NDVI, Explained
NDVI is the satellite vegetation index behind most 'crop health' maps — a canopy-greenness score from red and near-infrared light. Here's what it actually measures, what it's good for, and where it fools people.
What NDVI is
NDVI — Normalized Difference Vegetation Index — is a canopy-greenness score computed from satellite imagery. Healthy leaves absorb red light for photosynthesis and strongly reflect near-infrared. NDVI compares the two:
NDVI = (NIR − Red) ÷ (NIR + Red)
The result runs from −1 to +1. Bare soil sits low (roughly 0.1–0.2), and a closed, healthy corn canopy in July commonly reads 0.7–0.9. Most "crop health" layers in ag software are NDVI or a close cousin.
What it's actually good for
- Change and contrast, not diagnosis. NDVI reliably shows where a field differs from itself — the sand knob, the drowned-out hole, the nitrogen-pale strip — and when a field drops against its own recent trend. It cannot tell you why; that's what boots in the field are for.
- A scouting director. The honest workflow is: satellite flags the anomaly, you walk to the anomaly. Ten minutes of walking beats an hour of guessing which corner deserves it.
- Season-over-season memory. The same acres showing the same weak zone three years running is a management zone talking to you.
Where it fools people
- Saturation. Once canopy closes, NDVI flattens near its ceiling — two fields can read identically while yielding 40 bushels apart. Mid-season "everything's 0.85" maps carry little information.
- Clouds and haze contaminate readings; a sudden one-image "crash" is more often a cloud shadow than a crop collapse. Filter by cloud cover before reacting.
- Green isn't grain. Weeds read healthy. A lush waterhemp patch scores beautifully.
- Timing beats intensity. A drop in late June means something very different than the natural decline of maturity in September.
Used with those limits in mind, NDVI is one of the cheapest early-warning systems an operation can run — Clevis pulls an NDVI timeline for every field and flags drops against the field's own baseline, so the anomaly finds you instead of the other way around.
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