Assessment of Hyperspectral Indices-Based Chlorophyll Models for Maize Crop
Abstract
Chlorophyll is the main parameter of crops which directly influence the photosynthetic activity, overall health and also act as indicators of mutation, stress and nutritional status. Therefore, the estimation of the accurate chlorophyll content of the leaf leads to the significant assessments of crop growth, stress by disease and nutritional deficiency. Hyperspectral remote sensing with higher spatial resolution is widely used for the precise estimation of the chlorophyll. Present analysis focusses on examining the relationship between observed and simulated chlorophyll reflectance in chlorophyll estimation. Several hyperspectral chlorophyll indices were used to retrieve the leaf chlorophyll contents namely normalised difference vegetation index (NDVI), Modified simple ratio index (mSR), Modified chlorophyll absorption ratio index (MCARI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI) and the integrated forms (MCARI/SAVI and TCARI/SAVI).
