Retrieval of paddy rice variables during the growth season with a modified water cloud model on polarimetric radar images

Abstract

This paper proposed a modified Water Cloud Model (MWCM) for rice variable estimation during the whole growth season with eight RADARSAT-2 quad-pol SAR images. The improvements achieved with the MWCM include considering the heterogeneity of water content of the rice canopy in different directions and different phenologies, and applying the scattering components from an improved polarimetric decomposition in the model instead of the backscattering coefficients. With the MWCM, four rice variables were estimated through the genetic algorithm, including leaf area index (LAI), rice height (h), volumetric water content of total canopy (mv) and ear biomass (De). The validation was conducted using the field data with the average R2 of each variable above 0.8. The median relative error (MRE) of the rice variables ranged from 9% to 15% in most phenological stages. The results demonstrated that the MWCM works well for the estimation of rice biophysical parameters with polarimetric SAR data, and it is significant to consider the heterogeneity of water content of the rice canopy in the horizontal direction for estimation of rice variables during the whole rice growth season.