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    NDVI

    Also called: Normalized Difference Vegetation Index

    Definition

    NDVI (Normalized Difference Vegetation Index) is a satellite- or drone-derived measurement of crop vigor calculated as (NIR − Red) / (NIR + Red). Values range from -1 to +1, with healthy vegetation typically between 0.4 and 0.9. NDVI is the most widely used remote-sensing index in agriculture because it correlates strongly with biomass, chlorophyll content, and yield potential.

    How NDVI Works

    NDVI exploits a physical property of healthy plants: chlorophyll absorbs most red light for photosynthesis and reflects most near-infrared (NIR) light. The greater the contrast between NIR and Red reflectance in a pixel, the healthier the vegetation. Bare soil typically has NDVI between 0.1 and 0.2, stressed crops 0.2 to 0.4, and vigorous crops 0.5 to 0.9.

    In precision agriculture, NDVI is used for three main workflows: (1) weekly in-season crop monitoring to spot stress zones before the eye can see them, (2) variable rate application (VRA) of nitrogen — zones with low NDVI get more fertilizer — and (3) yield-potential mapping at the end of the season. Free NDVI imagery is available from Sentinel-2 (10 m resolution, 5-day revisit) and Landsat (30 m, 16-day revisit); commercial platforms offer sub-meter drone-derived NDVI.

    NDVI has known limitations: it saturates at high biomass (values above ~0.8 are hard to differentiate), it cannot distinguish between nitrogen stress and water stress, and cloud cover can make satellite imagery unusable for days. Advanced indices like NDRE, EVI, and SAVI address some of these limits. WiseYield combines Sentinel-2 NDVI with soil and weather data to deliver actionable zone recommendations, not just raw index maps.

    Sources

    1. Rouse et al. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA.
    2. Sentinel-2 User Handbook, European Space Agency (2023).

    Related Terms

    Precision Agriculture
    Technology
    Variable Rate Application
    Technology
    Remote Sensing
    Technology
    Back to all glossary terms

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