NDVI (Normalized Difference Vegetation Index)

NDVI is a fast, interpretable measure of vegetation vigor. It contrasts near-infrared (high reflectance for healthy leaves) with red (chlorophyll absorption) to produce values from -1 to 1.

Learning objectives

  • Explain what NDVI measures and typical value ranges.
  • Compute NDVI on Landsat or Sentinel-2 data in Earth Engine.
  • Mask and visualize NDVI meaningfully.
  • Interpret common NDVI pitfalls (clouds, scale/offset, phenology).
NDVI formula graphic
NDVI = (NIR - Red) / (NIR + Red), scaled from -1 (water) to +1 (healthy vegetation).

Quick win: NDVI on Landsat 8

// Landsat 8 SR: apply scale/offset
var img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210623')
  .multiply(0.0000275).add(-0.2);
var ndvi = img.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI');

Map.centerObject(img, 9);
Map.addLayer(ndvi, {min: 0, max: 0.8, palette: ['brown', 'yellow', 'green']}, 'NDVI');

// Inspect values
print('NDVI stats', ndvi.reduceRegion({
  reducer: ee.Reducer.minMax(),
  geometry: img.geometry(),
  scale: 30
}));

What you should see

A green-to-brown NDVI layer; console min/max near 0-0.8 in vegetated areas.

Value guide

  • -1 to 0: water, clouds, shadows.
  • 0 to 0.2: bare soil or sparse vegetation.
  • 0.2 to 0.5: moderate vegetation.
  • 0.5 to 0.9: dense, healthy vegetation.

Pro tips

  • Always mask clouds/shadows before computing NDVI.
  • Use native bands: Landsat 8 (B5 NIR, B4 red); Sentinel-2 (B8 NIR, B4 red).
  • Keep visualization ranges modest (for example, min 0, max 0.8) to avoid washed-out maps.

Try it: extend the workflow

Swap to Sentinel-2 SR and recompute NDVI (B8, B4).

Threshold NDVI at 0.3 to mask vegetation: ndvi.gte(0.3).

Plot a time series of mean NDVI over an AOI using ui.Chart.image.series.

Common mistakes

  • Forgetting scale/offset on surface reflectance products.
  • Interpreting cloudy pixels as low NDVI; mask first.
  • Comparing NDVI across sensors without consistent preprocessing.

šŸŒ Real-World Application

Agricultural Monitoring: The USDA uses NDVI from satellites to monitor crop health across millions of acres. Farmers can detect drought stress, pest damage, or nutrient deficiencies weeks before visible symptoms appearā€”saving crops and reducing costs.

Deforestation Tracking: Brazil's PRODES program uses NDVI time series to detect illegal deforestation in the Amazon, triggering enforcement actions within days of clearing.

Quick self-check

  1. Why does NDVI use NIR and red?
  2. What NDVI range suggests healthy vegetation?
  3. What should you do before computing NDVI on a cloudy scene?

This module builds on

Next steps