NDVI
Normalized Difference Vegetation Index · Also known as: NDVI
The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitoring, drought assessment, crop productivity forecasting, and land cover change detection.
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When to use it
Use NDVI to monitor vegetation health at regional to global scales, assess drought impacts, forecast crop yields, and detect land cover changes. NDVI works best for large-scale assessments (pixel resolution >10 m) and is less useful for local-scale management where field-level detail is needed. Combine NDVI with other indices (EVI, SAVI) for robustness and with precipitation/temperature data for interpreting drivers.
Strengths & limitations
- Simple and computationally fast, requiring only two satellite bands widely available across sensors and archives
- Spatially continuous, enabling assessment across regions lacking field observations or ground infrastructure
- Strong empirical relationship to leaf area index (LAI), photosynthesis rates, and biomass
- Long temporal archive (Landsat since 1972, MODIS since 2000) enabling decadal trend analysis
- Saturates at high vegetation density; two dense forests with different structure appear similar in NDVI
- Sensitive to atmospheric effects (aerosols, clouds) that introduce noise unless carefully corrected
- Cannot distinguish vegetation type or quality; healthy weeds appear similar to healthy crops
- Temporal resolution depends on cloud cover and satellite revisit frequency; gaps occur in cloudy regions
Frequently asked
Why use NDVI instead of just looking at red reflectance?
Red reflectance alone doesn't account for soil background. A dark soil and a sparse plant canopy both have low red reflectance. By normalizing the difference between NIR and red by their sum, NDVI isolates the vegetation signal from soil. The resulting index correlates strongly with leaf area, photosynthesis, and biomass.
What are typical NDVI values for different land covers?
Water and snow: < 0.1; bare soil: 0.1–0.2; grass and shrubland: 0.2–0.5; crops (depending on growth stage): 0.3–0.8; dense forest: 0.6–0.9. Values vary seasonally (low in winter, peak in mid-summer in temperate regions).
How does cloud cover affect NDVI monitoring?
Clouds block the satellite view entirely; cloudy pixels are masked and must be interpolated or skipped. In tropical regions with persistent cloud cover, useful NDVI data may be infrequent (< 50% of observations clear). Radar-based indices (not affected by clouds) like NDVI-derived backscatter provide alternatives in cloudy areas.
Can NDVI distinguish between crop types?
Not directly. NDVI reflects greenness, not crop identity. Wheat and barley have similar NDVI trajectories. However, phenological timing (peak NDVI date, growing season length) can differ among crops, enabling crop classification when combined with weather data or field observations.
Sources
- Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite Symposium Proceedings, 1, 309-317. link ↗
- Jackson, R. D. (1983). Spectral indices in n-space. Remote Sensing of Environment, 13(5), 409-421. DOI: 10.1016/0034-4257(83)90010-X ↗
How to cite this page
ScholarGate. (2026, June 3). Normalized Difference Vegetation Index. ScholarGate. https://scholargate.app/en/geophysics/ndvi
Which method?
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