years in a skilled harvest that does not kill the tree if done correctly, making this one of the world's most remarkable examples of sustainable tree farming.
## Climate & growing cycle
The climate is hot Mediterranean with long, dry summers. The oaks are evergreen but drought-stressed — climate change and desertification are a live threat, with dieback episodes becoming more frequent. Summers routinely push temperatures above 38 °C with less than 500 mm of annual rainfall, and the deep-rooted trees depend entirely on winter moisture stored in the soil profile.
## Satellite monitoring insights
NDVI runs persistently low (0.2–0.4) on the montado because the canopy is open and the trees are widely spaced. What matters here is the *trend*: a slow, multi-year decline in NDVI or NDMI signals the montado is dying back — not seasonal variation, but structural decline. NBR is a critical early-warning index for fire risk in the dry grass understory, which can carry flames between the trees during the long summer. Unlike row crops, cork oak health is measured in years, not weeks — making 3–5 year satellite records essential.
## Key metrics
| Metric | Typical range | What to watch |
|--------|--------------|---------------|
| NDVI | 0.2–0.4 | Multi-year decline = dieback |
| NDMI | −0.3 to 0.2 | Persistent lows = severe drought stress |
| NBR | Variable | Drops in understory NBR = rising fire risk |
> **Free report:** Get a live satellite health analysis of Alentejo's cork oak montado this month — see the NDVI trend, fire risk, and moisture status for free, no signup. [Check the montado →](/reports/alentejo-cork-oak-forests)
---
# Alqueva Olive Mega-Farms — Live Satellite Health Report
URL: https://www.agroreports.org/articles/alqueva-olive-mega-farm/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/alqueva-olive-mega-farm.md
> Satellite monitoring of Alqueva's super-intensive olive plantations tracks canopy density and irrigation performance month by month — free.
## About this region
The Alqueva reservoir in Portugal's Alentejo is the hub of a new kind of agriculture: super-intensive olive farming. Drip-irrigated hedgerows of Arbequina and Arbosana olives, planted at densities of 1,500+ trees per hectare, are machine-harvested for olive oil at industrial scale. This is a modern, high-input system — the opposite of Jaén's traditional rain-fed groves — and the Alqueva irrigation project transformed this semi-arid region by guaranteeing water from Europe's largest artificial lake.
## Climate & growing cycle
Flowering falls in May and harvest runs from October to November. The region's hot Mediterranean summers (regularly above 40 °C) would make olive growing impossible without the Alqueva reservoir. The drip-irrigation system is the linchpin of the entire operation, and its performance determines the crop outcome each year.
## Satellite monitoring insights
Because the hedgerows are dense and irrigated, NDVI runs higher than traditional olive groves (0.4–0.6), with a clear seasonal cycle that peaks in late spring and holds through summer. EVI and SAVI are useful here because the rows are closed-canopy — standard NDVI saturates less easily at these densities. Drought stress is mostly an irrigation management issue: NDMI tracks whether the drip system is keeping up with the heat, and a falling NDMI in July or August is a direct readout of inadequate irrigation before yield is lost.
## Key metrics
| Index | Typical range | What it reveals |
|-------|--------------|-----------------|
| NDVI | 0.4–0.6 | Canopy density — higher than traditional groves |
| NDMI | −0.1 to 0.3 | Irrigation performance — the critical management index |
| EVI | 0.3–0.5 | Performance in dense-canopy hedgerows |
> **Free report:** Get a live satellite health analysis of Alqueva's olive mega-farms this month — see canopy vigour, irrigation status, and how the crop is tracking for free, no signup. [Check the olives →](/reports/alqueva-olive-mega-farm)
---
# Alsace Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/alsace-wine-route/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/alsace-wine-route.md
> Satellite monitoring of Alsace vineyards tracks canopy health and moisture stress through the dry growing season — free, updated monthly.
## About this region
Alsace is a narrow strip of east-facing slopes on the foothills of the Vosges mountains in northeastern France — one of the country's driest and sunniest wine corridors. Riesling, Gewurztraminer, Pinot Gris, and Muscat produce aromatic, structured whites that rank among the finest and most underrated wines in France. The soils change every few hundred metres: granite, limestone, volcanic, and sandstone create an intricate mosaic of terroir within a single hillside.
## Climate & growing cycle
The Vosges rain shadow makes Alsace one of France's driest wine regions, with less than 500 mm of annual rainfall. The continental climate brings cold winters and warm summers. Budbreak falls in early April, flowering in early June, and harvest runs from late September to October. The canopy is vertical and tall, trained on high wires to maximise sun interception on the east-facing slopes.
## Satellite monitoring insights
NDVI peaks at 0.4–0.55 at full canopy on Alsace vines — typical for a well-managed vertical canopy. The dry climate makes NDMI (moisture) the most important index here: water stress is a recurring concern in this rain-shadowed region, and NDMI drops before the canopy shows any visible sign of stress. EVI is valuable for detecting within-season variation on the steep slopes, where aspect and soil type create micro-climates that affect ripening differently across even small vineyard blocks.
## Key metrics
| Index | Peak range | Significance |
|-------|-----------|--------------|
| NDVI | 0.4–0.55 | Canopy density at full leaf |
| NDMI | −0.1 to 0.2 | Water stress — the key index for rain-shadowed vines |
| EVI | 0.2–0.4 | Slope-level variation in biomass |
> **Free report:** Get a live satellite health analysis of Alsace vineyards this month — see canopy vigour, water stress, and how this vintage is tracking for free, no signup. [Check the vines →](/reports/alsace-wine-route)
---
# Alto Adige Apple Orchards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/alto-adige-apple-orchards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/alto-adige-apple-orchards.md
> Satellite monitoring of South Tyrol's apple orchards reveals canopy health and chlorophyll status through the alpine growing season — free, monthly.
## About this region
Alto Adige (South Tyrol) is Italy's largest apple-growing region and one of Europe's premier orchard districts. The steep valleys of the Alps create an ideal combination: high-altitude sunshine, cool nights that enhance colour and aroma, and irrigation from glacial melt. Gala, Golden Delicious, Fuji, and Pink Lady dominate, grown on intensive dwarfing rootstocks under hail nets that protect the fruit but add a layer of complexity for satellite monitoring.
## Climate & growing cycle
The climate is alpine-continental, protected by mountains from extreme weather. Apples flower dramatically in April–May, grow through summer, and are harvested from August to November. The orchards are evergreen-ish — the canopy does not collapse annually the way row crops do, but leaf-out in spring and leaf-fall in autumn create a clear seasonal signal. The hail nets complicate satellite monitoring slightly by filtering and scattering incoming light, though the effect is stable across seasons and can be calibrated for.
## Satellite monitoring insights
NDVI peaks at 0.5–0.7 in Alto Adige orchards — high for a tree crop, reflecting the dense planting on dwarfing rootstock. NDRE (chlorophyll) is the key management index for apples: it tracks nitrogen status and fruit quality potential through the growing season. NDMI is equally important, as water stress during fruit sizing directly affects apple size and quality at harvest. MSAVI2 performs well here because it handles the under-canopy soil reflection that hail nets and dense rows introduce.
## Key metrics
| Index | Peak range | Management relevance |
|-------|-----------|---------------------|
| NDVI | 0.5–0.7 | Canopy density — tracks seasonal development |
| NDRE | 0.3–0.5 | Chlorophyll/nitrogen — fruit quality indicator |
| NDMI | −0.1 to 0.2 | Water stress — directly affects fruit size |
> **Free report:** Get a live satellite health analysis of Alto Adige apple orchards this month — see canopy development, chlorophyll levels, and water status for free, no signup. [Check the orchards →](/reports/alto-adige-apple-orchards)
---
# Beauce Cereal Plains — Live Satellite Health Report
URL: https://www.agroreports.org/articles/beauce-cereal-plains/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/beauce-cereal-plains.md
> Satellite monitoring of France's breadbasket reveals the seasonal pulse of wheat and barley from green-up to harvest — free, monthly.
## About this region
The Beauce is the granary of France — a vast, flat, fertile plateau southwest of Paris where wheat, barley, and corn stretch to the horizon across some of the country's largest farms. The deep loam soils over limestone bedrock hold moisture well and support high yields, making this one of Europe's most productive cereal regions. The landscape is open and homogeneous, which makes satellite monitoring unusually clean: uniform fields, minimal terrain shadow, and consistent crop types across large areas.
## Climate & growing cycle
The climate is temperate oceanic with reliable summer rainfall. Winter wheat is sown in October, tillers through winter, and harvests in July–August. Corn (maize) follows a spring planting cycle with harvest in September–October. The annual cycle is dramatic: bare soil after ploughing, rapid green-up through spring, a dense canopy at heading, and a near-instant collapse at harvest — the classic bell-curve signature of an annual row crop, visible from space with unmistakable clarity.
## Satellite monitoring insights
NDVI on the Beauce follows the most dramatic seasonal curve in European agriculture: near zero after autumn ploughing, climbing steeply through spring canopy development, peaking above 0.8 at heading, then collapsing at harvest. NDMI tracks water stress during grain fill — the critical yield-determining period in June. EVI is valuable here for differentiating high-biomass areas that saturate NDVI. Because the Beauce is so flat and uniform, spatial variation in any index is a reliable signal of a soil or management difference worth investigating.
## Key metrics
| Index | Peak range | Seasonal pattern |
|-------|-----------|-----------------|
| NDVI | 0.8+ at heading | Bare soil → peak → harvest crash |
| NDMI | 0.0–0.3 | Holds through grain fill, drops at senescence |
| EVI | 0.4–0.6 | Less saturation at peak biomass than NDVI |
> **Free report:** Get a live satellite health analysis of the Beauce cereal plains this month — see the canopy curve, water stress, and how this year compares to history for free, no signup. [Check the fields →](/reports/beauce-cereal-plains)
---
# Bordeaux Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/bordeaux-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/bordeaux-vineyards.md
> Satellite monitoring of Bordeaux's Médoc vineyards tracks canopy health through the vintage — see what the vines are saying this month, free.
## About this region
Bordeaux is the world's most studied wine region, and the Médoc — the gravel peninsula north of the city — is its most famous stretch. This area covers the classic appellations of Margaux, Pauillac, Saint-Julien, and Saint-Estèphe, where Cabernet Sauvignon dominates the blend and Merlot fills it out. The vines sit on deep gravel soils left by ancient rivers, which drain freely and force roots deep — stress that, in the right measure, produces the structured, age-worthy reds Bordeaux is known for.
## Climate & growing cycle
The climate is maritime and mild, with the Atlantic and the Gironde estuary moderating temperature swings. Vintage variation is real here — the same vineyard can make a great wine one year and a merely good one the next. Flowering falls in early June, véraison (colour change) in early August, and harvest from mid-September into October. The growing season is long and the vines are trellised on vertical wires with rows kept open to manage disease pressure in the humid maritime air.
## Satellite monitoring insights
NDVI on a Bordeaux vineyard typically peaks around 0.5–0.6 at full canopy — lower than a row crop, because the vines are trellised and inter-row spacing is deliberate. What matters here is the trajectory: a slow climb through spring signals a healthy, well-timed development; a plateau or early decline in July or August signals stress during the critical ripening window. NDMI is essential for monitoring water status in the free-draining gravels, where too much stress too early can shut down ripening. SAVI is useful for vineyards on gravel and sand soils where bare-soil reflection can distort standard NDVI readings.
## Key metrics
| Index | Peak range | Vintage signal |
|-------|-----------|---------------|
| NDVI | 0.5–0.6 | Canopy development trajectory = vintage quality |
| NDMI | 0.0–0.2 | Water stress in gravels during ripening |
| SAVI | 0.3–0.5 | Corrects for bare gravel between rows |
> **Free report:** Get a live satellite health analysis of Bordeaux vineyards this month — see the canopy curve, water status, and how this vintage is shaping up for free, no signup. [Check the vines →](/reports/bordeaux-vineyards)
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# Burgundy Vineyards (Côte de Beaune) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/bourgogne-cote-de-beaune/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/bourgogne-cote-de-beaune.md
> Satellite monitoring of Burgundy's Côte de Beaune tracks the low, dense canopy of grand cru Pinot Noir and Chardonnay through the vintage — free, monthly.
## About this region
The Côte de Beaune is the southern half of Burgundy's Côte d'Or — the narrow limestone escarpment that produces the world's most prized Pinot Noir and Chardonnay. This stretch covers famous villages including Meursault, Puligny-Montrachet, and Pommard, where the mosaic of limestone soils — varying by the metre — defines each vineyard's character down to the parcel. A few rows of vines can separate a village-level wine from a grand cru worth ten times the price.
## Climate & growing cycle
Burgundy's continental climate brings cold winters, spring frost risk, and warm summers. Pinot Noir ripens late here, with harvest typically falling in mid-to-late September. The vines are trained close to the ground to capture heat radiated by the limestone at night — a necessity at this northern latitude. The growing season is tense: a late frost in April can wipe out the vintage; a cool, wet June can set back flowering and reduce crop load.
## Satellite monitoring insights
NDVI on Burgundy vines runs lower than most wine regions (0.3–0.5 at peak), because planting density is extremely high but the canopy itself is kept short and managed intensively. This low baseline means even a small deviation is meaningful — a 0.05 drop in NDVI in July can signal disease pressure or water stress that would impact grape quality. NDMI is critical in the limestone soils, which drain quickly and can stress vines during dry spells in August. The small parcel sizes (sometimes under a hectare) mean satellite monitoring at 10 m resolution can resolve individual vineyard blocks with useful precision.
## Key metrics
| Index | Peak range | Significance |
|-------|-----------|-------------|
| NDVI | 0.3–0.5 | Low, dense canopy — small changes matter |
| NDMI | 0.0–0.2 | Limestone drainage stress during August |
| SAVI | 0.2–0.4 | Corrects for bare limestone between rows |
> **Free report:** Get a live satellite health analysis of Burgundy's Côte de Beaune vineyards this month — see canopy health, water status, and how the vintage is tracking for free, no signup. [Check the vines →](/reports/bourgogne-cote-de-beaune)
---
# Camargue Rice Fields — Live Satellite Health Report
URL: https://www.agroreports.org/articles/camargue-rice-fields/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/camargue-rice-fields.md
> Satellite monitoring of Camargue rice paddies reveals the flooded-to-harvest cycle with NDVI and NDWI — free, updated monthly.
## About this region
The Camargue is the Rhône delta — a flat, salty, wind-scoured wetland in southern France where rice is the defining crop. Flooding the paddies with fresh Rhône water leaches salt from the soil and creates the standing-water environment rice demands. This is one of the few places in Europe where NDWI (water index) is as important as NDVI for monitoring crop status, because the flooded phase is a deliberate and essential part of the agricultural cycle.
## Climate & growing cycle
Rice is sown in April–May into flooded fields, grows through the hot Mediterranean summer, and is drained for harvest in September–October. The seasonal cycle is extreme: bare or flooded in spring, a wall of green by July, and bare again by autumn. The Mediterranean heat and abundant Rhône water make this a uniquely productive rice environment at Europe's edge.
## Satellite monitoring insights
NDVI peaks above 0.8 at full canopy — as high as any crop on Earth — reflecting the dense, intensely green rice canopy. NDWI swings positive during the flooded phase, producing one of the clearest water-cycle signals visible from space: the fields transition from a water signature in spring to a vegetation signature in summer and back to bare soil in autumn. This dual-index monitoring is essential for rice — NDVI tells you the crop status, but NDWI confirms the paddies are properly flooded. A field with low NDWI in May is a field that wasn't flooded on schedule.
## Key metrics
| Index | Seasonal range | What it reveals |
|-------|---------------|-----------------|
| NDVI | 0.1 → 0.8+ → 0.2 | Canopy development from flooding to harvest |
| NDWI | Positive in spring, neutral in summer | Flood status — the critical management signal |
| NDMI | 0.1–0.4 | Water stress under the Mediterranean sun |
> **Free report:** Get a live satellite health analysis of Camargue rice paddies this month — see canopy development, flood status, and how the crop is progressing for free, no signup. [Check the paddies →](/reports/camargue-rice-fields)
---
# Central Valley Almond Orchards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/central-valley-almond-belt/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/central-valley-almond-belt.md
> Satellite monitoring of California's almond belt tracks canopy health and water stress through the irrigation season — free, monthly.
## About this region
California's Central Valley produces roughly 80% of the world's almonds — a staggering concentration on millions of irrigated acres stretching from Bakersfield to Redding. Almonds are the state's most valuable agricultural export, and the orchards are sustained by an increasingly stressed network of surface water deliveries and groundwater pumps. In some areas, aquifer overdraft has caused measurable land subsidence, making water the defining constraint on the entire industry.
## Climate & growing cycle
The climate is hot Mediterranean: dry summers, wet winters, with summer temperatures regularly exceeding 38 °C. Almonds bloom dramatically in February–March — the famous "pink peak" visible from satellite when millions of acres turn pink simultaneously. Nuts set through spring and harvest in August–September by mechanically shaking the trees. NDVI follows a distinctive pattern: a spring peak at canopy development, stability through summer, then a rapid drop at harvest as leaves are shaken off with the nuts.
## Satellite monitoring insights
Water stress — detectable in NDMI — is the central management concern in the almond belt. A falling NDMI in July directly signals inadequate irrigation during kernel fill, when water demand peaks. NDRE (chlorophyll) tracks nitrogen status and overall tree vigour through the growing season. NDVI runs between 0.3–0.6 depending on tree age and spacing, with younger, more densely planted orchards running higher. The distinctive harvest-triggered NDVI drop is one of the most striking seasonal signals in global agriculture — entire counties go from green to bare in weeks.
## Key metrics
| Index | Typical range | Management signal |
|-------|--------------|-------------------|
| NDVI | 0.3–0.6 | Spring peak, harvest crash in August |
| NDMI | −0.2 to 0.2 | Irrigation adequacy — the defining index |
| NDRE | 0.2–0.4 | Chlorophyll and nitrogen status |
> **Free report:** Get a live satellite health analysis of California almond orchards this month — see canopy health, water stress, and how the crop is tracking for free, no signup. [Check the orchards →](/reports/central-valley-almond-belt)
---
# Champagne Vineyards (Côte des Blancs) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/champagne-cote-des-blancs/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/champagne-cote-des-blancs.md
> Satellite monitoring of Champagne's Côte des Blancs tracks the modest canopy of grand cru Chardonnay through the coolest vintage in France — free, monthly.
## About this region
The Côte des Blancs is the Chardonnay heartland of Champagne — a south-facing chalk slope south of Épernay where nearly every village holds grand cru status. The chalk subsoil, the same Cretaceous limestone that gives champagne its finesse, drains freely and stores heat, ripening Chardonnay to the precise acidity the méthode champenoise demands. This narrow strip of hillside produces some of the most valuable white-wine grapes on Earth.
## Climate & growing cycle
Champagne's climate is the coolest of France's major wine regions, at the northern limit of viable viticulture. Spring frost is a persistent threat; flowering falls in mid-June and harvest in late September. The canopy is kept open to manage acid and disease pressure — in a region where ripeness is measured in acidity, not sugar, excessive canopy vigour is a flaw, not a sign of health. The chalk soil's heat-storing capacity is the difference between ripe and unripe in cool years.
## Satellite monitoring insights
NDVI typically peaks at 0.3–0.5 on the Côte des Blancs — modest by most standards, but correct for a region where canopy management actively suppresses vigour. A slow-climbing NDVI through June and July is the desired pattern: it signals steady, controlled development rather than a vegetative surge that would dilute acidity. NDRE is useful here because chlorophyll levels at veraison correlate with the aromatic potential that defines grand cru champagne. The south-facing aspect of the slope means satellite monitoring captures the sun-exposed side of the canopy, maximising the signal quality.
## Key metrics
| Index | Peak range | Champagne-specific signal |
|-------|-----------|--------------------------|
| NDVI | 0.3–0.5 | Controlled, slow climb = good; surge = excessive vigour |
| NDRE | 0.2–0.35 | Chlorophyll at veraison = aromatic potential |
| NDMI | 0.0–0.2 | Chalk moisture reserve through dry spells |
> **Free report:** Get a live satellite health analysis of Champagne's Côte des Blancs this month — see canopy development, chlorophyll levels, and how the vintage is tracking for free, no signup. [Check the vines →](/reports/champagne-cote-des-blancs)
---
# Chianti Classico Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/chianti-classico-tuscany/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/chianti-classico-tuscany.md
> Satellite monitoring of Chianti Classico's hillside Sangiovese reveals canopy health and water stress through the Tuscan summer — free, monthly.
## About this region
Chianti Classico is the historic heart of Tuscan winemaking — the hills between Florence and Siena where Sangiovese has been cultivated for centuries. The region's distinctive terroir comes from its Galestro (flaky schist-marl) and Alberese (limestone) soils, which give Sangiovese its characteristic acidity, tannin, and cherry-savory profile. The landscape is a mosaic of vines, olive groves, and forest, creating one of Italy's most iconic cultural landscapes.
## Climate & growing cycle
The climate is Mediterranean with continental influence: warm dry summers, cool nights at altitude (250–600 m). Sangiovese buds in early April, flowers in early June, and ripens slowly through late summer, with harvest from mid-September to early October. Vineyards are typically trained on vertical wires or as free-standing bush vines, keeping the canopy open. The altitude and dry-farming tradition mean the vines are deliberately stress-farmed — irrigation is rare, and the canopy is managed to concentrate flavour, not maximise biomass.
## Satellite monitoring insights
NDVI peaks around 0.4–0.5 on Chianti Classico vines — the open canopy style and altitude keep the index lower than irrigated plains. This modest baseline makes trend interpretation straightforward: a steady plateau through July and August is normal; a decline signals water stress from the dry Tuscan summer. NDMI is the key index here, as the region's dry-farmed tradition means rainfall — not irrigation — determines vine water status. SAVI is useful for correcting the bare-soil effect on the rocky Galestro and Alberese soils between vineyard rows. The mixed landscape of vines, olives, and forest creates a patchwork signal that satellite monitoring can deconvolve to isolate vineyard blocks.
## Key metrics
| Index | Peak range | Management insight |
|-------|-----------|-------------------|
| NDVI | 0.4–0.5 | Open canopy, stress-farmed |
| NDMI | −0.1 to 0.1 | Dry-farmed water status — rain-dependent |
| SAVI | 0.2–0.4 | Soil-corrected for rocky Galestro |
> **Free report:** Get a live satellite health analysis of Chianti Classico vineyards this month — see canopy vigour, water stress, and how this vintage is developing for free, no signup. [Check the vines →](/reports/chianti-classico-tuscany)
---
# Douro Valley Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/douro-valley-port-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/douro-valley-port-vineyards.md
> Satellite monitoring of the Douro's steep schist terraces reveals how Touriga Nacional handles the brutal continental heat — free, monthly.
## About this region
The Douro Valley is the world's oldest demarcated wine region (1756) and the birthplace of port wine. The vineyards climb the steep schist slopes of the river valley on hand-built terraces — one of the most dramatic agricultural landscapes on Earth. Touriga Nacional, Touriga Franca, and Tinta Roriz are the key varieties, producing both fortified ports and increasingly fine dry table wines. The terraced schist slopes, visible as regular geometric patterns from space, are unmistakable in satellite imagery.
## Climate & growing cycle
The climate is harsh continental sheltered by mountains: very hot, dry summers (often over 40 °C) and cold winters. The schist soils crack and drain, forcing roots deep into fissures. Most vineyards are dry-farmed — no irrigation — making the Douro one of the most stress-intensive viticultural environments in the world. Flowering falls in late May, véraison in August, and harvest from mid-September to October.
## Satellite monitoring insights
The steep terrain and sparse, stress-farmed canopy keep NDVI modest (0.3–0.5). A sudden drop in NDVI here signals real water stress in a region already at the dry-farming limit — there is no irrigation safety net. NDMI is the critical index for the Douro: a falling NDMI in July or August means the schist moisture reserve is depleted and the vines are shutting down photosynthesis. The terraced geometry creates strong aspect-dependent variation in satellite signals — south-facing terraces show stress earlier than north-facing ones — which satellite monitoring can resolve at 10 m resolution. SAVI is useful for correcting the bare-schist background reflectance between narrow vine rows.
## Key metrics
| Index | Peak range | Douro-specific signal |
|-------|-----------|----------------------|
| NDVI | 0.3–0.5 | Stress-farmed canopy — drops signal real trouble |
| NDMI | −0.3 to 0.1 | Schist moisture reserve — the defining index |
| NBR | Varies | Fire risk in the dry understory |
> **Free report:** Get a live satellite health analysis of the Douro Valley vineyards this month — see canopy stress, schist moisture status, and how the vintage is holding up for free, no signup. [Check the terraces →](/reports/douro-valley-port-vineyards)
---
# Huelva Strawberry Belt — Live Satellite Health Report
URL: https://www.agroreports.org/articles/huelva-strawberry-belt/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/huelva-strawberry-belt.md
> Satellite monitoring of Huelva's berry fields reveals winter crop health and water status under plastic tunnels — free, monthly.
## About this region
Huelva, on Spain's Atlantic coast near the Portuguese border, is Europe's strawberry capital. The sandy soils, mild winters, and abundant sunlight make it possible to produce berries through the winter months — the core export window runs from December to April, when most of Europe is frozen. The region also grows raspberries, blueberries, and blackberries under plastic tunnels and open fields, making it one of Europe's most intensive horticultural zones.
## Climate & growing cycle
Huelva's mild Mediterranean-Atlantic climate is driven by proximity to the ocean, which moderates winter temperatures and prevents frost. Strawberries are planted in late summer, begin producing in December, and reach peak production in February–March before the cycle resets in the summer heat. The winter production window is the reverse of most crops — peak NDVI occurs in January and February when the rest of Europe is bare.
## Satellite monitoring insights
Strawberries are grown under plastic mulch and tunnels, which complicates satellite monitoring — the plastic canopy reflects light differently from bare leaves, and the alternating strips of plastic and vegetation create a mixed pixel signal. NDVI runs high in the peak winter production months (0.5–0.7) and dips during the summer heat when the cycle resets. Water management is the central challenge: the sandy soils drain fast and the region has faced chronic aquifer overdraft, making NDMI (moisture) monitoring especially relevant. EVI is valuable here for penetrating the plastic-tunnel interference — it handles atmospheric and surface reflectance artefacts better than simple NDVI.
## Key metrics
| Index | Peak range | Management signal |
|-------|-----------|-------------------|
| NDVI | 0.5–0.7 (winter) | Peak production in Jan–Feb |
| NDMI | −0.1 to 0.2 | Water stress in fast-draining sandy soils |
| EVI | 0.3–0.5 | Handles plastic tunnel reflectance artefacts |
> **Free report:** Get a live satellite health analysis of Huelva's berry fields this month — see canopy health, water status, and winter production progress for free, no signup. [Check the fields →](/reports/huelva-strawberry-belt)
---
# Jaén Olive Groves — Live Satellite Health Report
URL: https://www.agroreports.org/articles/jaen-olive-sea/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/jaen-olive-sea.md
> Satellite monitoring of Jaén's sea of rain-fed olive groves — the world's largest — reveals drought stress and canopy health, free, monthly.
## About this region
Jaén, in Andalusia, produces more olive oil than any region on Earth. The landscape is an unbroken "sea of olives" — millions of mostly Picual trees stretching to every horizon, many centuries old, grown under rain-fed conditions on the rolling hills of southern Spain. This single province accounts for roughly half of Spanish olive oil output, making it one of the most concentrated agricultural landscapes on the planet.
## Climate & growing cycle
The climate is Mediterranean with brutal summers — temperatures regularly exceed 40 °C and rainfall is scarce (under 500 mm/year). Olives are drought-tolerant, but sustained heat and drought stress lower yield. Flowering falls in April–May; harvest runs from November to January. The canopy of a traditional olive grove is sparse and widely spaced — trees are planted 7–12 metres apart — and the sun-baked soil between them dominates the satellite pixel.
## Satellite monitoring insights
NDVI runs persistently low (0.2–0.4) on traditional Jaén groves, which is normal for this crop, not a sign of stress. The widely spaced trees and bare soil between them produce a mixed pixel signal where the soil background dominates — SAVI is essential here to correct for this soil interference. NDMI is the most important index: a falling NDMI in June or July signals accumulated drought stress before yield is impacted. Because the groves are rain-fed, NDMI trends in summer are a direct readout of how effectively the winter rains recharged the soil profile.
## Key metrics
| Index | Typical range | Rain-fed significance |
|-------|--------------|----------------------|
| NDVI | 0.2–0.4 | Low and sparse canopy — normal for traditional groves |
| NDMI | −0.2 to 0.1 | Rain-dependent moisture — falling = drought stress |
| SAVI | 0.1–0.3 | Soil-corrected for the bare inter-row space |
> **Free report:** Get a live satellite health analysis of Jaén's olive groves this month — see crop vigour, drought stress, and how the trees are coping for free, no signup. [Check the groves →](/reports/jaen-olive-sea)
---
# Jerez Sherry Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/jerez-sherry-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/jerez-sherry-vineyards.md
> Satellite monitoring of Jerez's albariza-soil vineyards tracks how Palomino Fino survives the Andalusian summer on stored winter rain — free, monthly.
## About this region
Jerez de la Frontera, in Andalusia, is the birthplace of sherry — the fortified wine made from Palomino Fino and aged in a solera system under a layer of flor yeast. The chalky albariza soils (white, moisture-retentive marl) are the region's defining terroir: they reflect sunlight onto the vines, moderating temperature, and store winter rainfall for the long, rainless growing season. Without the albariza's sponge-like properties, viticulture in this climate would be nearly impossible.
## Climate & growing cycle
The climate is hot Mediterranean with very dry summers and mild winters. Palomino Fino buds in March, flowers in May, and harvests in early September — one of the earliest harvests in Europe. The albariza acts as a soil reservoir, holding winter rain that the vines draw on through the dry season. The canopy is kept open and the vines are often bush-trained (en vaso), producing modest leaf area that matches the limited water supply.
## Satellite monitoring insights
NDVI peaks at 0.3–0.45 on Jerez vines — reflecting the dry, open viticulture of southern Spain. Water stress (NDMI) is the region's defining management challenge: the albariza can store enough winter rain for a normal season, but a drought winter followed by a hot summer can deplete the reserve before véraison, and satellite monitoring picks this up weeks before the vines show visible stress. The bright white albariza soil creates unusually high background reflectance, so SAVI is essential for accurate canopy vigour readings.
## Key metrics
| Index | Peak range | Albariza-specific signal |
|-------|-----------|-------------------------|
| NDVI | 0.3–0.45 | Open, bush-trained canopy |
| NDMI | −0.2 to 0.1 | Albariza moisture reserve status |
| SAVI | 0.2–0.35 | Corrects for the bright white soil background |
> **Free report:** Get a live satellite health analysis of Jerez sherry vineyards this month — see canopy health, albariza moisture status, and how the vines are coping for free, no signup. [Check the vines →](/reports/jerez-sherry-vineyards)
---
# La Rioja Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/la-rioja-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/la-rioja-vineyards.md
> Satellite monitoring of La Rioja's Tempranillo vineyards reveals canopy health and water stress through the Ebro valley season — free, monthly.
## About this region
La Rioja is Spain's most famous wine region, straddling the Ebro valley in the north. Tempranillo is the king grape, producing structured, age-worthy reds with cherry and leather character. The region splits into Rioja Alta (cooler, higher, Atlantic-influenced) and Rioja Alavesa (limestone soils, south-facing slopes) — together they account for over 400 million bottles a year, making this one of the world's highest-volume quality wine regions.
## Climate & growing cycle
The climate is continental with Atlantic influence: cool nights, hot days, modest rainfall. Iron-rich clay and limestone soils over a chalk base drain freely. Tempranillo buds in April, flowers in early June, and ripens slowly through the long, dry summer, with harvest from late September into October. Vineyards are typically bush-trained (en vaso) or on low wires, keeping the canopy open to maximise sun exposure and minimise disease pressure in the dry climate.
## Satellite monitoring insights
NDVI peaks around 0.4–0.5 on Rioja vines — lower than irrigated row crops, reflecting the open canopy and dry-farmed tradition. SAVI is particularly useful here because the bare, iron-rich clay soil between rows contributes strongly to the satellite pixel signal. NDMI tracks the water status of the chalk subsoil through the long dry summer, and a declining trend in August directly correlates with reduced grape weight and concentration at harvest. The Rioja Alta vs Rioja Alavesa distinction is visible from space: the higher-altitude Alta greens up later and stays greener through summer than the limestone-dominated Alavesa.
## Key metrics
| Index | Peak range | Rioja-specific signal |
|-------|-----------|----------------------|
| NDVI | 0.4–0.5 | Open, bush-trained canopy |
| NDMI | −0.1 to 0.1 | Subsoil moisture through the dry summer |
| SAVI | 0.2–0.4 | Corrects for bare iron-rich clay background |
> **Free report:** Get a live satellite health analysis of La Rioja vineyards this month — see canopy vigour, water status, and how this vintage is developing for free, no signup. [Check the vines →](/reports/la-rioja-vineyards)
---
# Marlborough Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/marlborough-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/marlborough-vineyards.md
> Satellite monitoring of Marlborough's Sauvignon Blanc vineyards reveals canopy health and phenology in the Southern Hemisphere season — free, monthly.
## About this region
Marlborough, at the top of New Zealand's South Island, is the Sauvignon Blanc capital of the world — producing the pungent, zesty style that redefined the grape in the 1980s. The flat Wairau Valley has deep alluvial soils, low fertility, and abundant sunshine, with cool nights preserving the acidity that defines the style. It is the largest wine region in New Zealand by far, and its Sauvignon Blanc is one of the most recognisable wine styles globally.
## Climate & growing cycle
The climate is maritime and cool, with a long growing season stretching from September budbreak to March–April harvest. The Southern Hemisphere season is the mirror image of Europe's: vines green up in September–October, peak in January–February, and are harvested in autumn (March–April). The vertically trained canopy is managed to maximise sunlight interception in a climate where every hour of sun counts.
## Satellite monitoring insights
NDVI peaks high (0.5–0.7) in Marlborough thanks to the dense, lush canopy and reliable rainfall. The long, sunny days of January produce the peak canopy signal, and NDVI holds steady through February before declining toward harvest. NDMI is typically stable due to consistent rainfall and the alluvial soils' water-holding capacity — water stress is less common here than in mediterranean or continental regions. Like Stellenbosch and Mendoza, Marlborough is an excellent Southern Hemisphere showcase: its seasonal NDVI curve mirrors European vineyards with a precise six-month offset, making it ideal for demonstrating phenology monitoring across hemispheres.
## Key metrics
| Index | Peak range | Southern Hemisphere signal |
|-------|-----------|---------------------------|
| NDVI | 0.5–0.7 | Peak in January–February (southern summer) |
| NDMI | 0.0–0.2 | Usually stable — maritime climate, alluvial soils |
| EVI | 0.3–0.5 | High biomass without NDVI saturation |
> **Free report:** Get a live satellite health analysis of Marlborough vineyards this month — see canopy development through the Southern Hemisphere season for free, no signup. [Check the vines →](/reports/marlborough-vineyards)
---
# Mendoza Vineyards (Luján de Cuyo) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/mendoza-lujan-de-cuyo/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/mendoza-lujan-de-cuyo.md
> Satellite monitoring of Mendoza's high-altitude Malbec vineyards tracks snowmelt-dependent water stress through the desert growing season — free, monthly.
## About this region
Luján de Cuyo, in Argentina's Mendoza province, is the spiritual home of Malbec — the grape that put Argentine wine on the world map. The vineyards sit at high altitude (900–1,100 m) in the rain shadow of the Andes, watered almost entirely by snowmelt irrigation channels dating back centuries. The combination of intense sunlight, cool desert nights, and pure Andean water produces deeply coloured, structured Malbecs that are recognisable in any tasting.
## Climate & growing cycle
The climate is desert-continental: very dry, hot days, cold nights, under 200 mm of annual rainfall. Without irrigation there would be no viticulture — the snowmelt from the Andes is an existential resource, and the winter snowpack determines each year's irrigation budget before the growing season even begins. The Southern Hemisphere season runs from September budbreak to March–April harvest, peaking in the intense January sun. The high-altitude, dry-farmed (snowmelt) canopy is kept modest to match the limited water supply.
## Satellite monitoring insights
NDVI peaks around 0.4–0.5 on Mendoza Malbec — the stress-farmed canopy does not produce the lush growth of maritime or irrigated regions. Water management is existential here: NDMI is the single most watched index, as the snowpack determines each year's irrigation budget and a falling NDMI in January means the allocation is running short. The high-altitude desert atmosphere produces exceptionally clear satellite imagery — atmospheric interference is minimal at this altitude and aridity, giving some of the cleanest NDVI signals in global viticulture.
## Key metrics
| Index | Peak range | High-desert signal |
|-------|-----------|-------------------|
| NDVI | 0.4–0.5 | Modest, stress-farmed canopy |
| NDMI | −0.2 to 0.0 | Snowmelt water status — the defining index |
| SAVI | 0.2–0.4 | Corrects for bare desert soil between rows |
> **Free report:** Get a live satellite health analysis of Mendoza Malbec vineyards this month — see canopy stress, snowmelt water status, and how the vintage is holding up for free, no signup. [Check the vines →](/reports/mendoza-lujan-de-cuyo)
---
# Mosel Valley Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/mosel-valley-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/mosel-valley-vineyards.md
> Satellite monitoring of the Mosel's near-vertical slate vineyards reveals how Riesling survives at the absolute northern limit of wine — free, monthly.
## About this region
The Mosel is one of the world's most dramatic viticultural landscapes — Riesling vines clinging to near-vertical slate slopes above a winding river, in Germany's coolest quality-wine region. The slate soils (blue, red, and grey Devonian) store the sun's heat by day and radiate it at night, ripening Riesling to an extraordinary balance of acidity and fruit at the absolute northern limit of viable winemaking. These are some of the steepest cultivated slopes on Earth, and every vineyard task is done by hand.
## Climate & growing cycle
The climate is marginal — every degree of slope aspect matters. Riesling buds late (mid-April), flowers in late June, and ripens slowly into October or November, often harvested selectively over multiple passes through the same vineyard. Yields are very low. The slate acts as a thermal battery, absorbing the weak northern sun during the day and releasing it at night, creating a microclimate that can be 2–3 °C warmer than the surrounding valley floor.
## Satellite monitoring insights
The canopy is small and vertical — NDVI runs low (0.3–0.45) and its trend is a direct readout of how the vintage is shaping up. A slow-climbing NDVI in June and July means a difficult, cool year; a strong, steady climb means a potentially great one. The steep slopes create strong aspect-dependent variation: south-facing vines green up faster and maintain higher NDVI throughout the season. The Mosel is one of the most challenging environments for satellite monitoring due to slope angle and shadow effects, but the narrow, regular rows are visible at 10 m resolution and the seasonal signal is clean. SAVI helps correct for the dark slate background reflectance between rows.
## Key metrics
| Index | Peak range | Marginal-climate signal |
|-------|-----------|------------------------|
| NDVI | 0.3–0.45 | Slope of the climb = vintage quality |
| NDMI | 0.0–0.2 | Slate moisture through the cool growing season |
| SAVI | 0.2–0.3 | Corrects for dark slate between narrow rows |
> **Free report:** Get a live satellite health analysis of Mosel Valley vineyards this month — see canopy development, slate heat capture, and how the vintage is tracking for free, no signup. [Check the slopes →](/reports/mosel-valley-vineyards)
---
# Napa Valley Vineyards (Rutherford) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/napa-valley-rutherford/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/napa-valley-rutherford.md
> Satellite monitoring of Napa's Rutherford bench reveals Cabernet canopy health, water stress, and fire risk through the California season — free, monthly.
## About this region
The Rutherford bench, on the Napa Valley floor, is one of California's most prestigious Cabernet Sauvignon terroirs — a fan of alluvial gravel washed down from the Mayacamas mountains. The gravelly soils drain freely, forcing roots deep and producing the structured, age-worthy Cabs that made Napa famous. Cabernets from this stretch fetch some of the highest prices in American wine, and the "Rutherford dust" character is one of the most discussed terroir signatures in the New World.
## Climate & growing cycle
Napa has a warm Mediterranean climate: dry sunny summers with large diurnal swings — cool nights from marine fog rolling in from San Pablo Bay. Most vineyards are drip-irrigated, though the best are dry-farmed once established. Cabernet Sauvignon buds in March–April, flowers in May, véraison in late July, and harvest falls in September–October. The canopy is dense and vertically shoot-positioned, maximising sun interception in the valley's long, bright days.
## Satellite monitoring insights
NDVI peaks high (0.5–0.7) at full canopy on Napa's Cabernet — the combination of warm sun, drip irrigation, and managed canopy creates a vigorous signal. Drought and wildfire are the region's defining threats: NDMI tracks irrigation performance and developing water stress during the dry summer, while NBR is a critical fire-risk index for the surrounding hills and vineyard margins. A falling NDMI in August signals inadequate irrigation during the final ripening push, directly affecting berry size and tannin maturity. The large diurnal temperature swing means Napa vines recover at night, producing a stable NDVI plateau through summer that is unusual in Mediterranean viticulture.
## Key metrics
| Index | Peak range | California-specific signal |
|-------|-----------|---------------------------|
| NDVI | 0.5–0.7 | Dense, irrigated canopy — stable through summer |
| NDMI | −0.1 to 0.2 | Irrigation performance during ripening |
| NBR | Varies | Fire risk in vineyard margins and hills |
> **Free report:** Get a live satellite health analysis of Napa Valley's Rutherford bench this month — see canopy health, water status, and fire risk for free, no signup. [Check the vines →](/reports/napa-valley-rutherford)
---
# Pfalz Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/pfalz-wine-route/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/pfalz-wine-route.md
> Satellite monitoring of the Pfalz wine route reveals how one of Germany's warmest regions ripens Riesling and red varieties — free, monthly.
## About this region
The Pfalz (Palatinate) is Germany's second-largest wine region, a sun-drenched strip along the Haardt mountains that forms the natural continuation of the French Vosges. The region is warm enough to ripen red varieties (Dornfelder, Spätburgunder) alongside Riesling, Müller-Thurgau, and an increasing variety of international grapes. The Deutsche Weinstraße (German Wine Route) runs through it, and the climate here challenges the stereotype of German wine as exclusively cool-climate.
## Climate & growing cycle
The climate is one of Germany's warmest and driest — the Haardt mountains create a rain shadow similar to the Vosges in Alsace, giving the Pfalz more sunshine hours than any other German region. Soils are remarkably diverse: loess, sandstone, basalt, and shell-limestone change across the landscape. Riesling buds in mid-April, flowers in mid-June, and harvests in October. The canopy is denser than the Mosel's because the gentler slopes and warmer climate support more vigorous growth.
## Satellite monitoring insights
NDVI peaks around 0.4–0.55 on Pfalz vines — higher than the Mosel's slope vineyards because the terrain is gentler and the climate warmer, supporting a denser canopy. The Pfalz is a good case study for the northern limit of warmer-climate viticulture: its NDVI trends higher than most German regions, but still well below Mediterranean vineyards. NDMI is useful for tracking water stress during dry spells, though the loess soils hold moisture well. The diverse soil map means SAVI variations across a Pfalz vineyard often correspond to soil-type boundaries, offering a way to map terroir from space.
## Key metrics
| Index | Peak range | Pfalz signal |
|-------|-----------|-------------|
| NDVI | 0.4–0.55 | Warmer = higher than Mosel, cooler than Mediterranean |
| NDMI | 0.0–0.2 | Loess moisture through dry spells |
| SAVI | 0.2–0.4 | Soil-type variation across the diverse geology |
> **Free report:** Get a live satellite health analysis of Pfalz vineyards this month — see canopy vigour, moisture status, and how this vintage is shaping up for free, no signup. [Check the vines →](/reports/pfalz-wine-route)
---
# Priorat Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/priorat-slate-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/priorat-slate-vineyards.md
> Satellite monitoring of Priorat's slate-terraced vineyards reveals how old-vine Garnacha survives the extreme Catalan heat — free, monthly.
## About this region
Priorat is a dramatic, mountainous wine region in Catalonia — only the second in Spain to receive the prestigious DOCa status after Rioja. The defining feature is its *llicorella* (black slate) soils, which crack and force old Garnacha and Cariñena vines to drive roots 25 metres deep in search of water. Yields are among the lowest in the world, producing intensely concentrated, age-worthy reds from vines that sometimes exceed a century in age.
## Climate & growing cycle
The climate is harsh continental with Mediterranean influence: very hot, dry summers and cold winters, with steep terraced slopes making every vineyard operation labour-intensive. The slate terraces absorb the Catalan sun all day and radiate heat through the night, accelerating ripening in an environment that already pushes vines to their physiological limit. Most vineyards are dry-farmed — the vines survive on what their deep roots can find in the fractured slate.
## Satellite monitoring insights
The steep terrain makes Priorat one of the most visually distinctive vineyard landscapes on satellite imagery — the regular patterns of hand-built terraces are unmistakable at 10 m resolution. The stress-farmed canopy stays low and sparse: NDVI typically peaks at 0.3–0.45, and its trend is a direct readout of how the vintage is developing under extreme conditions. NDMI is the critical index — the llicorella slate holds almost no water, so a declining NDMI signals that even deep-rooted old vines are running out. SAVI is essential for correcting the dark slate background that dominates the satellite pixel in this sparse-canopy environment.
## Key metrics
| Index | Peak range | Extreme-terroir signal |
|-------|-----------|----------------------|
| NDVI | 0.3–0.45 | Sparse canopy — every point matters |
| NDMI | −0.3 to 0.0 | Slate moisture — the survival index |
| SAVI | 0.15–0.3 | Corrects for dark llicorella background |
> **Free report:** Get a live satellite health analysis of Priorat's slate vineyards this month — see canopy stress, slate moisture, and how these old vines are coping for free, no signup. [Check the terraces →](/reports/priorat-slate-vineyards)
---
# Prosecco Hills (Valdobbiadene) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/prosecco-hills-valdobbiadene/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/prosecco-hills-valdobbiadene.md
> Satellite monitoring of Valdobbiadene's UNESCO-listed Prosecco hills reveals canopy health across the steep terraced landscape — free, monthly.
## About this region
The Prosecco hills of Conegliano and Valdobbiadene, in the Veneto, are a UNESCO World Heritage site — a steep, hand-terraced landscape where the Glera grape produces the sparkling wine that outsells champagne worldwide by volume. The Cartizze cru, a tiny 108-hectare hillside, is considered the finest expression of Prosecco, producing wines of remarkable concentration from one of viticulture's most unlikely mass-market grapes.
## Climate & growing cycle
The climate is mild and humid, with the Alps to the north protecting the vines from cold continental air and the Adriatic to the south moderating temperatures. Steep slopes mean mechanisation is nearly impossible — harvesting is still done entirely by hand. Glera buds in early April, flowers in early June, and harvests from mid-September. The canopy is lush and vertically trained on steep hillsides, with some of the highest leaf area per vine in Old World viticulture.
## Satellite monitoring insights
NDVI runs higher than most Old World vineyards (0.5–0.6 at peak) because the Glera canopy is substantially denser than that of dry-farmed Mediterranean vines. The steep terrain creates strong aspect-dependent variation visible from space: south-facing slopes green up faster in spring and show stress earlier in dry August than north-facing ones. NDMI tracks the humidity-driven disease pressure that is the region's primary management challenge — excessive moisture in the dense canopy creates conditions for downy mildew. The regular, tight terrace pattern is one of the most recognisable agricultural signatures in European satellite imagery.
## Key metrics
| Index | Peak range | Prosecco-specific signal |
|-------|-----------|-------------------------|
| NDVI | 0.5–0.6 | Dense, lush canopy on steep terraces |
| NDMI | 0.1–0.3 | High — humidity drives disease pressure |
| EVI | 0.3–0.5 | Handles high-biomass saturation on dense canopy |
> **Free report:** Get a live satellite health analysis of Valdobbiadene's Prosecco hills this month — see canopy density, aspect-dependent variation, and how the vintage is developing for free, no signup. [Check the hills →](/reports/prosecco-hills-valdobbiadene)
---
# Puglia Olive Plains — Live Satellite Health Report
URL: https://www.agroreports.org/articles/puglia-olive-plains/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/puglia-olive-plains.md
> Satellite monitoring of Puglia's centuries-old olive groves reveals the slow decline or stability of a cultural landscape under threat — free, monthly.
## About this region
Puglia — the heel of Italy's boot — is home to millions of olive trees, many of them centuries old, and is Italy's largest olive oil producing region. The Tavoliere plain and the surrounding rolling country are planted to Coratina and Ogliarola varieties, producing robust, peppery oils that form the backbone of Italian olive oil production. Some trees in this region are over a thousand years old, making these groves a cultural-heritage landscape as much as an agricultural one.
## Climate & growing cycle
The climate is hot Mediterranean with very dry summers and mild winters. The deep clay-limestone soils retain what little moisture falls. Olives flower in April–May and harvest from November to January. The traditional trees are widely spaced and goblet-pruned, producing a sparse, open canopy that limits water loss — a millenia-old adaptation to the region's chronic summer drought.
## Satellite monitoring insights
Puglia has been at the front line of *Xylella fastidiosa*, a bacterial disease that has killed millions of olive trees since 2013 — the largest agricultural biological disaster in modern European history. Monitoring the canopy here is not just agronomy; it is watching a cultural-heritage landscape under existential threat. NDVI runs low (0.2–0.4) for healthy traditional groves — this is normal for the open, widely spaced canopy. A persistent, multi-year decline in NDVI may signal disease, drought, or both, and the spatial pattern of decline across the grove can indicate whether the cause is pathogen-driven (patchy spread) or drought-driven (uniform stress). SAVI is essential for correcting the bare-soil signal between widely spaced trees.
## Key metrics
| Index | Typical range | Disease-monitoring signal |
|-------|--------------|--------------------------|
| NDVI | 0.2–0.4 | Persistent decline = possible Xylella or drought |
| NDMI | −0.3 to 0.1 | Chronic summer water stress |
| SAVI | 0.1–0.3 | Corrects for bare soil between wide-spaced trees |
> **Free report:** Get a live satellite health analysis of Puglia's ancient olive groves this month — see canopy stability, water stress, and whether the trees are holding on for free, no signup. [Check the groves →](/reports/puglia-olive-plains)
---
# Rheingau Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/rheingau-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/rheingau-vineyards.md
> Satellite monitoring of the Rheingau's south-facing Rhine slopes reveals how Riesling exploits the river's heat mirror — free, monthly.
## About this region
The Rheingau is a narrow strip of south-facing slopes along the Rhine between Wiesbaden and Rüdesheim — one of Germany's oldest wine regions and the spiritual home of Riesling alongside the Mosel. The Taunus hills protect the vineyards from cold northern winds, and the Rhine acts as a heat mirror, reflecting sunlight back onto the slopes. The deep loess and clay soils over slate store water well, buffering the vines through the region's occasional dry spells.
## Climate & growing cycle
Riesling dominates, with smaller plantings of Spätburgunder (Pinot Noir) on the warmest sites. The climate is marginal continental, substantially warmed by the river: budbreak is late April, flowering in late June, and harvest in October. The south-facing aspect is the region's defining advantage — the angle of the slopes relative to the Rhine maximises both direct solar radiation and the reflected heat from the water surface below.
## Satellite monitoring insights
NDVI follows a tight seasonal curve on the Rheingau: slow spring development, peak around 0.4–0.5 at full canopy in August, and a sharp drop after the October harvest. The south-facing aspect produces one of the cleanest seasonal NDVI signals in German viticulture — shadow effects are minimal compared to the steeper Mosel, and the uniform slope orientation means within-vineyard variation is lower. The Rheingau is an ideal showcase for satellite phenology monitoring, because the seasonal curve is so regular and the aspect so consistent that even small deviations from the expected pattern are meaningful.
## Key metrics
| Index | Peak range | Rheingau signal |
|-------|-----------|----------------|
| NDVI | 0.4–0.5 | Clean seasonal bell curve — aspect-driven uniformity |
| NDMI | 0.0–0.2 | Loess/clay moisture through dry spells |
| NDRE | 0.2–0.35 | Riesling chlorophyll at veraison |
> **Free report:** Get a live satellite health analysis of Rheingau vineyards this month — see the seasonal canopy curve, moisture status, and how the vintage is tracking for free, no signup. [Check the slopes →](/reports/rheingau-vineyards)
---
# Ribera del Duero Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/ribera-del-duero/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/ribera-del-duero.md
> Satellite monitoring of Ribera del Duero's high-meseta Tinto Fino reveals how the vines survive extreme diurnal swings — free, monthly.
## About this region
Ribera del Duero follows the Duero river across the high meseta of Castilla y León, at 700–1,000 m elevation — one of the highest quality-wine regions in Europe. The extreme diurnal range — hot days, cold nights — and the region's Tinto Fino (a local clone of Tempranillo adapted to altitude) produce some of Spain's deepest, most structured red wines, from Vega Sicilia to countless smaller producers punching above their weight.
## Climate & growing cycle
The climate is harsh continental: short growing season, late spring frost risk, summer heat spikes above 38 °C, and less than 450 mm of annual rainfall. Soils are alternating layers of sand, limestone, marl, and chalk. The vines struggle deliberately — low yields, concentrated fruit, small berries with thick skins. Harvest runs from late September to mid-October. The high-altitude, dry-farmed philosophy means every vine is deliberately kept small and stressed to maximise concentration.
## Satellite monitoring insights
The high altitude and dry farming keep canopies small: NDVI typically peaks at 0.35–0.5, reflecting the stress-farming philosophy that defines the region. Unlike irrigated wine regions, Ribera's NDVI curve is entirely rain-dependent — a dry spring produces a lower peak, and a hot July produces an earlier decline. NDMI is the most important index here, tracking how the scarce rainfall and the water stored in the marl and chalk layers are sustaining the vines through the hot, dry summer. The high meseta's exceptionally clear atmosphere produces clean satellite signals, and the flat terrain means aspect variation is minimal — the NDVI values you see represent the vine, not terrain shadow.
## Key metrics
| Index | Peak range | High-altitude signal |
|-------|-----------|---------------------|
| NDVI | 0.35–0.5 | Altitude-stressed, rain-dependent canopy |
| NDMI | −0.2 to 0.1 | Marl/chalk moisture through the dry summer |
| SAVI | 0.2–0.35 | Corrects for bare sand/limestone background |
> **Free report:** Get a live satellite health analysis of Ribera del Duero this month — see canopy stress, rainfall-dependent vigour, and how the vintage is tracking for free, no signup. [Check the vines →](/reports/ribera-del-duero)
---
# Sancerre Vineyards (Loire Valley) — Live Satellite Health Report
URL: https://www.agroreports.org/articles/loire-valley-sancerre/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/loire-valley-sancerre.md
> Satellite monitoring of Sancerre's Kimmeridgian marl vineyards reveals canopy health and the cool-climate tension that defines the vintage — free, monthly.
## About this region
Sancerre, on the upper Loire, produces some of the world's most distinctive Sauvignon Blanc — crisp, mineral, and flint-driven — from the chalky Kimmeridgian marl soils that also underpin Chablis. The region also produces lighter reds and rosés from Pinot Noir. The landscape is rolling hills above the river, with southeast-facing slopes catching morning sun in a region where every degree of aspect and every hour of sunlight matters for ripening.
## Climate & growing cycle
The climate is cool continental at the northern limit of Sauvignon Blanc ripening. Spring frost is a chronic risk — the Loire's position means cold air pools in the valley, threatening buds well into April. Budbreak falls in mid-April, flowering in mid-June, and harvest in late September to early October. The canopy is small and carefully managed for airflow and disease control — mildew is a constant threat in wet years, and an open canopy is the first line of defence.
## Satellite monitoring insights
NDVI peaks modestly at 0.35–0.5 on Sancerre vines — reflecting the cool climate and open canopy style. The index's trend is a direct readout of whether the vintage is shaping up well or running into trouble: a steady climb through June and July signals a smooth development, while a stalled or declining NDVI points to frost damage, mildew pressure, or water stress. NDMI is essential for tracking moisture in the Kimmeridgian marl, which can hold water well but drains differently on slopes depending on marl depth. NDRE tracks chlorophyll at veraison, correlating with the aromatic compound development that defines Sancerre's character.
## Key metrics
| Index | Peak range | Vintage signal |
|-------|-----------|---------------|
| NDVI | 0.35–0.5 | Canopy trajectory — smooth climb = good; stall = trouble |
| NDMI | 0.0–0.2 | Marl moisture through the dry August weeks |
| NDRE | 0.2–0.35 | Chlorophyll at veraison = aromatic potential |
> **Free report:** Get a live satellite health analysis of Sancerre vineyards this month — see canopy development, moisture status, and how the vintage is tracking for free, no signup. [Check the vines →](/reports/loire-valley-sancerre)
---
# Stellenbosch Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/stellenbosch-vineyards/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/stellenbosch-vineyards.md
> Satellite monitoring of Stellenbosch's mountain-ringed vineyards reveals phenology inverted from the Northern Hemisphere — free, monthly.
## About this region
Stellenbosch is South Africa's premier wine region, nestled between mountains northeast of Cape Town. The diverse soils — granite, shale, sandstone — and varied aspects produce a wide range of styles, from Bordeaux blends and Pinotage (South Africa's signature cross) to Chenin Blanc and Syrah. It is one of the most beautiful wine landscapes in the world, with Table Mountain visible on the horizon and ancient granite domes framing the vineyards.
## Climate & growing cycle
The climate is Mediterranean: warm dry summers, cool wet winters, with strong cooling winds from False Bay moderating the afternoon heat. The Southern Hemisphere season is inverted relative to Europe — budbreak in September, flowering in November, véraison in January, and harvest from February to April. This means the peak canopy period falls in December–February, the southern summer, and the vines are bare during the European growing season.
## Satellite monitoring insights
NDVI peaks in the Southern Hemisphere summer (December–February), creating a seasonal curve that mirrors Northern Hemisphere vineyards with a precise six-month offset. This inverted season makes Stellenbosch an excellent showcase for demonstrating phenology monitoring: its NDVI climbs through October–December, holds through January, and declines toward the February–April harvest. NDMI is typically stable through the dry summer due to the deep granite-derived soils that hold winter rainfall well. The cooling maritime winds create a distinctive diurnal pattern that moderates stress, producing a flatter NDVI plateau through summer than inland regions.
## Key metrics
| Index | Peak period | Southern Hemisphere signal |
|-------|------------|---------------------------|
| NDVI | Dec–Feb | Six-month offset from Europe |
| NDMI | 0.0–0.2 | Deep granite soils buffer water stress |
| EVI | 0.3–0.5 | High biomass without saturation |
> **Free report:** Get a live satellite health analysis of Stellenbosch vineyards this month — see the Southern Hemisphere canopy curve and how the vintage is shaping up for free, no signup. [Check the vines →](/reports/stellenbosch-vineyards)
---
# Valencia Orange Groves — Live Satellite Health Report
URL: https://www.agroreports.org/articles/valencia-orange-groves/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/valencia-orange-groves.md
> Satellite monitoring of Valencia's historic citrus groves reveals the subtle signals of an evergreen crop through the seasons — free, monthly.
## About this region
The Valencian *huerta* is the historic citrus belt of Spain — the coastal plains and alluvial fans around the Turia river where oranges, mandarins, and lemons have been cultivated for centuries. The mild Mediterranean climate, fertile alluvial soils, and traditional irrigation channels inherited from Moorish agriculture make this one of Europe's classic citrus regions. The landscape of orderly rows of dark green trees against the pale soil is among Spain's most recognisable agricultural vistas.
## Climate & growing cycle
Citrus is evergreen — the canopy does not collapse to bare soil each winter the way row crops do. This is the defining difference between monitoring citrus and monitoring annual crops. Flowering falls in April–May and blankets the region in the fragrance of orange blossom. The main harvest for oranges runs from winter through spring, with fruit often held on the tree for months after ripening — the tree is simultaneously carrying this year's fruit and next year's flowers.
## Satellite monitoring insights
NDVI stays relatively stable year-round on citrus (0.4–0.6), with stress showing as gradual dips rather than dramatic seasonal collapse. Monitoring citrus is fundamentally different from monitoring row crops: the goal is to detect slow, structural changes — chlorophyll loss (NDRE), water stress (NDMI), and canopy thinning — rather than tracking a seasonal bell curve. A falling NDRE over months signals declining tree health that may precede visible symptoms. NDMI is critical for irrigation management in a region where water allocation is politically charged and every cubic metre counts. Because the canopy is permanent, multi-year NDVI comparisons are the most powerful diagnostic — a grove that was 0.55 three years ago and is 0.45 today is in measurable decline, even if both values are within "normal" range.
## Key metrics
| Index | Typical range | Evergreen signal |
|-------|--------------|-----------------|
| NDVI | 0.4–0.6 (year-round) | Stable — dips signal stress, not seasonality |
| NDRE | 0.3–0.5 | Chlorophyll decline = slow health loss |
| NDMI | −0.1 to 0.2 | Irrigation adequacy in a water-constrained region |
> **Free report:** Get a live satellite health analysis of Valencia's orange groves this month — see chlorophyll levels, water stress, and canopy stability for free, no signup. [Check the groves →](/reports/valencia-orange-groves)
---
# Willamette Valley Vineyards — Live Satellite Health Report
URL: https://www.agroreports.org/articles/willamette-valley-pinot-noir/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/willamette-valley-pinot-noir.md
> Satellite monitoring of Oregon's Willamette Valley reveals how Pinot Noir handles the cool maritime climate — free, monthly.
## About this region
The Willamette Valley, southwest of Portland in Oregon, is one of the New World's premier Pinot Noir regions. The cool climate and volcanic-basaltic soils of the valley's rolling hills produce elegant, Burgundian-style Pinots that rival the best of the Old World — so much so that the region is sometimes called "Oregon's Burgundy." The combination of latitude (45° N, the same as Bordeaux and Burgundy), maritime influence, and distinctive volcanic soils gives Willamette Pinot its characteristic tension between ripe fruit and earthy savouriness.
## Climate & growing cycle
The climate is maritime, heavily influenced by cool air funnelling through a gap in the Coast Range from the Pacific. Summers are dry and warm; the risk is too much cloud and too little heat — not drought. Budbreak falls in early April, flowering in mid-June, and harvest in late September to October. The canopy is lush and vertically trained to capture every available hour of sunlight in a region where heat accumulation is the limiting factor.
## Satellite monitoring insights
NDVI peaks high (0.5–0.7) on Willamette Pinot Noir — the dense, well-watered canopy reflects the region's reliable moisture and long daylight hours at this latitude. Mildew pressure from cool, humid conditions is the primary threat rather than drought: monitoring NDRE (chlorophyll) and NDMI (canopy moisture) helps time protective sprays before infection establishes. NDRE is particularly valuable here because chlorophyll levels correlate with the vine's nitrogen status and leaf health, both of which affect disease susceptibility. The high NDVI baseline means small declines can signal early mildew damage before it's visible at ground level.
## Key metrics
| Index | Peak range | Cool-maritime signal |
|-------|-----------|---------------------|
| NDVI | 0.5–0.7 | Lush, dense canopy — moisture-plentiful environment |
| NDRE | 0.3–0.5 | Chlorophyll = nitrogen status and disease susceptibility |
| NDMI | 0.1–0.3 | High — risk is excess moisture (mildew), not drought |
> **Free report:** Get a live satellite health analysis of Willamette Valley vineyards this month — see canopy density, chlorophyll status, and early-warning disease signals for free, no signup. [Check the vines →](/reports/willamette-valley-pinot-noir)
---
# Aude Corbières Fire 2025 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/aude-corbieres-fire-2025/
Updated: 2026-07-10
Markdown mirror: https://www.agroreports.org/articles/aude-corbieres-fire-2025.md
> France's worst Mediterranean wildfire in 50 years burned 11,133 hectares across the Corbières massif in Aude. 1 dead, 36 homes destroyed, 16 communes affected. Satellite dNBR burn severity analysis.
## About this fire
The Aude Corbières megafire was France's worst Mediterranean wildfire in over 50 years. Ignited on 5 August 2025 near the commune of Ribaute in the Corbières massif, it raged for 25 days across the garrigue and pine forests of the Aude department in Occitanie. By the time it was officially declared extinguished on 28 August 2025, the fire had destroyed **11,133 hectares** of vegetation across an envelope of 17,000 hectares, affecting 16 communes.
The fire claimed one life — a 65-year-old woman in Saint-Laurent-de-la-Cabrerisse — and injured 9 people. Thirty-six homes were destroyed. Up to 2,000 firefighters were mobilized at the peak of the blaze, supported by Canadair water-bombing aircraft and European reinforcements. The Corbières is a wine-growing region, and the fire directly threatened Languedoc AOP vineyards. While firefighters managed to save many estates, the fire scorched vineyard edges and raised smoke-taint concerns for grapes harvested weeks later.
The Aude prosecutor's office opened a criminal investigation, with the fire's origin suspected to be arson. The disaster underscored the vulnerability of France's Mediterranean massifs as climate change drives longer droughts and more extreme fire weather into regions previously considered temperate.
## Timeline & severity
| Date | Event |
|------|-------|
| **5 Aug 2025** | Fire ignites near Ribaute during extreme heat and drought |
| **5–10 Aug 2025** | Rapid spread through garrigue, pine forest, and vineyards; 16 communes affected |
| **10 Aug 2025** | Fire declared "maîtrisé" but remains under surveillance |
| **28 Aug 2025** | Officially extinguished after 25 days, aided by late-August rains |
| **Post-fire** | 11,133 ha destroyed; 1 death; 36 homes destroyed; criminal investigation opened |
The fire's longevity — nearly four weeks from ignition to full extinction — was driven by persistent hot, dry conditions and the rugged terrain of the Corbières, which hampered ground access. Residual hotspots smoldered for weeks after containment, requiring continuous aerial monitoring.
## Satellite analysis
Sentinel-2 satellite imagery reveals the fire's impact and the region's recovery trajectory. The **dNBR (differenced Normalized Burn Ratio)** compares pre-fire and post-fire NBR values to quantify burn severity.
### Burn severity (dNBR)
| Metric | Value | Image date |
|--------|-------|------------|
| Pre-fire NBR | 0.196 | 28 Jun 2025 |
| Post-fire NBR | 0.164 | 3 Sep 2025 |
| **dNBR** | **0.032** | — |
| USGS severity class | Unburned (area average) | — |
The area-average dNBR of 0.032 falls below the USGS "unburned" threshold (0.1). This does not mean the fire caused no damage — rather, it reflects the naturally low biomass of Corbières garrigue (pre-fire NBR ~0.20, vs. 0.5+ for dense forest) and the fact that the bounding box includes unburned patches between fire fronts. Pixel-level dNBR would show moderate-to-high severity in the burned cores.
### Vegetation timeline
| Period | NDVI | NBR | NDMI | Context |
|--------|------|-----|------|---------|
| Jan 2024 | 0.325 | 0.191 | 0.075 | Winter baseline |
| Jul 2024 | 0.310 | 0.164 | 0.024 | Summer drought — NDMI near zero |
| Jan 2025 | 0.257 | 0.143 | 0.058 | Pre-fire winter low |
| **Jul 2025** | **0.346** | **0.227** | **0.071** | **8 days before fire — peak biomass** |
| 5 Aug 2025 | — | — | — | **Fire ignites** |
| Sep 2025 | — | 0.164 | — | Post-fire image — NBR dropped 28% |
| Jan 2026 | 0.342 | 0.210 | 0.094 | 5 months post-fire — NBR at 93% of pre-fire |
| **Jul 2026** | **0.339** | **0.213** | **0.056** | **11 months post-fire — near full recovery** |
### Key findings
The NBR trajectory tells a clear story: pre-fire NBR peaked at 0.227 in July 2025 (healthy garrigue at summer peak), then dropped to 0.164 after the fire — a **28% decline** in vegetation cover. By January 2026, NBR had recovered to 0.210 (93% of pre-fire), and by July 2026 it reached 0.213 — **94% of pre-fire levels**. This rapid recovery is characteristic of fire-adapted garrigue vegetation, which resprouts from root crowns within months.
However, the current **NDMI of 0.056** sits in the "transitional, increasing fire risk" zone (−0.1 to 0.1). Combined with 37% of pixels showing stressed NDVI values (<0.3), the Corbières faces elevated fire risk for the 2026 season — the vegetation has regrown, but it is dry.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
Post-fire recovery in the Corbières will be uneven. Garrigue vegetation, adapted to fire, may resprout within months. Pine forests will take decades to regenerate, and the loss of mature trees increases erosion risk on the steep Corbières slopes before winter rains. For vineyard owners, the priority is assessing vine damage and smoke taint — satellite monitoring of NDVI and NBR over the coming seasons will help distinguish vines that recover from those needing replanting. The criminal investigation and the broader policy debate about fire prevention in France's Mediterranean massifs will shape land management in the Corbières for years to come.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Castelnau-de-Guers Fire 2025 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/castelnau-de-guers-fire-2025/
Updated: 2026-07-10
Markdown mirror: https://www.agroreports.org/articles/castelnau-de-guers-fire-2025.md
> A wildfire burned 700 hectares near Castelnau-de-Guers in Hérault, threatening Languedoc vineyards. 750 firefighters deployed, 220 caravans saved. Satellite dNBR burn severity analysis.
## About this fire
On 8 July 2025, a violent wildfire broke out in Castelnau-de-Guers, a wine-growing commune in the Hérault department of Occitanie. The fire spread rapidly across **700 hectares** — 400 of them severely burned — advancing 3.5 kilometers through garrigue and vineyard edges in the Languedoc wine country. The blaze was one of nine simultaneous fire starts in the Hérault that day, eight of which were stopped at an early stage by rapid-response firefighting teams.
The fire directly threatened the **Languedoc AOP vineyards** that define the region's economy and identity. While firefighters managed to save 220 caravans and eight agricultural estates (mas) in the fire's path, initial field assessments indicated significant damage to vineyards exposed to the flames. The A9 motorway was briefly threatened before traffic was restored as the situation stabilized.
At peak deployment, **750 firefighters** were engaged, with 430 still on scene at 21:30. Six Canadair water-bombers provided continuous aerial support until nightfall. No homes were destroyed and no casualties were reported — a testament to the speed and coordination of the response. The Hérault prefect publicly commended the firefighters, both professional and volunteer, for their work under extreme conditions.
## Timeline & severity
| Date | Event |
|------|-------|
| **8 Jul 2025 (afternoon)** | Fire ignites at Castelnau-de-Guers; 9 simultaneous starts in Hérault |
| **8 Jul 2025 (evening)** | 700 ha burned (400 severely); A9 motorway threatened then reopened |
| **8 Jul 2025 (night)** | 430 firefighters on scene; 6 Canadairs active until nightfall |
| **9 Jul 2025** | Fire stabilized; vineyard damage assessment begins |
| **Post-fire** | 700 ha burned; 220 caravans saved; 8 estates protected; vineyard damage reported |
The fire's spread through vineyard edges is particularly significant for the Languedoc wine industry. Even where vines were not directly burned, heat exposure can damage vine physiology and smoke can taint ripening grapes — financial losses that extend well beyond the visible fire perimeter.
## Satellite analysis
Sentinel-2 satellite imagery provides the clearest picture of fire impact and recovery among the four July 2025 fires analyzed.
### Burn severity (dNBR)
| Metric | Value | Image date |
|--------|-------|------------|
| Pre-fire NBR | 0.185 | 10 Jun 2025 |
| Post-fire NBR | 0.125 | 3 Sep 2025 |
| **dNBR** | **0.060** | — |
| USGS severity class | Unburned (area average) | — |
The dNBR of 0.060 is the highest among the four July–August 2025 French fires analyzed, approaching the USGS "low severity" threshold (0.1). The NBR dropped **32%** from 0.185 to 0.125 — a significant vegetation loss. While the area-average classification remains "unburned" (due to the garrigue's naturally low biomass and unburned patches within the bounding box), the NBR decline is substantial enough to confirm real fire damage at the vegetation level.
### Vegetation timeline
| Period | NDVI | NBR | NDMI | Context |
|--------|------|-----|------|---------|
| Jan 2024 | 0.300 | 0.170 | 0.069 | Winter baseline |
| Jul 2024 | 0.298 | 0.146 | 0.019 | Summer drought — NDMI near zero |
| Dec 2024 | 0.295 | 0.169 | 0.078 | Pre-fire winter |
| **Jun 2025** | — | **0.185** | — | **Pre-fire image — healthy vineyard/garrigue** |
| 8 Jul 2025 | — | — | — | **Fire ignites** |
| Sep 2025 | — | 0.125 | — | Post-fire image — **NBR dropped 32%** |
| **Jul 2026** | **0.304** | **0.187** | **0.050** | **1 year post-fire — NBR fully recovered** |
### Key findings
The Castelnau-de-Guers data shows the clearest fire-and-recovery trajectory of all four fires. Pre-fire NBR (0.185) dropped to 0.125 after the fire — a **32% decline** indicating significant vegetation damage. One year later, NBR has recovered to **0.187 — slightly above the pre-fire value**, indicating full vegetation regrowth. This rapid recovery is consistent with garrigue scrubland, which is adapted to fire and resprouts vigorously.
However, current NDMI (0.050) is in the "transitional, increasing fire risk" zone, and the NDVI (0.304) remains moderate rather than high — the regrowth consists of pioneer garrigue species, not the vineyard vegetation that was damaged. For wine growers, this means the natural vegetation has recovered, but vineyard parcels that burned may still need replanting. Satellite monitoring of NDVI at the parcel level over the coming seasons will distinguish recovering vines from those requiring replacement.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
The Castelnau-de-Guers fire burned through a mosaic of garrigue and vineyards. Garrigue vegetation is fire-adapted and expected to regrow within 1–2 years, but the loss of ground cover creates erosion risk on the sloped terrain before autumn rains. For vineyard owners, the recovery timeline depends on burn severity: lightly scorched vines may resume normal growth the following season, while severely burned vines require replanting — a 3–5 year setback before the first harvest. Satellite monitoring of NDVI regrowth over the coming months will help growers identify which vines are recovering and which need replacement, and will track garrigue regeneration to inform future fuel management in the Languedoc wine corridor.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Marseille Area Fire 2025 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/marseille-fire-2025/
Updated: 2026-07-10
Markdown mirror: https://www.agroreports.org/articles/marseille-fire-2025.md
> A wildfire burned 750 hectares near Marseille in July 2025, forcing airport closure, highway shutdowns, and mass evacuations. 9 firefighters injured. Satellite dNBR burn severity analysis.
## About this fire
On the morning of 8 July 2025, a violent wildfire erupted in Pennes-Mirabeau and spread rapidly toward France's second-largest city, Marseille. In just hours, the fire consumed **750 hectares** of pine forest and garrigue on the northern outskirts of the Marseille metropolitan area. France's Interior Minister Bruno Retailleau traveled to the site that evening as the situation remained under high surveillance.
The fire's proximity to a major urban center caused immediate and severe disruption. **Marseille-Provence Airport** — France's 5th-busiest — was shut down at midday, with 70 flights cancelled and aircraft diverted to Nice, Montpellier, and Lyon. The A55 and A50 motorways were partially closed, and TGV and TER train service to Marseille's Saint-Charles station was suspended except for the Toulon direction. Within the city, northern neighborhoods including Saint-Antoine, l'Estaque, Saint-Henri, and Septèmes-les-Vallons were evacuated as smoke engulfed the city and air quality deteriorated sharply.
Over **720 firefighters** and marines-pompiers were mobilized, with reinforcements from neighboring departments. Nine firefighters were injured and more than a dozen homes were damaged. The event demonstrated how a wildfire on the wildland-urban interface can paralyze a major European city within hours.
## Timeline & severity
| Date | Event |
|------|-------|
| **8 Jul 2025 (morning)** | Fire ignites in Pennes-Mirabeau, spreading toward Marseille |
| **8 Jul 2025 (midday)** | Marseille-Provence Airport closed; 70 flights cancelled |
| **8 Jul 2025 (afternoon)** | 750 ha burned; A55/A50 motorways shut; northern Marseille evacuated |
| **8–9 Jul 2025** | 720 firefighters deployed; fire brought under control overnight |
| **Post-fire** | 750 ha burned; 9 firefighters injured; 12+ homes damaged |
The fire's spread rate — 750 hectares in a single morning — was driven by strong Mistral winds, temperatures above 35°C, and decades of fuel accumulation in the pine forests north of Marseille. The urban-wildland interface made evacuation and infrastructure protection extraordinarily complex.
## Satellite analysis
Sentinel-2 satellite imagery captures the fire's impact on the Pennes-Mirabeau wildland-urban interface.
### Burn severity (dNBR)
| Metric | Value | Image date |
|--------|-------|------------|
| Pre-fire NBR | 0.018 | 29 Jun 2025 |
| Post-fire NBR | 0.021 | 8 Aug 2025 |
| **dNBR** | **−0.003** | — |
| USGS severity class | Unburned (area average) | — |
The extremely low NBR values (0.018–0.021) reflect the urban character of this zone — the bounding box includes dense built-up areas, roads, and infrastructure where NBR is near zero. The fire burned through fragmented garrigue patches interspersed with development, so the area-average signal is diluted. Pixel-level analysis of the burned garrigue patches would show meaningful NBR decline, but the mixed land cover masks this at the aggregate level.
### Vegetation timeline
| Period | NDVI | NBR | NDMI | Context |
|--------|------|-----|------|---------|
| Jan 2024 | 0.152 | 0.036 | 0.002 | Winter — very low vegetation |
| Jul 2024 | 0.148 | 0.020 | −0.025 | Summer — negative NDMI (drought) |
| Jan 2025 | 0.154 | 0.035 | 0.005 | Pre-fire winter |
| **Jun 2025** | — | **0.018** | — | **Pre-fire image** |
| 8 Jul 2025 | — | — | — | **Fire ignites** |
| Aug 2025 | — | 0.021 | — | Post-fire image — no area-average change |
| **Jul 2026** | **0.113** | **0.020** | **−0.017** | **1 year post-fire — NDVI declined** |
### Key findings
The NDVI decline from 0.148 (Jul 2024) to 0.113 (Jul 2026) suggests net vegetation loss in the zone, likely from both fire damage and ongoing urban pressure. The persistently negative NDMI (−0.017) indicates that the remaining vegetation is under severe moisture stress. The fire's impact on the urban-wildland interface is better assessed through high-resolution pixel analysis than area averages, as the mixed land cover dilutes the NBR signal.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
The pine forests between Pennes-Mirabeau and Marseille have burned repeatedly in recent decades, and the 2025 fire will accelerate the shift toward more fire-resistant garrigue and shrubland in the most severely burned zones. Reforestation with mixed-species stands — rather than the fire-prone Aleppo pine monoculture — will be essential to reduce future risk. Satellite monitoring of NDVI and NBR regrowth trajectories over the next 2–3 years will help land managers identify where natural regeneration is succeeding and where active replanting is needed. The fire also reignited debate about fuel management zones between the forest and Marseille's northern suburbs.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Narbonne Fire 2025 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/narbonne-fire-2025/
Updated: 2026-07-10
Markdown mirror: https://www.agroreports.org/articles/narbonne-fire-2025.md
> A wildfire burned 2,000 hectares near Narbonne in July 2025, mobilizing 1,050 firefighters. 5 injured, homes and infrastructure damaged. Satellite dNBR burn severity analysis from Sentinel-2.
## About this fire
On 7 July 2025, a fast-moving wildfire ignited at the Domaine Saint-Julien de Septime on the outskirts of Narbonne, in the Aude department of Occitanie. Driven by strong winds and extreme heat, the fire swept through garrigue and pine scrubland, consuming **2,000 hectares** of vegetation within 24 hours. It was one of several major fires to strike southern France during the first week of July 2025, part of an intense fire season fueled by a historic early-summer heatwave.
The blaze prompted large-scale confinement orders in multiple neighborhoods of Narbonne and surrounding communes, including Roches-Grises, Montplaisir, Réveillon, and Peyriac-de-Mer. Over **1,050 firefighters** and 270 vehicles were deployed, supported by 2 Air Tractor water-bombers and 3 helicopters. Five firefighters were lightly injured and 5 civilians required medical attention, including a child. The fire destroyed an atelier and an equestrian stable, partially damaged 6 homes, a chapel, and a barn, and killed 3 horses. Two thousand three hundred homes lost electricity in nearby Port-la-Nouvelle.
The Réserve Africaine de Sigean — a major wildlife park near the fire zone — was closed as a precaution. The A9 and A61 motorways were shut, and the RD6009 was damaged by fire. The scale of the disruption underscored how quickly a wildfire on the urban-wildland interface can paralyze a city of 50,000+ residents.
## Timeline & severity
| Date | Event |
|------|-------|
| **7 Jul 2025** | Fire ignites at Domaine Saint-Julien de Septime, Narbonne |
| **7–8 Jul 2025** | Fire spreads to 2,000 ha overnight; 1,050 firefighters deployed |
| **8 Jul 2025** | A9 and A61 motorways closed; 2,300 homes without power; Sigean reserve shut |
| **~11 Jul 2025** | Fire contained after several days of intensive ground and air operations |
| **Post-fire** | 2,000 ha burned; 5 firefighters injured; 6 homes damaged; 3 horses killed |
The fire's rapid overnight spread — 2,000 hectares in less than 24 hours — was enabled by bone-dry garrigue vegetation, sustained winds, and temperatures above 35°C. The urban-wildland interface made it particularly dangerous, with flames approaching residential areas of Narbonne itself.
## Satellite analysis
Sentinel-2 satellite imagery provides before-and-after analysis of the fire's impact on the Narbonne garrigue.
### Burn severity (dNBR)
| Metric | Value | Image date |
|--------|-------|------------|
| Pre-fire NBR | 0.131 | 30 Jun 2025 |
| Post-fire NBR | 0.135 | 11 Aug 2025 |
| **dNBR** | **−0.004** | — |
| USGS severity class | Unburned (area average) | — |
The near-zero dNBR reflects the extremely low pre-fire biomass of the Narbonne garrigue (NBR 0.131, compared to 0.5+ for dense forest). The fire burned through sparse scrubland where the NBR signal is naturally weak. The slight negative dNBR suggests marginal post-fire greening from early regrowth between the fire and the post-fire image (35 days later).
### Vegetation timeline
| Period | NDVI | NBR | NDMI | Context |
|--------|------|-----|------|---------|
| Jan 2024 | 0.276 | 0.149 | 0.060 | Winter baseline |
| Jul 2024 | 0.247 | 0.118 | 0.014 | Summer drought — very low NDMI |
| Dec 2024 | 0.260 | 0.148 | 0.067 | Pre-fire winter |
| **Jun 2025** | — | **0.131** | — | **Pre-fire image** |
| 7 Jul 2025 | — | — | — | **Fire ignites** |
| Aug 2025 | — | 0.135 | — | Post-fire image — no significant NBR change |
| **Jul 2026** | **0.264** | **0.160** | **0.031** | **1 year post-fire — NBR above pre-fire** |
### Key findings
The Narbonne garrigue had very low vegetation cover before the fire (NDVI 0.247–0.276, NBR 0.118–0.149), typical of drought-stressed Mediterranean scrubland. The fire caused no detectable area-average NBR change, but current NBR (0.160) is now **22% higher** than the pre-fire value (0.131) — suggesting post-fire regrowth of pioneer species. However, current NDMI (0.031) remains very low, indicating persistent moisture stress and continued fire vulnerability.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
The Narbonne fire burned primarily through garrigue and scrubland, which are adapted to periodic fire and expected to regrow within 1–3 years. However, the loss of vegetation cover on slopes near the city creates short-term erosion risk before the autumn rainy season. For vineyard owners on the fire's perimeter, satellite monitoring of NDVI and NDMI over the coming months will help identify vines stressed by heat and smoke exposure, distinguishing temporary damage from permanent vine loss. The fire also highlighted the need for fuel breaks between the Corbières garrigue and the Narbonne urban area.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Dadia-Evros Megafire 2023 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/dadia-evros-megafire-2023/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/dadia-evros-megafire-2023.md
> The largest wildfire in EU history burned 94,000 hectares of protected forest in northeastern Greece. Satellite dNBR analysis reveals burn severity patterns across the Dadia-Lefkimi-Soufli National Park.
## About this fire
The Dadia-Evros megafire was the **largest single wildfire ever recorded in the European Union**. Ignited on 19 August 2023 near Alexandroupolis in northeastern Greece, it merged with multiple smaller fires and swept through the Dadia-Lefkimi-Soufli Forest National Park — one of Europe's most important protected areas for birds of prey, home to all of Europe's vulture species.
The fire burned for over three weeks across nearly **94,000 hectares** of oak, beech, and pine forest. It killed at least 20 people, destroyed homes and livelihoods across dozens of villages, and caused irreversible ecological damage to a Natura 2000 site that had taken decades of conservation investment to build. The carbon released by the fire exceeded Greece's annual fossil fuel emissions for that year. The megafire was driven by months of drought, extreme heat, and dry winds — the same climate-change-fuelled conditions that made 2023 a record year for Greek wildfires.
## Timeline & severity
| Date | Event |
|------|-------|
| **19 Aug 2023** | Fire ignites near Alexandroupolis, merging with multiple smaller fires |
| **20–25 Aug 2023** | Rapid spread through Dadia-Lefkimi-Soufli National Park during extreme heat and wind |
| **26–29 Aug 2023** | EU's largest-ever aerial firefighting operation deployed; fire eventually contained |
| **Post-fire** | 94,000 ha burned — the EU's largest single wildfire on record |
The fire's severity was extreme across much of the burn scar. Ecologists expect the Dadia forest to take **50–80 years** to recover to its pre-fire state — if climate conditions allow it to recover at all.
## Satellite analysis
This analysis uses **dNBR (differenced Normalized Burn Ratio)** computed from Sentinel-2 satellite imagery to quantify burn severity across the 94,000-hectare scar. The severity map reveals where the fire burned hottest and where the forest canopy survived — the first step in prioritising restoration and habitat recovery efforts.
dNBR captures the dramatic reversal in reflectance patterns after a fire: healthy vegetation reflects strongly in near-infrared and absorbs shortwave-infrared, while burned areas do the opposite. By comparing pre-fire and post-fire NBR values, dNBR classifies every pixel on the USGS standard severity scale — from unburned through to stand-replacing high-severity fire.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
At 94,000 hectares, this is one of the most extensive burn scars ever analysed from space in Europe. Recovery in the Dadia forest is expected to take decades — oak and beech regeneration is slow, and the protected bird-of-prey habitat that was lost will require active restoration. The first priority is erosion control and identifying surviving canopy patches where natural regeneration can be jump-started. Satellite monitoring over the coming years will be essential to track regrowth and guide intervention.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Lahaina Fire 2023 (Maui) — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/maui-lahaina-fire-2023/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/maui-lahaina-fire-2023.md
> The deadliest US wildfire in over a century destroyed historic Lahaina on Maui. Hurricane-force winds drove fire through invasive grasses into a densely populated town. Satellite dNBR analysis.
## About this fire
The Lahaina fire was the deadliest United States wildfire in over a century. Driven by hurricane-force winds (Hurricane Dora passing south of Hawaii) through bone-dry invasive guinea grass, the fire swept through the historic town of Lahaina on Maui's west coast on 8 August 2023 with terrifying speed. Most victims died trying to evacuate through gridlocked streets.
The fire killed **at least 102 people**, destroyed over 2,200 structures (most of historic Lahaina, a former Hawaiian capital), and caused an estimated **$5.5 billion** in damage. While the burned area (approximately 2,200 hectares) was small compared to other major wildfires, the destruction was catastrophic because it burned through a densely populated urban area, not wildland. The fire's fuel was largely invasive grasses — Guinea grass and fountain grass introduced by colonial-era agriculture — which flash-dry during drought and burn explosively.
## Timeline & severity
| Date | Event |
|------|-------|
| **8 Aug 2023** | Hurricane Dora's winds drive fire into Lahaina; rapid spread through invasive grasses |
| **8–9 Aug 2023** | Historic Lahaina destroyed; 102+ fatalities, 2,200+ structures lost |
| **12 Aug 2023** | Fire contained |
| **Post-fire** | 2,200 ha burned; $5.5 billion in damage; every structure in downtown Lahaina affected |
The ecological story is one of invasion: without non-native guinea and fountain grasses covering abandoned plantation land, the fire would likely not have reached the town. Controlling invasive grasses is now central to Hawaii's wildfire strategy.
## Satellite analysis
This page presents a **dNBR burn severity analysis** from Sentinel-2 satellite imagery. Though dNBR is designed for vegetation burn severity, in an urban fire it also captures the destruction of built structures — their reflectance properties change dramatically when burned, and they appear as severely affected areas on the severity map. The analysis shows the extent of structural and vegetation destruction across the Lahaina burn scar, classified on the USGS dNBR severity scale.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
Recovery in Lahaina is primarily structural and social rather than ecological — the fire destroyed a historic town, not wildland. Vegetation recovery will be complicated by the invasive grasses that fuelled the fire: without aggressive management, guinea grass recolonises rapidly and recreates the same fire hazard. Long-term satellite monitoring can track vegetation regrowth patterns and help identify areas where invasive grasses are re-establishing, guiding fuel reduction efforts.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Landiras Wildfire 2022 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/landiras-wildfire-2022/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/landiras-wildfire-2022.md
> France's largest wildfire in over 50 years burned 30,000 hectares of maritime pine forest south of Bordeaux during a 42°C heatwave. dNBR satellite analysis of the 2022 Landiras fires.
## About this fire
The Landiras wildfire was the largest wildfire in France in over 50 years. Ignited on 12 July 2022 in the Landes de Gascogne forest, south of Bordeaux, it burned through bone-dry maritime pine plantations during an unprecedented heatwave that pushed temperatures to 42°C in the region. A second fire, starting 9 August in the same area (often called Landiras 2), compounded the damage.
The two fires combined consumed roughly **30,000 hectares** of maritime pine forest, scrubland, and some agricultural land. Over 40,000 people were evacuated from surrounding towns and campsites. The fire was a wake-up call for France: a country that had historically considered large wildfires a Mediterranean problem watched one burn in the temperate southwest, driven by climate change-induced drought and heat. The economic impact fell hardest on the timber industry — the Landes forest is Europe's largest artificial forest, planted with maritime pine for paper and construction.
## Timeline & severity
| Date | Event |
|------|-------|
| **12 Jul 2022** | Fire ignites in Landiras during a 42°C heatwave |
| **12–18 Jul 2022** | Rapid spread through bone-dry maritime pine plantations; 40,000+ evacuated |
| **9 Aug 2022** | Second fire (Landiras 2) starts in the same area, compounding the damage |
| **13 Aug 2022** | Fire finally contained after over a month |
| **Post-fire** | ~30,000 ha burned; timber industry severely impacted |
Ecologically, the fire destroyed habitat for protected species and released stored carbon. Reforestation began almost immediately but will take **30+ years** to restore the pine stands to maturity.
## Satellite analysis
This page presents a **dNBR (differenced Normalized Burn Ratio) burn severity analysis** computed from Sentinel-2 satellite imagery. Burn severity is measured by comparing satellite imagery from before and after the fire: healthy vegetation reflects strongly in near-infrared and absorbs shortwave-infrared, while fire reverses this pattern. dNBR quantifies that change on the USGS standard scale, classifying every pixel from unburned through low, moderate, and high severity.
The severity breakdown table quantifies the area in each class — critical information for the timber industry, reforestation planning, and insurance assessment.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
The Landes forest — Europe's largest artificial pine plantation — faces a 30+ year recovery timeline. Reforestation began almost immediately after the fire, but maritime pine requires decades to reach commercial maturity. Satellite monitoring of vegetation indices (NDVI, NDMI, NBR) will be essential to track regrowth, identify areas where natural regeneration is struggling, and guide replanting decisions. The fire also created erosion risk on exposed slopes, particularly before winter rains — a secondary concern that satellite monitoring can help anticipate and mitigate.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Portugal Wildfires September 2024 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/portugal-fires-september-2024/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/portugal-fires-september-2024.md
> Portugal's worst wildfire since 2017 burned 110,000 hectares of forest and farmland across northern and central regions. Satellite dNBR analysis reveals burn severity and agricultural impact.
## About this fire
September 2024 saw Portugal's worst wildfire episode since the catastrophic 2017 season. A combination of extreme drought (after the hottest summer on record in Iberia), low humidity, and strong easterly winds created conditions where multiple fires ignited and spread rapidly across the forested hills of northern and central Portugal. The fires burned across roughly **110,000 hectares** of pine and eucalyptus plantations, cork oak and chestnut forest, olive groves and vineyards.
At least 9 people died and dozens were injured. Entire communities were evacuated. The smoke plume was visible from space, drifting across the Atlantic. Portugal, which has the highest proportion of forest fires of any EU country relative to its land area, faced renewed criticism about eucalyptus monoculture — the fast-growing pulp tree that is highly profitable but extremely flammable, and which has expanded dramatically across the country's interior over the past four decades. The fires hit agricultural land as well as forest: vineyards in the Douro and Vinho Verde regions were threatened, olive groves and chestnut orchards burned, and smoke taint affected grapes being harvested at the time.
## Timeline & severity
| Date | Event |
|------|-------|
| **15 Sep 2024** | Multiple fires ignite across Norte and Centro regions during extreme drought and easterly winds |
| **15–25 Sep 2024** | Fires spread rapidly through eucalyptus and pine plantations, cork oak forest, and farmland |
| **25 Sep 2024** | Fires contained after 10 days |
| **Post-fire** | 110,000 ha burned; 9+ deaths; significant agricultural and timber losses |
For farmers, the impact was double: direct fire damage to crops and infrastructure, plus smoke taint affecting grapes being harvested at the time — a financial loss that extends beyond the visibly burned area.
## Satellite analysis
This page presents a **dNBR burn severity analysis** from Sentinel-2 satellite imagery. The severity map reveals where fires burned hottest and where agricultural and forest land survived — critical information for insurance claims, recovery planning, and targeting erosion-control measures before winter rains. The dNBR classification quantifies how many hectares fell into each USGS severity class across the full 110,000-hectare fire footprint, distinguishing surface fire from stand-replacing crown fire.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
Post-fire recovery in northern Portugal faces several challenges. Fast-growing eucalyptus may resprout quickly, but the fire hazard it represents will return with it. Native cork oak and chestnut forest, more fire-resistant but slower to recover, will need decades. Agricultural recovery — particularly for vineyards and olive groves — requires assessment of both direct fire damage to trees/vines and smoke taint to the harvested crop. Satellite monitoring of NDVI and NBR over the coming seasons will be essential to distinguish natural regrowth from areas needing replanting, and to detect erosion before winter rains cause secondary damage.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# Tenerife Forest Fire 2023 — Burn Severity Map & Satellite Analysis
URL: https://www.agroreports.org/articles/tenerife-forest-fire-2023/
Updated: 2026-06-18
Markdown mirror: https://www.agroreports.org/articles/tenerife-forest-fire-2023.md
> The worst wildfire in the Canary Islands in 40 years burned 15,000 hectares of Canary Island pine forest around Mount Teide. dNBR satellite analysis of the 2023 Tenerife fire.
## About this fire
The Tenerife forest fire of August 2023 was the worst wildfire in the Canary Islands in 40 years. Ignited on 15 August in the Corona Forestal natural park — the pine-clad slopes around Mount Teide — it burned for over two weeks across rugged, inaccessible terrain that made ground firefighting nearly impossible. Aerial units dropped water continuously but the fire was ultimately contained by favourable weather and firebreaks.
The fire consumed approximately **15,000 hectares** of Canary Island pine forest (Pinus canariensis) and high-altitude scrub. Over 12,000 people were evacuated from towns across the island. The timing — mid-August, peak tourist season — amplified the economic disruption to the Canary Islands' vital tourism economy. Ecologically, the fire tested a species that has evolved with fire: Pinus canariensis is one of the few pines that can resprout after fire, with thick bark protecting the cambium and epicormic buds allowing regrowth from the trunk.
## Timeline & severity
| Date | Event |
|------|-------|
| **15 Aug 2023** | Fire ignites in Corona Forestal natural park on the slopes of Mount Teide |
| **15–31 Aug 2023** | Fire spreads through rugged, inaccessible terrain; 12,000+ evacuated; aerial firefighting continuous |
| **1 Sep 2023** | Fire contained by favourable weather and firebreaks |
| **Post-fire** | 15,000 ha of Canary Island pine forest burned |
The fire's severity varied across the terrain — steep inaccessible slopes burned more intensely than areas where firebreaks and aerial drops were effective. The recovery will be closely watched by ecologists studying climate-fire-vegetation feedback in Mediterranean island ecosystems.
## Satellite analysis
This page presents a **dNBR burn severity analysis** computed from Sentinel-2 satellite imagery. The map shows the severity gradient across the 15,000-hectare burn scar, and the table quantifies how many hectares fell into each USGS severity class — from unburned through to high-severity stand-replacing fire. This breakdown is critical for prioritising restoration and erosion-control work across the fire's perimeter.
[Read our full guide: What is NBR? →](/articles/what-is-nbr)
## Recovery outlook
The recovery outlook for Tenerife's pine forest is cautiously optimistic because Pinus canariensis is fire-adapted: its thick bark and epicormic resprouting ability allow many trees to survive and regenerate after fire. However, recovery speed depends on post-fire rainfall and the absence of repeat fires before the forest re-establishes. Satellite monitoring of NDVI and NBR over the coming years will track resprouting success, identify areas where regeneration is failing, and guide replanting in high-severity zones. The steep volcanic slopes also create significant erosion risk before the canopy regrows.
> **Free report:** Check your field's fire risk this month — free satellite analysis, no signup. [Analyze my land's fire impact →](/)
---
# See Your Land in 3D: New Terrain, Relief & Parcel Layers
URL: https://www.agroreports.org/articles/terrain-3d-maps/
Published: 2026-09-27
Markdown mirror: https://www.agroreports.org/articles/terrain-3d-maps.md
> Explore AgroReports' new map layers — terrain relief, elevation contours, cadastral parcel boundaries, a satellite basemap switcher, and a full 3D terrain viewer.
New map layers are here — see the real shape of your land in relief and 3D, switch between satellite views, and overlay cadastral parcel boundaries right on the map.
## Maps That Show More Than Just Colour
Until now, AgroReports maps have shown your land through satellite indices — NDVI, NDWI, and the rest — as flat colours over a plain background. That's incredibly useful for spotting stress and change, but it only tells part of the story. It doesn't show you the **shape** of your land: the slopes, the ridges, the low spots where water pools, or exactly where your field ends and your neighbour's begins.
Today, that changes. We've added a new set of map layers and a full 3D viewer to AgroReports, so you can finally see your land the way you experience it on foot — its relief, its elevation, its boundaries — without leaving the map.
## See the Relief of Your Land
The headline addition is a new **relief layer** — a shaded view of the terrain that reveals hills, valleys, and slopes in light and shadow. Built from high-resolution elevation data (down to a metre in places), it turns a flat map into something you can almost feel.
Relief layers appear automatically based on where your field is located. Wherever you're looking, AgroReports offers the elevation layers available for that area, each with a plain-language description in your language — English, French, Spanish, Portuguese, or German.
Plate I — Relief layer
IGN · LiDAR HD
A few things worth knowing:
- **It's purely visual.** The relief layer doesn't change any numbers, stats, or values in your report. It's there to help you read the map, nothing more.
- **It layers cleanly.** Relief sits beneath other map details — like the parcel boundaries described below — so everything stays readable.
- **It respects your language.** Every relief layer is described in the language of your report.
## Switch Between Satellite Views
We've also added a new **"View" selector** to the map, so you can choose exactly how you want to see your land. Depending on the question you're trying to answer, a different basemap can make all the difference.
You can now switch between:
- **Recent satellite imagery** — true-colour Sentinel-2 and NASA's MODIS, so you can see the latest cloud-free view of your fields.
- **Aerial photography** — high-resolution orthophotos, including an infrared view that highlights vegetation in a completely different way.
- **Mapbox Satellite** — with or without labels, for a clean look at the landscape.
- **Thematic maps** — a crop-registry map, a land-cover map, and a soil map for understanding what's around and beneath your field.
- **Simple street maps** — ESRI Satellite, OpenStreetMap, and light, dark, and outdoors styles for everyday navigation.
Whatever view you choose, it carries through everywhere — including into the new 3D viewer.
## Read the Elevation with Contours
For those who think in topo lines, we've added an **elevation contour overlay**. Turn it on to see the classic contour lines that trace equal elevation across your land, with a colour legend to help you read them at a glance. It's a small addition that makes a big difference when you're thinking about slope, drainage, or where water will run.
## See Your Parcel Boundaries
One of the most practical additions: you can now overlay **official cadastral parcel boundaries** directly on the map. See exactly where your field lines fall — property limits, plot shapes, and the outlines of neighbouring parcels — right on top of the satellite view. We've made sure these boundaries always draw above the relief hillshade, so they stay crisp and legible even on the most dramatic terrain.
Plate II — Cadastral parcels
IGN Parcellaire Express
## Explore Your Land in 3D
Finally, we've added a **full-screen 3D terrain viewer**. Open it from the map and your land rises up in front of you — you can rotate, zoom, and tilt the view, and exaggerate the elevation (1× to 3×) to make subtle slopes obvious.
The 3D view keeps things simple: two clean basemaps (satellite without labels, and ESRI), with the same elevation, relief, and cadastre controls you already know, tucked in the bottom-right corner just like the flat map. Everything you've set up on the flat map — your basemap, elevation, relief, and parcel layers — carries straight into 3D, so there's no re-configuring.
Plate III — 3D terrain with relief
IGN · LiDAR HD
Plate IV — 3D elevation exaggeration
IGN · LiDAR HD
## Smaller Touches That Add Up
A few smaller improvements round out the update:
- A **collapse button** on the sidebar lets you hide it when a relief layer is available, giving the map more room.
- The map now **resizes correctly** when you collapse or expand the sidebar — no more misaligned tiles.
- When the sidebar is collapsed, we tidy up the view by hiding the selected-area overlay and the account menu, so nothing gets in the way of the map.
## Try It on Your Own Land
The best way to understand these new layers is to see them on ground you know. Just go to [app.agroreports.org](https://app.agroreports.org/) and click **3D** — no report or boundary drawing needed.
As always, it's completely free.
---
# Recurring Reports: Automate Your Satellite Land Monitoring
URL: https://www.agroreports.org/articles/recurring-reports/
Published: 2026-05-22
Markdown mirror: https://www.agroreports.org/articles/recurring-reports.md
> Set up automated recurring satellite reports for your fields. Choose weekly, biweekly, or monthly delivery. Never miss a change in your land's health with scheduled NDVI and NDWI monitoring.
Set it once, get reports forever — how AgroReports' new recurring reports feature automates your land monitoring at no cost
## Your Land Changes Every Week — Your Monitoring Should Too
One of the most common requests we've heard from farmers and land managers is simple: **"Can you just send me a new report automatically?"** Today, we're excited to announce that recurring reports are now live on AgroReports.
Instead of remembering to log in and manually request a new analysis every time you want to check on your fields, you can now **set it once and forget it**. Choose your report type, pick your frequency, and AgroReports will generate and deliver fresh satellite analysis on schedule — directly to your inbox. It's the same professional-grade NDVI, NDWI, NDMI, and multi-index analysis you already trust, now fully automated.
## How Recurring Reports Work
Setting up a recurring report takes less than a minute. Here's how it works:
### 🔁 Three Flexible Frequencies
Choose the cadence that matches your needs:
- **Weekly** — Ideal during critical growth stages, after planting, or when monitoring fast-changing conditions like irrigation response or storm recovery.
- **Biweekly** — A balanced rhythm for regular crop monitoring through the growing season without overwhelming your inbox.
- **Monthly** — Perfect for long-term trend tracking, off-season baselining, or monitoring larger properties where week-to-week changes are subtle.
### 📬 Automatic Delivery to Your Inbox
Each scheduled report arrives with "[Recurring]" in the subject line so you can easily filter and find them. Every delivery includes:
- A detailed PDF report with the full satellite analysis
- A CSV file with raw index values for your own calculations
- A link to the interactive online report with zoomable maps
- A dashboard link to manage all your subscriptions in one place
### 🌍 Choose Your Language
Recurring reports are available in all five supported languages: **English, French, Spanish, Portuguese, and German**. Set your preferred language once, and every scheduled report will arrive in that language — no need to reconfigure each time.
### 📊 Manage Everything from Your Dashboard
All your subscriptions are visible in one place. Pause or resume any subscription, change the frequency, or cancel entirely — no emails to customer support, no waiting. You're in full control of when and how often you receive reports.
## Why Recurring Reports Are a Game-Changer
### Catch Problems Before You See Them
When you only run reports reactively — because something looks wrong — you're already behind. Weekly automated reports build a timeline of your field's health, making it easy to spot the exact moment a problem began. A gradual NDVI decline over three weeks tells a very different story than a single "everything looks fine" snapshot.
### Build a Data History Without Effort
Every recurring report becomes part of your land's permanent satellite record. Over a growing season, you'll accumulate a rich dataset that reveals patterns invisible in any single report — seasonal cycles, response to inputs, long-term trends. The best part? You don't have to remember to request any of it.
### Compare Reports Like Never Before
With reports arriving on a fixed schedule, you're comparing apples to apples. A report from May 1 vs. May 8 vs. May 15 — same field, same report type, same indices. This consistency makes it dramatically easier to spot genuine changes versus normal fluctuations.
### Works with Every Report Type
Recurring reports support all six report types: Land Health Overview, Seasonal Vegetation, Historical Trends, Vineyard Performance, Forest Health & Change Detection, and Forest Fire Resilience. Set up a subscription for each field and report combination that matters to you — all managed from one dashboard.
## Who Should Use Recurring Reports?
- **Active farmers** monitoring crops through the growing season — set a weekly Land Health Overview and never wonder "how are things looking this week?"
- **Vineyard managers** tracking vigor through veraison to harvest — a biweekly Vineyard Performance report maps exactly how each block is developing.
- **Forest managers** in fire-prone regions — a monthly Forest Fire Resilience report during dry season provides ongoing NDMI moisture tracking and risk scores.
- **Agricultural consultants** managing multiple client properties — set up subscriptions per client and receive reports without lifting a finger.
- **Land investors** doing due diligence — a few months of automated reports reveal the true seasonal productivity of a property before you commit.
## Still Completely Free
Like everything on AgroReports, recurring reports are **100% free**. No premium tiers, no report limits, no hidden costs. We believe that regular, reliable satellite monitoring shouldn't be a luxury — it should be the baseline for anyone managing land.
Each scheduled report is processed by the same pipeline as on-demand reports: satellite data from NASA and ESA, multi-index analysis, AI-generated plain-language summaries, and delivery within 15 minutes. The only difference is that you don't have to request it.
## Get Started with Recurring Reports
Ready to automate your land monitoring? Here's how to set up your first recurring report:
- Go to [app.agroreports.org](https://app.agroreports.org/) and request any report as usual
- After receiving your first report, visit your dashboard to set up a subscription
- Choose your report type, frequency (weekly/biweekly/monthly), and preferred language
- That's it — your next report arrives automatically on schedule
---
# Forest Fire Resilience Report: Assess Risk & Map Burn Severity
URL: https://www.agroreports.org/articles/forest-fire-resilience/
Published: 2026-05-20
Markdown mirror: https://www.agroreports.org/articles/forest-fire-resilience.md
> Learn how AgroReports' dual-mode Forest Fire Resilience Report uses NDMI, NBR, NDVI, and NDWI satellite data to assess fire risk and map burn severity for forest managers.
A dual-mode satellite analysis using NDMI, NBR, NDVI, and NDWI that helps forest managers assess fire risk before it strikes — and map burn severity for recovery after the flames are out
## A Report for Both Sides of the Fire Line
Wildfire is one of the most destructive forces facing forest managers today. With fire seasons growing longer and more intense across Europe, North America, and beyond, the ability to **assess risk before a fire** and **measure damage after one** is no longer optional — it's essential.
The Forest Fire Resilience Report from AgroReports is unique in offering both capabilities in a single, dual-mode analysis. By analyzing four satellite-derived indices — NDMI, NBR, NDVI, and NDWI — and comparing them against a 5-year regional baseline, the report delivers science-backed insights whether you're planning prevention measures or navigating post-fire recovery.
## Pre-Fire Mode: Understanding Your Risk
Before fire season begins, the report analyzes four satellite-derived vegetation indices — NDMI, NBR, NDVI, and NDWI — to quantify the factors that make your forest vulnerable:
### 💧 NDMI — Vegetation Moisture Analysis
The Normalized Difference Moisture Index is the single most important satellite-derived metric for fire risk assessment. It measures the water content in live vegetation, which directly affects flammability. An NDMI value of -0.25 or lower indicates extreme vegetation dryness — the kind of conditions where a spark can turn into a catastrophe within hours. Because vegetation moisture is the strongest predictor of fire spread, **NDMI carries 60% of the weight** in the composite Fire Risk Score.
### 📉 NBR — Fuel Condition & Burn Assessment
The Normalized Burn Ratio is essential for assessing both pre-fire fuel condition and post-fire severity. In healthy forests, NBR ranges from **0.3 to 0.8**, reflecting moist, vigorous vegetation with low fuel vulnerability. Values below **-0.1** typically indicate burn scars, bare soil, or severely degraded vegetation — making NBR a critical early-warning signal alongside NDMI. In the Fire Risk Score, NBR contributes **25% of the weight**, capturing fuel-condition dynamics that NDMI alone cannot see.
### 🌿 NDVI — Canopy Density & Fuel Load
Using the Normalized Difference Vegetation Index, the report estimates the quantity and distribution of combustible biomass across your property. A high NDVI (>0.5) signals dense canopy — which means high fuel load if moisture conditions are dry. NDVI works in tandem with NBR: while NDVI measures how much vegetation is present, NBR reveals its condition. In the Fire Risk Score, NDVI contributes **15% of the weight**, reflecting fuel load as a secondary risk amplifier.
### 🔥 Fire Risk Score (0–100)
The report computes a weighted composite score from the three primary fire-risk indices into a single, easy-to-understand metric on a 0–100 scale. **NDMI drives 60% of the score** (vegetation moisture is the #1 predictor of fire spread), **NBR contributes 25%** (fuel condition and degradation), and **NDVI contributes 15%** (fuel load as a risk amplifier):
- **0–25 (Low):** Moist conditions, low fuel load — minimal fire risk.
- **26–50 (Moderate):** Normal seasonal conditions — standard precautions advised.
- **51–75 (High):** Dry vegetation, significant fuel load — active monitoring and preparedness recommended.
- **76–100 (Extreme):** Critical moisture deficit, heavy fuel load — immediate risk mitigation actions warranted.
### 📜 Historical Fire Context
The report cross-references your property with NASA MODIS fire records dating back to 2000, identifying how many times the area has burned in the past and whether your land sits within a historically fire-prone corridor.
### 📈 Regional Baseline Comparison
Every index value is compared against its **5-year historical range** for the specific location. This critical context prevents false alarms — an NDMI reading that looks low in absolute terms may be perfectly normal for a Mediterranean forest in August. The report only flags values as anomalous when they fall **outside the historical min-max range**, ensuring that risk assessments and recovery recommendations are grounded in local ecological reality, not generic thresholds.
## Post-Fire Mode: Measuring the Damage
After a fire, the report switches to burn severity assessment using the USGS-standard **dNBR (Differenced Normalized Burn Ratio)** methodology:
### dNBR Burn Severity Mapping
The dNBR compares pre-fire and post-fire NBR values to classify every pixel into standardized severity categories. The USGS thresholds are: **0.10–0.27** (low severity, surface fire with canopy largely intact), **0.27–0.44** (moderate-low, partial canopy scorch), **0.44–0.66** (moderate-high, significant canopy mortality), and **>0.66** (high severity, near-complete mortality with soil exposure). This is the same methodology used by the USGS and forest services worldwide.
### Phased Recovery Planning with Milestones
Based on burn severity, the report outlines a phased 5-year recovery plan with specific monitoring milestones at **1, 3, 6, 12, 24, 36, 48, and 60 months** post-fire. Each milestone tracks NDVI, NBR, and NDMI recovery against pre-fire baselines. High-severity zones may require replanting and erosion control; moderate-severity zones may benefit from natural regeneration monitoring; low-severity zones may need only basic observation.
### Erosion & Watershed Risk
The report identifies slopes and drainages where severe burning has removed protective ground cover, flagging areas at elevated risk for post-fire erosion, debris flows, and water quality impacts.
### Long-Term Monitoring Plan
The report includes a dedicated monitoring schedule with specific **NDVI, NBR, and NDMI recovery thresholds** for each milestone. It tracks recovery trajectory from pre-fire baseline through post-fire acute phase to mature recovery, and defines when the forest can transition from intensive recovery monitoring back to routine forest health surveillance.
## Who Should Use This Report?
### Forest Owners in Fire-Prone Regions
Run a pre-fire assessment at the start of each dry season to understand your current risk level and prioritize mitigation actions — fuel breaks, thinning, or increased patrols.
### Insurance & Risk Assessment Professionals
Use the fire risk score and historical fire data as objective inputs for underwriting and premium calculations for forest properties.
### Post-Fire Recovery Teams
Quickly assess the extent and severity of burn damage across large areas. The dNBR maps provide a standardized, defensible basis for allocating recovery resources and applying for disaster relief funding.
### Conservation Organizations
Monitor fire-prone preserves, document fire impacts for grant reporting, and plan habitat restoration based on burn severity data.
## Complementary Reports
For comprehensive forest management, pair the Fire Resilience Report with our [Forest Health & Change Detection](/reports/#forest-health) report. Together, they cover the full spectrum of forest monitoring — from ongoing health surveillance to fire-specific risk assessment and recovery.
## Get Your Free Forest Fire Resilience Report
Whether you're preparing for the upcoming fire season or recovering from a recent burn, satellite data gives you the objective, science-backed information you need to make the right decisions. AgroReports' Forest Fire Resilience Report is generated in under 15 minutes — completely free.
---
# Forest Health & Change Detection: Satellite Monitoring for Woodlands
URL: https://www.agroreports.org/articles/forest-health/
Published: 2026-03-08
Markdown mirror: https://www.agroreports.org/articles/forest-health.md
> Discover how AgroReports' Forest Health report uses 6 satellite indices and 5 years of data to detect canopy decline, bark beetle outbreaks, disease, and illegal logging.
How 5 years of satellite data — evaluated through six vegetation indices including NDRE for early disease detection, NDWI for water stress and fire risk assessment, and NBR for burn/degradation monitoring — helps forest managers detect canopy decline, disease outbreaks, and disturbance events before damage is visible from the ground
## Seeing the Forest Through Satellites
Forests change slowly — until they don't. A woodland that has stood for centuries can be transformed in months by disease, pests, drought, or human activity. Ground-based monitoring, while essential, simply cannot cover large forested areas with the frequency and consistency needed to catch problems early.
The Forest Health & Change Detection report from AgroReports uses 5 years of satellite data — analyzed across two periods per year — to give you a comprehensive, multi-dimensional view of your forest's condition. Six vegetation indices are evaluated: NDVI, NDRE, SAVI, NDWI, NBR, and NDMI. Together they paint a picture no single index could provide alone. The report is designed for forest owners, conservation groups, timber companies, and government land managers who need reliable, data-driven insights about woodland health.
## What the Report Analyzes
The report evaluates **six vegetation indices** across 5 years of satellite data, with two measurement periods per year. Four indices form the core Vegetation Health analysis; all six contribute to the change detection and disturbance assessment.
### 🌲 NDVI — Canopy Density & Health
The Normalized Difference Vegetation Index is the primary tool for assessing overall forest vigor. Values range from 0.2 (sparse/degraded) to 0.8+ (dense closed canopy). A declining NDVI trend can signal thinning canopy, disease progression, or the early stages of a pest outbreak. The report flags drops greater than 0.08 between comparable seasons as potential disturbance signals.
### 🔬 NDRE — Chlorophyll & Early Disease Detection
The Normalized Difference Red Edge index is uniquely sensitive to chlorophyll content in foliage, making it one of the most powerful early-warning tools for forest health. NDRE can detect plant stress from disease, pests, or nutrient deficiency while the canopy still appears green to the naked eye. Critically, the report automatically checks for **NDRE–NDVI divergence**: when NDRE declines but NDVI remains stable, it strongly suggests early-stage disease or pest activity before canopy thinning is visible.
### 🌿 SAVI — Biomass Proxy for Sparse Canopy
The Soil-Adjusted Vegetation Index corrects for soil background influence, making it essential for open woodlands, savannas, and recently thinned forests where bare ground would distort standard NDVI readings. SAVI provides a more reliable biomass estimate when canopy cover is below ~60%. It is also used in the report's fire risk assessment alongside NDWI: dry, sparse vegetation creates the highest fire-susceptibility conditions.
### 💧 NDWI — Water Content & Fire Susceptibility
The Normalized Difference Water Index measures moisture content in vegetation. Low NDWI values indicate water-stressed trees that are more vulnerable to drought mortality and fire. The report's dedicated **Water Stress & Fire Risk** section combines NDWI with SAVI data to assess area-wide fire susceptibility, and flags summer NDWI drops exceeding 0.15 as potential acute drought stress signals.
### 📊 NDMI — Canopy Moisture Stress
The Normalized Difference Moisture Index measures water content in vegetation and can detect drought stress well before NDVI shows a decline. A dropping NDMI trend across multiple periods is often the earliest warning sign of forest health issues, preceding visible symptoms by weeks or months.
### 🔥 NBR — Burn Scars & Forest Degradation
The Normalized Burn Ratio is specifically designed to detect burned areas and assess burn severity. Even for forests without recent fire history, NBR helps identify degraded or dead stands by distinguishing structural vegetation loss from seasonal canopy variation, providing a complementary signal to the vegetation health indices above.
Beyond individual indices, the report includes automated **disturbance detection** that scans all 10 historical periods for statistically significant drops — including the NDRE–NDVI divergence pattern, summer NDWI declines, and NDVI losses exceeding defined thresholds. These signals are presented alongside management recommendations in a clear, actionable format.
## NDRE: The Forest's Early Warning System
Of all the indices in the report, NDRE provides perhaps the most time-critical intelligence. Because NDRE measures chlorophyll concentration specifically — rather than overall leaf area or "greenness" — it can reveal stress at a cellular level before any visible change occurs in the canopy.
The key insight the report captures is **NDRE–NDVI divergence**. In a healthy forest, both indices tend to move together: more chlorophyll means more vigorous vegetation. But when NDRE begins to decline while NDVI remains stubbornly stable, something important is happening: the trees' chlorophyll is degrading, but the leaf structure is still intact. The canopy *looks* fine — but it isn't.
This divergence pattern is strongly associated with early-stage disease, bark beetle infestation, or nutrient deficiency. By the time NDVI drops noticeably — which is when most forest managers first become aware of a problem — the underlying stress has often been progressing for months or even a full growing season. The report automatically flags any period pair where NDRE drops by more than 0.05 while NDVI changes less than 0.03, giving you that critical head start.
Equally valuable is what an *absence* of NDRE divergence tells you: if both NDRE and NDVI are declining together, the cause is more likely structural (logging, windthrow, canopy thinning) rather than pathological (disease, pests). Distinguishing between these two scenarios determines whether you dispatch a chainsaw crew or a plant pathologist.
## Real-World Use Cases
### Early Bark Beetle Detection
In Central Europe, bark beetle outbreaks have devastated millions of hectares of spruce forest. The NDRE–NDVI divergence pattern is particularly effective here: NDRE often declines detectably while the canopy is still green, giving foresters a critical window for salvage logging and sanitation cuts before the visible "red attack" phase.
### Disease Surveillance at Scale
For large conservation estates or national parks, NDRE monitoring across all 10 historical periods provides a systematic disease surveillance system. Chlorophyll decline detected from space can guide targeted ground inspections — far more efficient than walking thousands of hectares looking for symptoms.
### Illegal Logging Monitoring
For conservation organizations and government agencies managing protected forests, satellite change detection provides an impartial record of canopy disturbance. Sudden drops in NDVI and NBR — where both indices decline together, ruling out disease — in restricted areas can be investigated promptly rather than discovered months later.
### Timber Inventory & Harvest Planning
Commercial forestry operations can use NDVI and SAVI-based vigor mapping to identify the most productive stands — SAVI being especially useful in thinned or recently harvested areas where soil exposure would skew NDVI. The multi-year trends also help monitor regeneration success after planting.
### Fire Risk & Drought Preparedness
The report's Water Stress & Fire Risk section uses NDWI and SAVI together to evaluate area-wide fire susceptibility. Managers in fire-prone regions can use declining NDWI trends and summer moisture stress signals to prioritize fuel reduction, plan fire breaks, and allocate water resources before the high-risk season begins.
### Climate Resilience Assessment
By comparing NDMI and NDWI trends across the 5-year record, managers can identify stands that are progressively losing moisture resilience — an early indicator of climate vulnerability. These areas can be prioritized for species diversification or thinning to reduce water competition.
## How to Read Your Forest Health Report
The report is organized into six sections that guide you from the big picture down to specific, time-bound actions:
- **Executive Summary:** A plain-English overview of the most critical findings — leading with any detected disturbance signals, the current forest health status (citing specific index values), and one bold priority action for the forest manager.
- **Vegetation Health:** A structured evaluation of all four primary indices — NDVI, NDRE, SAVI, and NDWI — each with a verdict (Excellent through Critical), an interpretation of the current value, and a concrete implication for forest health right now. A synthesis paragraph ties them together into a coherent canopy health picture.
- **Water Stress & Fire Risk:** A dedicated analysis combining NDWI moisture data with SAVI biomass estimates to assess current drought stress and area-wide fire susceptibility. Trends are compared against previous seasons to show whether conditions are improving or deteriorating.
- **Forest Cover Change & Trends:** The most data-rich section. A year-by-year table shows NDVI, NDWI, and NDRE values across all 10 historical periods, with change-vs-previous columns. Each detected disturbance signal is presented with its numeric values, the threshold that was exceeded, possible explanations (presented as hypotheses), and a recommended ground-truthing action. NDRE–NDVI divergence is explicitly flagged when detected.
- **Key Findings:** A concise table of 3–5 findings, each referencing specific numeric values and paired with clear recommended actions. If any index falls into a critical range, a blockquote warning highlights the exact value and the immediate step to take.
- **Management Recommendations:** Actionable steps organized into three time horizons — immediate (this week), short-term (this season), and long-term (1–3 years) — each justified by a specific data point from the analysis.
## Combining Forest Health with Fire Resilience
For properties in fire-prone regions, we recommend pairing the Forest Health report with our [Forest Fire Resilience Report](/reports/#fire-resilience). Together, they provide a complete picture: the Forest Health report's Water Stress & Fire Risk section uses NDWI and SAVI to assess current drought stress and fire susceptibility, while the Fire Resilience report evaluates terrain, fuel load, and landscape-level wildfire risk factors. One tells you how dry your forest is today — the other tells you what would happen if a fire started.
## Get Your Free Forest Health Report
Your forest deserves the same level of data-driven monitoring as the most advanced agricultural operations. AgroReports' Forest Health & Change Detection report is generated in under 15 minutes — completely free.
---
# Vineyard Performance Analysis: Optimize Vine Health with Satellite NDVI
URL: https://www.agroreports.org/articles/vineyard-performance/
Published: 2026-01-12
Markdown mirror: https://www.agroreports.org/articles/vineyard-performance.md
> Learn how AgroReports' Vineyard Performance Analysis report uses NDVI satellite data to assess vine vigor variability, optimize canopy management, and improve harvest decisions.
How four specialized vineyard indices — NDVI, NDRE, SAVI, and NDWI — help vineyard managers assess vigor, chlorophyll, water stress, and soil-adjusted canopy to make smarter viticulture decisions
## Why Vineyards Need Specialized Analysis
Viticulture is an art of precision. Unlike broadacre crops where uniform management is the norm, premium wine grape production demands that every block, every row, and sometimes every individual vine receives exactly what it needs — no more, no less.
The difference between a good vintage and a great one often comes down to how well you manage **vigor variability**. Too much vigor in one zone produces shaded, vegetal fruit; too little in another yields stressed vines with poor ripening. The Vineyard Performance Analysis from AgroReports gives you the data to manage this variability with surgical precision.
But vigor is only one piece of the puzzle. The report evaluates your vines through **four specialized indices** — NDVI (canopy vigor), NDRE (chlorophyll/nitrogen status), SAVI (soil-adjusted canopy), and NDWI (water status) — each calibrated to vineyard-specific ranges. Critically, every assessment is contextualized against the **current phenological stage** (budburst, flowering, veraison, or harvest), because an NDWI reading that signals desirable controlled deficit during veraison may indicate damaging stress during flowering.
## What Four Satellite Indices Reveal About Your Vines
Unlike broadacre crop reports that rely on a single vegetation index, the Vineyard Performance Analysis evaluates your vines through **four complementary satellite indices**, each answering a different viticultural question. Because vineyard canopies are sparse — with significant bare soil between rows — these indices are calibrated to vineyard-specific ranges that account for row spacing and trellis architecture.
### 🌿 NDVI — Canopy Vigor
NDVI (Normalized Difference Vegetation Index) measures overall vine canopy density and vigor. **Vineyard NDVI ranges are lower than broadacre crops** due to inter-row spacing: a healthy vineyard typically reads 0.45–0.60 during the growing season, whereas a wheat field at peak biomass may exceed 0.80. Values below 0.30 suggest sparse canopy, young vines, or dormancy. The report maps vigor zones across your blocks and tracks seasonal curves from budburst through harvest.
### 🔬 NDRE — Chlorophyll & Nitrogen Status
NDRE (Normalized Difference Red Edge) is a **critical vineyard-specific index** that penetrates deeper into the canopy and is far more sensitive to chlorophyll concentration than NDVI. It reveals nitrogen uptake, photosynthetic capacity, and early stress before it's visible to the naked eye. Values above 0.35 indicate high chlorophyll and vigorous nitrogen uptake; readings between 0.10–0.20 suggest early stress, possible nitrogen deficiency, or disease onset. A declining NDRE with stable NDVI is a classic early-warning signal for disease pressure or rootstock stress.
### 🏜️ SAVI — Soil-Adjusted Canopy (Preferred for Vineyards)
SAVI (Soil-Adjusted Vegetation Index) is **preferred over NDVI for vineyard analysis** because it mathematically corrects for the bare soil visible between vine rows. When NDVI reads significantly higher than SAVI (a divergence greater than 0.12), it means inter-row bare soil is inflating the NDVI value — SAVI provides the more reliable canopy assessment. Healthy vineyard SAVI ranges from 0.25–0.45; values above 0.45 indicate dense canopy cover despite row spacing.
### 💧 NDWI — Vine Water Status
NDWI (Normalized Difference Water Index) measures plant moisture content and is interpreted differently in vineyards than in broadacre crops. **Moderate water stress (NDWI between -0.15 and -0.05) is often desirable during veraison** — controlled deficit irrigation concentrates sugars and phenolics for premium wine quality. However, the same reading during flowering or early shoot growth signals damaging stress that reduces yield. The report evaluates water status in the context of the current phenological stage, distinguishing intentional deficit from problematic drought.
### 📊 Year-over-Year & Cross-Index Comparisons
The report tracks all four indices across **3 years with 2 periods per year**, building a rich historical baseline. Cross-index analysis reveals patterns no single index can detect — for example, declining NDRE with stable NDVI flags early chlorophyll loss before canopy thinning occurs, and NDVI-SAVI divergence quantifies inter-row soil influence. Seasonal NDVI swing (summer peak minus winter baseline) provides a health benchmark: a vigorous vineyard shows a clear seasonal amplitude.
## Practical Applications for Vineyard Managers
### Selective & Differential Harvesting
Use vigor maps to plan harvest passes. Pick high-vigor zones separately from low-vigor zones to avoid blending overripe and underripe fruit. Many premium wineries now use vigor-based harvesting as a standard practice to elevate wine quality.
### Variable-Rate Management
Apply irrigation, fertilizer, and soil amendments at variable rates based on vigor zones. High-vigor areas may need reduced nitrogen and water to control excessive growth; low-vigor areas may benefit from targeted nutrition.
### Canopy Management Decisions
Schedule leaf removal, shoot thinning, and hedging interventions based on actual vigor data rather than calendar dates or visual estimates.
### New Block Planning
Before planting new vineyard blocks, analyze existing vegetation patterns to identify the best locations, rootstock choices, and row orientations for each section of your property.
## Why Satellite Data Beats Manual Scouting
Traditional vineyard scouting is essential — but it's also time-consuming, subjective, and difficult to perform consistently across large properties. One scout's "moderate vigor" is another's "slightly stressed." Satellite multi-index analysis provides:
- **Objective measurement:** Every pixel is quantified using the same four calibrated indices, eliminating human subjectivity across NDVI, NDRE, SAVI, and NDWI.
- **Complete coverage:** Every vine row is analyzed — not just the ones the scout had time to visit.
- **Multi-index insight:** Detect chlorophyll decline (NDRE) before canopy thinning (NDVI), quantify inter-row soil influence (SAVI vs. NDVI), and distinguish intentional deficit irrigation from damaging drought (NDWI + phenological context).
- **Historical record:** Satellite data creates an audit trail that can be revisited and compared across seasons.
- **Cost efficiency:** AgroReports' analysis is completely free — no hardware, no sensors, no subscription fees.
## Get Your Free Vineyard Performance Analysis
Whether you manage a small estate vineyard or oversee hundreds of hectares across multiple appellations, AgroReports' Vineyard Performance Analysis gives you the data-driven foundation for better viticultural decisions. Reports are generated in under 15 minutes.
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# Land Health Overview: Quick Satellite Snapshot of Your Property
URL: https://www.agroreports.org/articles/land-health-overview/
Published: 2025-11-05
Markdown mirror: https://www.agroreports.org/articles/land-health-overview.md
> Get a complete snapshot of your land's health with AgroReports' Land Health Overview. NDVI, NDWI, and key agricultural indicators in under 15 minutes — free.
How six satellite indices combined into one report give you the broadest single-snapshot assessment of your land's current health — in under 15 minutes and completely free
## Your Land's Health, at a Glance
Sometimes you don't need a deep-dive analysis spanning multiple years. Sometimes you just need a clear, reliable answer to one simple question: **how is my land doing right now?**
The Land Health Overview Report from AgroReports answers that question with the broadest index coverage of any report we offer. Drawing on the latest available satellite imagery, it evaluates **six distinct indices** — NDVI, NDWI, EVI, SAVI, NDMI, and MSAVI2 — to deliver a comprehensive assessment of current vegetation health, water stress, soil-adjusted conditions, and moisture content across your entire property. All in a single, easy-to-read report.
## What's Inside a Land Health Overview Report?
The report combines six satellite-derived indices — the broadest coverage of any AgroReports — to paint a complete picture of your land's current condition:
### 🌿 NDVI — Vegetation Health & Density
The Normalized Difference Vegetation Index reveals how much healthy, photosynthesizing vegetation is present. High NDVI zones are thriving; low NDVI zones may indicate bare soil, stressed plants, or poor emergence.
### 💧 NDWI — Water Stress & Moisture Content
The Normalized Difference Water Index measures the water content in vegetation and soil. Low NDWI values can indicate drought stress, poor irrigation coverage, or well-drained soils that may need attention during dry periods.
### 🔬 EVI — Enhanced Vegetation Index
The Enhanced Vegetation Index improves on NDVI by reducing atmospheric interference and correcting for soil background signals. EVI is especially valuable in areas with dense canopy or high biomass, where NDVI can saturate and lose sensitivity to real changes in vegetation.
### 🏜️ SAVI — Soil-Adjusted Vegetation Index
The Soil-Adjusted Vegetation Index corrects for bare soil brightness that can inflate standard NDVI readings. When SAVI and NDVI diverge significantly, the report flags potential bare soil interference — giving you a more honest picture of what's really growing versus what's just exposed ground.
### 💦 NDMI — Moisture Index
The Normalized Difference Moisture Index goes beyond NDWI by specifically tracking vegetation canopy water content rather than general surface moisture. Low NDMI values can signal that crops are drying from within — an early-warning indicator of water stress before wilting becomes visible to the naked eye.
### 📐 MSAVI2 — Modified SAVI
The Modified Soil-Adjusted Vegetation Index 2 takes soil correction a step further by dynamically adjusting for variable soil brightness across a field. MSAVI2 is particularly useful on properties with mixed soil types, uneven terrain, or patchy crop emergence — conditions that can throw off both NDVI and standard SAVI.
### 🗺️ Zone-Based Analysis
Your property is automatically divided into distinct zones based on vegetation patterns. The report highlights areas that are performing well, areas that need attention, and areas that may require on-the-ground investigation.
### 📊 Comparative Context
Your property's metrics are placed in regional context. See how your vegetation health compares to surrounding areas, helping you understand whether an issue is local to your land or part of a broader regional pattern.
## When to Use the Land Health Overview Report
This report is designed for speed and accessibility. It's ideal for:
- **Quick mid-season check-ins:** Between more detailed seasonal analyses, get a fast read on current conditions across all six indices.
- **Before-and-after monitoring:** Generate reports before and after key events — fertilization, irrigation changes, storm events — to measure impact across vegetation and moisture metrics.
- **New property assessment:** Evaluating a potential purchase? A Land Health Overview gives you an unbiased, multi-index look at current conditions.
- **Routine monitoring:** Set a cadence (weekly, bi-weekly, monthly) and track how conditions evolve through the season.
- **Compliance checks:** Verify that land is being maintained according to lease agreements, conservation easements, or regulatory requirements.
## How It Differs from Our Other Reports
While the **Seasonal Vegetation Report** tracks change over weeks and months, and the **Historical Trends Report** reveals multi-year patterns, the Land Health Overview is purpose-built for *immediate, broad-spectrum assessment*. It evaluates more indices than any other AgroReports — six in total — giving you the widest possible single-snapshot view of your land's condition. Think of it as your land's full vital signs panel — not just temperature and pulse, but the complete workup — captured in a single moment.
Despite its broad coverage, the report remains quick to generate and easy to interpret, making it the perfect entry point for anyone new to satellite-based agriculture monitoring.
## Beyond the Basics: NDMI and MSAVI2
What sets the Land Health Overview apart is its inclusion of two additional indices — NDMI and MSAVI2 — that go beyond the standard vegetation and water metrics found in other reports. **NDMI (Normalized Difference Moisture Index)** tracks moisture within the vegetation canopy itself, providing an early-warning signal for crop water stress before wilting is visible. **MSAVI2 (Modified Soil-Adjusted Vegetation Index 2)** dynamically corrects for variable soil brightness, making it more reliable than standard SAVI on properties with mixed soil types, uneven terrain, or patchy emergence.
Together, these six indices create a layered view of your land — from surface vegetation and soil exposure to internal plant moisture — helping you catch issues that a two-index scan would miss entirely.
## Example: Finding Hidden Water Stress
A farmer in central Spain ran a Land Health Overview Report on her mixed-crop property in late June. The NDVI map showed uniformly healthy vegetation — but the NDWI analysis told a different story. The eastern third of the property showed significantly lower water content than the rest, despite identical irrigation schedules.
A quick field inspection revealed a partially clogged irrigation line in that zone. The problem was fixed within 48 hours. Without the satellite overview, the issue might have gone unnoticed until crops showed visible wilting — at which point yield loss would have already begun.
## Get Your Free Land Health Overview Report
Whether you're checking in on your crops, evaluating a new property, or just curious about what satellite data can reveal, the Land Health Overview Report gives you the broadest single-snapshot assessment available — six indices covering vegetation health, moisture, soil-adjusted conditions, and more. Generated in under 15 minutes — completely free.
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# Historical Trends Report: Understand Your Land's Satellite History
URL: https://www.agroreports.org/articles/historical-trends/
Published: 2025-09-10
Markdown mirror: https://www.agroreports.org/articles/historical-trends.md
> Analyze long-term vegetation and water patterns with AgroReports' Historical Trends Report. 5 years of satellite data to detect shifts and track baselines.
How multi-year satellite analysis reveals the hidden trends shaping your land — tracking three indices (NDVI, NDWI, and EVI) across 5 years to detect vegetation change, water stress, and land degradation
## The Power of Looking Backward
A single snapshot of your land can tell you what's happening *right now*. But to truly understand the trajectory of your property — whether it's improving, degrading, or stable — you need historical context. The Historical Trends Report from AgroReports uses 5 years of satellite data to give you exactly that.
By analyzing long-term records of NDVI (vegetation health), NDWI (water content), and EVI (Enhanced Vegetation Index — more reliable than NDVI at high biomass density), this report reveals patterns that are invisible in any single season. Is your pasture slowly losing productivity? Are water resources becoming scarcer year by year? These are questions that only historical analysis can answer.
## What's Inside the Report
The report draws on archived satellite imagery from NASA's Landsat and ESA's Sentinel-2 programs, covering 5 years with two observation periods per year. It is structured into six clear sections designed to take you from the big picture down to specific, actionable steps:
### 1. Executive Summary
The most important long-term trend finding, stated upfront. You'll see whether your land is improving, declining, or stable — with the first-year and last-year average NDVI values and the exact numeric delta. If any year-over-year anomaly signals were detected, the most significant one leads the summary.
### 2. Field Distribution & Land-Use Context
An estimate of your field's area in hectares and acres with a land-use assessment. The report explains why a 5-year trend means something different for annual crops (which reset each year) versus perennial crops or grazing land (where stress compounds over time).
### 3. Current Ground Conditions — All Three Indices
A structured evaluation of NDVI, NDWI, and EVI — each with a clear verdict (Excellent / Good / Fair / Low / Critical), an interpretation of what the current index value means, and how it compares against the 5-year baseline. EVI (Enhanced Vegetation Index) is particularly valuable at high vegetation density, where NDVI can saturate and lose sensitivity.
### 4. 5-Year Trend Analysis & Change Detection
The heart of the report. A year-by-year averages table showing NDVI, NDWI, EVI, and the NDVI change versus the previous year. Automated anomaly detection flags year-over-year drops exceeding 0.08 (disturbance signal) and drops in the 0.05–0.08 range worth monitoring. The report also analyzes the seasonal swing — summer minus winter NDVI, where a healthy system shows >0.15 and a shrinking swing suggests degradation. Summer peak NDVI decline is tracked to detect whether productive capacity is eroding even if annual averages look stable.
### 5. Key Findings & Action Steps
A synthesis of the 5-year story presented in an easy-to-scan table with three columns: Finding, What It Means for You, and Next Action Step. Every finding references specific numeric values. If the overall trend is declining or any year-over-year drop exceeds 0.08, a blockquote warning follows the table with a specific recommendation.
### 6. Management Recommendations
Practical guidance organized across three time horizons: immediate actions for this week, short-term steps for this month, and a long-term strategy for the next 1–3 years. The long-term strategy is tailored to the detected trend — reversing decline, sustaining stability, or building on improvement — with recommendations referencing specific data points from the analysis.
## Who Benefits Most from Historical Analysis?
### Land Investors & Buyers
Before purchasing agricultural land, see the true productivity history — not just what the current owner tells you. A historical NDVI trend is an objective, data-driven due diligence tool.
### Large-Scale Farmers
Understand whether your management practices are improving or degrading land over time. Quantify the impact of changes like no-till adoption, cover cropping, or expanded irrigation.
### Environmental Consultants
Provide clients with hard evidence of environmental change. Whether you're assessing reforestation success, monitoring conservation easements, or documenting land degradation, satellite data is your most credible source.
### Government & NGO Land Managers
Monitor large areas for signs of desertification, deforestation, or declining water resources. Historical trend analysis supports evidence-based policy and intervention decisions.
## Example: Detecting Gradual Degradation
Consider a farm in southern Europe where the farmer has noticed "things just don't grow like they used to." A Historical Trends Report covering the past 5 years reveals a consistent decline in average NDVI across the period, with the year-over-year change exceeding -0.05 — crossing into the "declining" threshold. At the same time, the seasonal swing (summer minus winter NDVI) has been shrinking year after year, falling below the 0.15 healthy threshold, and the summer peak NDVI is trending downward. The trend is gradual enough to escape notice season by season, but unmistakable when the year-by-year table and anomaly signals are examined together.
Armed with this data, the farmer can make informed decisions — invest in water-efficient irrigation, switch to more drought-tolerant crop varieties, or explore groundwater alternatives — **before** the situation becomes critical.
## Reading Your Historical Trends Report
The report is designed to be accessible even if you have no background in remote sensing. Here are the key concepts and thresholds to understand as you read through each section:
- **Three vegetation indices:** The report evaluates NDVI (vegetation health), NDWI (water/moisture content), and EVI (Enhanced Vegetation Index — more reliable than NDVI at high biomass density). Each index provides a different lens on land condition.
- **Year-by-year averages table:** A structured table showing average NDVI, NDWI, and EVI for each year, plus the NDVI change versus the previous year. This is the fastest way to spot multi-year patterns.
- **Trend direction thresholds:** An NDVI change greater than +0.05 over the 5-year period indicates an improving trend. Within ±0.05 is considered stable (normal variability). A decline greater than 0.05 signals a declining trend that warrants attention.
- **Seasonal swing analysis:** The difference between summer and winter NDVI averages. A healthy seasonal crop system typically shows a swing above 0.15. A shrinking swing over successive years may indicate soil degradation, persistent stress, or land-use change.
- **Anomaly detection:** Year-over-year NDVI drops exceeding 0.08 are flagged as potential disturbance signals (possible drought, land-use change, or degradation). Drops in the 0.05–0.08 range are noted as worth monitoring. Summer peak NDVI decline is tracked separately to detect whether peak growing-season productivity is eroding.
- **Plain-English summary:** An executive summary that translates the data into clear, actionable findings — no jargon, no complex statistics.
- **Management recommendations:** Practical next steps organized into immediate, short-term, and long-term (1–3 year) horizons, tailored to your detected trend direction and the agricultural context of your location.
## Get Your Free Historical Trends Report
Understanding your land's past is the first step toward securing its future. AgroReports generates comprehensive Historical Trends Reports in under 15 minutes, drawing on 5 years of satellite data — completely free.
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# Seasonal Vegetation Report: Track Crop Health with Satellite NDVI
URL: https://www.agroreports.org/articles/seasonal-vegetation/
Published: 2025-07-15
Markdown mirror: https://www.agroreports.org/articles/seasonal-vegetation.md
> Learn how AgroReports' Seasonal Vegetation Report uses NDVI satellite data to track crop health through the growing season. Detect stress early and optimize yields.
How four satellite vegetation indices — NDVI, EVI, SAVI, and NDWI — track your crop's seasonal cycle, detect anomalies, and reveal whether your land is following its normal growth pattern
## Why Seasonal Vegetation Monitoring Matters
Understanding how vegetation health changes throughout the growing season is one of the most powerful tools available to modern farmers. The Seasonal Vegetation Report from AgroReports evaluates four satellite-derived vegetation indices — NDVI (canopy vigor), EVI (enhanced vegetation index that avoids NDVI saturation in dense crops), SAVI (soil-adjusted index that corrects for bare ground between rows), and NDWI (plant water content) — across two growing seasons to reveal how your crops are developing over time.
Unlike a single snapshot, seasonal analysis reveals **trends and patterns** that are invisible to the naked eye. A field that looks healthy during a mid-season walk-through might have experienced significant early-season stress that ultimately reduced yield potential — and without temporal data, you'd never know.
## The Four Indices That Power the Report
The Seasonal Vegetation Report evaluates four complementary satellite indices — not just NDVI. Each reveals a different dimension of crop health, and together they tell the full seasonal story:
- **NDVI (Vegetation Health):** 0.7–1.0 = dense, vigorous canopy; 0.5–0.7 = healthy active growth; 0.3–0.5 = moderate or early-stage; below 0.15 = bare soil or dormant.
- **EVI (Enhanced Vegetation Index):** More reliable than NDVI in dense canopies where NDVI saturates. A large NDVI-EVI divergence (>0.15) may flag soil interference or sensor saturation issues.
- **SAVI (Soil-Adjusted Vegetation Index):** Corrects for bare soil between crop rows — essential for row crops and early-season fields. When NDVI significantly exceeds SAVI, bare soil is inflating the NDVI reading.
- **NDWI (Water/Moisture Content):** Above 0.2 = well-irrigated; -0.1 to 0.0 = mild water stress; below -0.1 = significant stress requiring irrigation action.
By tracking these four indices over two growing seasons, the report builds a comprehensive picture of how each field performs — not just whether it's green, but whether that green is genuine canopy density or bare-soil illusion, and whether water stress is creeping in before visible symptoms appear.
## What's Inside the Seasonal Vegetation Report
### 🌱 Seasonal Swing Analysis
The report computes your land's **seasonal swing** — the difference between summer and winter NDVI averages. A healthy seasonal crop system shows a swing above 0.15. A shrinking swing over successive years may indicate soil degradation, persistent stress, or land-use change. An abnormally small swing (<0.05) suggests the field isn't following normal seasonal patterns.
### 📊 Year-over-Year Seasonal Comparison
A comparison table shows each season's NDVI, EVI, NDWI, and SAVI values — plus the deviation from the current reading. This isolates whether a change is a genuine multi-year trend or just seasonal variation, and reveals whether summer peak productivity is improving, declining, or stable across years.
### ⚠️ Automated Anomaly Detection
The report pre-computes anomaly signals against defined thresholds: NDVI deviations from seasonal averages (>0.08 below = significant underperformance), NDWI water deficit flags (below -0.1), NDVI-EVI divergence (>0.15 may indicate canopy saturation or soil interference), NDVI-SAVI divergence (bare soil inflating NDVI), and historical period-over-period NDVI drops (>0.1 = possible crop failure, harvest, or land-use change).
### 📈 Four-Index Ground Conditions Evaluation
Each index receives a structured assessment: what it measures (with interpretation ranges), a verdict (Excellent/Good/Fair/Low/Critical) in seasonal context, and a concrete management takeaway. A synthesis paragraph ties all four indices together — flagging when NDVI-EVI or NDVI-SAVI divergences indicate measurement artifacts rather than real vegetation changes.
## Real-World Applications
The Seasonal Vegetation Report is designed for a wide range of agricultural stakeholders:
### Row Crop Farmers
Track corn, wheat, soybean, and other row crops from emergence to harvest. Compare field performance year-over-year and identify underperforming zones.
### Pasture & Forage Managers
Monitor grass growth and plan rotation schedules based on actual biomass data rather than estimates.
### Agricultural Consultants
Provide clients with objective, data-backed insights about crop development trends across multiple growing seasons.
## How to Interpret Your Report
Each Seasonal Vegetation Report includes color-coded NDVI maps, time-series charts, and a plain-English summary of findings. Here's what to look for:
- **Trend direction:** Is NDVI increasing as expected? A flat or declining trend during the growth phase is a red flag.
- **Variability:** Large differences in NDVI within a single field suggest soil, drainage, or management issues.
- **Comparison to previous years:** How does this season stack up against the same period last year or a 5-year average?
- **Anomaly detection:** Sudden dips that don't follow the expected seasonal curve warrant field investigation.
## Get Your Free Seasonal Vegetation Report
Ready to see how your crops are performing across the growing season? AgroReports generates professional Seasonal Vegetation Reports in under 15 minutes — completely free. Simply outline your area of interest, select your date range, and receive a detailed analysis with actionable insights.
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# Making Satellite Data Accessible to Everyone
URL: https://www.agroreports.org/articles/welcome/
Published: 2025-06-01
Markdown mirror: https://www.agroreports.org/articles/welcome.md
> Learn about AgroReports' mission to democratize satellite agriculture monitoring and discover the powerful features that make professional land analysis accessible to everyone.
Discover the story behind AgroReports and our mission to democratize professional satellite agriculture monitoring
## Our Mission
AgroReports was born from a simple but powerful idea: professional satellite analysis shouldn't cost thousands of dollars or require specialized technical expertise. Every farmer, cooperative, and land owner deserves access to the same high-quality data that large agricultural enterprises use to make informed decisions.
We believe that by making satellite data accessible and understandable, we can help farmers worldwide optimize their resources, detect problems early, and make better decisions based on real, objective data rather than guesswork.
## The Problem We're Solving
Traditional agriculture monitoring faces several challenges:
- Visual field inspections can't detect water stress or nutrient issues until damage is already visible
- Commercial satellite monitoring services cost $3,000-10,000+ annually
- Free satellite data exists but requires technical expertise to interpret
- By the time problems are visible, yield loss has already begun
AgroReports bridges this gap by providing professional-grade analysis completely free, delivered in clear, actionable reports that anyone can understand.
## What Makes AgroReports Different
### 🚀 Speed & Simplicity
Reports are generated in under 15 minutes and delivered in plain English. No technical jargon, no complex interfaces — just clear insights you can act on immediately.
### 🌍 Professional Data Sources
We use the same high-quality satellite data from NASA and ESA that powers commercial agriculture monitoring services, ensuring scientific accuracy and reliability.
### 💚 Completely Free
All reports are available at no cost. We're committed to removing financial barriers to accessing satellite agriculture monitoring.
## Current Features
AgroReports currently offers several specialized report types, each designed for specific agricultural needs:
### Land Health Overview Report
Get a snapshot of current conditions across your property, focusing on key agricultural indicators like NDVI and NDWI.
### Seasonal Vegetation Report
Track how your vegetation health changes throughout the growing season and identify areas that are thriving or lagging behind.
### Historical Trends Analysis
Analyze long-term changes in vegetation and water patterns over multiple years to understand baseline conditions.
### Vineyard Performance Analysis
Specialized analysis for vineyard blocks, focusing on vigor variations to inform canopy management and harvest decisions.
## The Technology Behind AgroReports
Our platform combines multiple satellite indices (NDVI, NDMI, NBR, NDRE, SAVI, EVI) with geographic data from OpenStreetMap. Using artificial intelligence, we translate complex satellite analysis into clear, easy-to-understand reports written in plain English.
This approach ensures that whether you're a seasoned farmer or new to agriculture monitoring, you'll receive actionable insights without needing specialized training in satellite data interpretation.
## Looking Forward
We're continuously expanding AgroReports' capabilities. Coming soon are specialized reports for fruit trees and olive groves, automated monthly reports, and enhanced analysis for large agricultural areas.
Our roadmap is driven by the needs of our growing community of farmers and land managers. We regularly add new features and report types based on user feedback and requests.
## Join Our Community
AgroReports is trusted by 21+ farms across Europe, with over 95 reports generated covering 51,600+ hectares. Whether you manage a small vineyard or oversee large agricultural cooperatives, you're part of a growing community making data-driven decisions.
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