The Normalized Difference Vegetation Index (NDVI) is a number from −1 to 1 that shows how green and healthy vegetation is, calculated from satellite or drone imagery. It's the most widely used vegetation index in remote sensing, behind crop monitoring, drought maps, tree canopy studies and urban heat analysis.
How NDVI works
Healthy leaves do two things with sunlight. Chlorophyll absorbs red light for photosynthesis, and the internal structure of the leaf strongly reflects near-infrared (NIR) light, which our eyes can't see. Stressed or sparse plants reflect more red and less NIR. NDVI compares the two:
NDVI = (NIR − Red) ÷ (NIR + Red)
Dividing by the sum "normalizes" the result, so it stays between −1 and 1 whatever the overall brightness of the scene.
Worked examples
Using typical reflectance values (the share of light reflected, from 0 to 1):
| Surface | NIR | Red | NDVI |
|---|---|---|---|
| Healthy, dense crop | 0.50 | 0.08 | 0.72 |
| Stressed crop | 0.30 | 0.12 | 0.43 |
| Bare soil | 0.25 | 0.20 | 0.11 |
| Urban surfaces | 0.20 | 0.17 | 0.08 |
| Water | 0.02 | 0.05 | −0.43 |
For the healthy crop: (0.50 − 0.08) ÷ (0.50 + 0.08) = 0.42 ÷ 0.58 = 0.72.
What the values mean
- Below 0: water, snow and clouds.
- 0 to about 0.2: bare soil, rock, sand and built-up areas.
- About 0.2 to 0.5: sparse vegetation, grassland, or stressed and early-season crops.
- Above about 0.6: dense, healthy vegetation such as forest and crops at peak growth.
These ranges are guides, not rules. NDVI is most useful for comparison: the same field this year against last year, or one neighbourhood's tree cover against another's.
Where NDVI data comes from
| Satellite | Red band | NIR band | Resolution | Revisit |
|---|---|---|---|---|
| Sentinel-2 (ESA) | B4 | B8 | 10 m | About 5 days with two satellites |
| Landsat 8 and 9 (USGS/NASA) | B4 | B5 | 30 m | 8 days combined |
| MODIS (NASA) | Band 1 | Band 2 | 250 m | Daily |
Landsat's archive goes back to the 1980s, which makes it the go-to source for long-term change. Drones with multispectral cameras give centimetre-level NDVI for individual fields.
What people use NDVI for
- Agriculture: finding stressed parts of a field before they show to the eye.
- Drought monitoring: comparing this season's greenness against normal.
- Urban heat and tree canopy: low NDVI often lines up with the hottest streets, which is why it's used in heat vulnerability maps.
- Habitat and land cover change: tracking forest loss, regrowth and wetland health.
- Wildfire recovery: watching burned land green up over time.
Limits to keep in mind
- Clouds block the view and must be masked out; cloud shadows lower NDVI.
- Saturation: in very dense vegetation NDVI levels off, so it stops telling dense forests apart. EVI is often used instead; see the spectral indices cheat sheet.
- Soil background: in sparse vegetation, bright soil skews values. SAVI adjusts for this.
- Season: compare images from the same time of year, or every change looks like a trend.
Frequently asked questions
Can I calculate NDVI from a normal photo?
No. Normal cameras don't record near-infrared. You need multispectral imagery.
Is higher NDVI always better?
For crop vigour, usually. But a high NDVI can also mean weeds or invasive species, so it needs interpretation.
How often can NDVI be updated?
As often as clear images arrive: every few days with Sentinel-2, cloud permitting.
Want to see vegetation change in your area?
We'll map it from satellite imagery.
Field stress, canopy loss, habitat change or heat: tell us the place and the question.