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Abstract Hyperspectral analysis of vegetation involves obtaining spectral reflectance measurements in hundreds of bands in the electromagnetic spectrum. These measurements may be obtained using hand‐held spectroradiometers or hyperspectral remote sensing instruments placed onboard aircraft or satellites. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, biomass, chlorophyll, and leaf nutrient concentration which are used to understand ecosystem functions, vegetation growth, and nutrient cycling. This article first reviews hyperspectral remote sensing and then describes current modeling and classification techniques used to estimate and predict vegetation type and biophysical characteristics.
Im et al. (Sat,) studied this question.