NDVI in plain words: how to read a vegetation map
Our remote sensing engineers teach models to tell drought from disease on satellite imagery. But half of our conversations with farms start with a simpler question: “What do these colors even mean?” Fair question — so here’s the answer we give over tea, not the one from a textbook.
What NDVI actually measures
A healthy plant does two things with light: it absorbs red (chlorophyll eats it for photosynthesis) and reflects near-infrared strongly. A stressed or sparse plant absorbs less red and reflects less infrared. NDVI is simply the ratio of that difference — one number per pixel, from -1 to 1.
Translated to farming: NDVI measures how much actively photosynthesizing biomass the satellite sees in each pixel. Not “health” directly, not yield, not nitrogen — biomass activity. Every practical use grows out of remembering exactly this.
How to read the colors
Maps color pixels from red (low) through yellow to deep green (high). Ranges vary by crop and season, but as a working scale for field crops mid-season:
| NDVI | What the satellite sees | What it usually means |
|---|---|---|
| 0–0.2 | Bare soil, water, stubble | Normal before emergence; alarming in June |
| 0.2–0.4 | Sparse or young vegetation | Early stages — or thinning, weeds dying off, stress |
| 0.4–0.6 | Moderate canopy | Active growth; compare against the field’s own history |
| 0.6–0.9 | Dense, active canopy | Peak vegetation — or lush weeds, to be honest |
Five reasons a zone “turns red” — not all of them trouble
- Moisture stress. The most common mid-season cause — often shows in NDMI (the moisture index) days before NDVI reacts.
- Disease or pests. Usually a spreading patch with soft edges that grows between images.
- Seeding gaps or thinning. Sharp geometric shapes — straight lines and rectangles are almost always machinery, not biology.
- Soil differences. The same corner is weaker every single year — sandy patch, salinity, old field road. History exposes it instantly.
- A cloud or its shadow. The most popular false alarm. Check the image date and the cloud mask before sounding any alarms.
Notice the pattern: the shape and the history of a zone say more than its color. Geometry points to machines, repetition points to soil, movement points to biology.
What NDVI can’t see — and what covers for it
- Through clouds. Optical satellites see clouds, not fields. Radar (Sentinel-1) fills those gaps — it doesn’t care about weather.
- Early stress before biomass reacts. Water stress shows in NDMI earlier; that’s why Planva shows several indices, not NDVI alone.
- The cause. NDVI says “this zone differs.” Why — drought, fungus, or a clogged coulter — is decided by a scout with boots on. The map’s job is to make that walk short and targeted.
- Overly dense canopies. Above ~0.8 NDVI saturates: a good field and a great field look the same. EVI handles dense canopies better.
NDVI doesn’t answer questions. It asks them precisely — which is worth more.
How this works in practice
The healthy weekly routine on a monitored farm takes ten minutes: open the fresh image, sort fields by NDVI change, look at the three that dropped, check their weather and history, send a scout to the one that has no innocent explanation. In Planva, the AI does the sorting part for you — it flags the drop and drafts the scouting task itself.
If you’re still weighing whether any of this is worth it on your hectares, start with what farming without monitoring actually costs — and when you’re ready to try, here’s the first-season playbook.
Common questions
Depends on the growth stage: 0.3–0.5 during tillering can be normal, while 0.7–0.85 is typical at peak vegetation. That’s why comparing a field against its own history and stage matters more than chasing an absolute number.
See Planva on your fields
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