Corn chlorophyll estimation with in situ collected hyperspectral reflectance data

Canopy reflectance of corn was collected with ASD FieldSpec during different growing stages, and simultaneously, contents of chlorophyll a and b are acquired. Vegetation indices of ratio analysis of reflectance spectra (RARS) and pigment-specific simple ratio (PSSR) are used to analyze the relationship between corn canopy hyperspectral reflectance and chlorophyll a and b content. It is found that RARS and PSSR arc useful vegetation indices for detecting chlorophyll content with hyperspectral Reflectance data. Still it was found that Backforward propagation (BP) neural network was an efficient data analyzing tools for predicting corn chlorophyll content with In-situ collected hyperspectral reflectance data. With the help of BP neural network, the estimation precision of corn chlorophyll with hyperspectral reflectance data could be improved greatly. Still more sample data need to be collected to calibrate the BP- neural network established for estimation corn chlorophyll content with hyperspectral reflectance data