Remote Estimation of Crop Biophysical Parameters: A Novel Three Channel Band Ratio Approach

Green Leaf Area Index (GLAI) and Green Biomass (GBM) are two of the fundamental biophysical properties indicating vegetation physiological status. In the current research a new three channel normalized band ratio algorithm i.e. "Weighted Normalized Crop Index (WNCI)" is proposed for remote estimation of vegetation GLAI and GBM. WNCI takes into account three of the most common vegetation sensitive spectral channels including Green, Red and Near Infra Red (NIR) in combination with the incorporation of a weighting coefficient to NIR reflectance in quantifying GLAI and GBM. The spectral reflectance characteristics of two crop species including rice and wheat, grown in different environmental conditions across three growing seasons have been analyzed and this confirms the better predictive ability of WNCI compared to three of the most common vegetation Indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Wide Dynamic Range Vegetation Index (WDRVI) and Visible Atmospherically Resistant Index (VARI). Standard statistical analysis illustrates a consistent lower standard error and greater linearity of WNCI to observed GLAI and GBM estimates. A relative sensitivity analysis carried out between WNCI and the rest of the VIs demonstrates a higher sensitivity of WNCI especially with respect to higher crop density (GLAI ≥ 3 and GBM ≥ 1000kg/ha). KeywordsProximal Sensing, Vegetation Index, Leaf Area Index, Green Biomass, Hyperspectral Remote Sensing