Utilizing Dual Polarimetric SAR Datasets for NDVI Modeling in Sugarcane Growth Assessment

The primary objective of this study is to develop a model that relates optical datasets with synthetic aperture radar (SAR) datasets for sugarcane crop. This investigation is motivated by the limitation regarding unavailability of optical datasets during cloudy or rainy conditions, as optical sensors are unable to penetrate cloud cover, while SAR sensors are capable of doing so. To achieve this, the study establishes correlation between the normalized difference vegetation index (NDVI) for sugarcane with key parameters of dual polarimetric SAR dataset including backscattering parameters and vegetation indices namely Radar Vegetation Index (RVI) and the Dual Polarization Radar Vegetation Index (DpRVI). The regression analysis was performed to evaluate the relationship between NDVI and the observed SAR parameters in relation to overall crop growth dynamics. Based on the coefficient of correlation, it was found that the backscattering coefficient corresponding to VH polarization shows better correlation with NDVI (R=0.72) compared to VV polarization (R=0.22). Other SAR parameters, such as VH × VV, VH/VV, RVI, and DpRVI, were also found to have a significant correlation with NDVI.