Enhanced Vegetation Index (EVI)
The Enhanced Vegetation Index (EVI) is an optimized spectral index that improves on NDVI by correcting for atmospheric aerosol effects and soil background reflectance, providing more accurate vegetation health measurements in areas with dense canopy or variable soil conditions.
The Enhanced Vegetation Index (EVI) is an advanced vegetation index developed to address known limitations of NDVINDVIThe Normalized Difference Vegetation Index (NDVI) is a widely used spectral index that quantifies vegetation health a..., particularly its sensitivity to atmospheric aerosols and soil background reflectance and its tendency to saturate in densely vegetated areas. EVI incorporates blue band reflectance in addition to near-infrared and red bands, using the formula: EVI = G × (NIR - Red) / (NIR + C1 × Red - C2 × Blue + L), where G is a gain factor, C1 and C2 are aerosol correction coefficients, and L is a canopy background adjustment. Advantages over NDVIEVI reduces atmospheric influences by using the blue band to correct for aerosol scattering. The canopy background adjustment term (L) minimizes soil reflectance effects in areas with sparse vegetation. Critically, EVI maintains sensitivity in high-biomass regions where NDVI saturates, making it more effective for monitoring tropical forests, dense croplands, and highly productive ecosystems. ApplicationsEVI is the standard vegetation index for NASA's MODIS sensor products and is widely used in global vegetation monitoring, carbon cycle modeling, land surface phenology, and agricultural yield estimation. It is particularly valuable in tropical regions where persistent aerosol haze and dense canopy cover compromise NDVI accuracy. EVI represents an important advancement in quantitative vegetation remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi..., providing more accurate and consistent vegetation measurements across diverse environments. Its adoption in global monitoring programs underscores its value for ecological research and agricultural management.
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