A multi-year cross-validation experiment for estimating rice plant area index (PAI) over the JECAM-India test site from simulated RADARSAT constellation mission (RCM) compact polarimetric SAR data

Abstract

Using the cross-validation approach, strategies for estimating biophysical parameters are still pre-operational with synthetic aperture radar (SAR) data. In this regard, the Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR inter-comparison experiments provide an opportunity for the potential implementation of cross-validation strategies for biophysical parameters retrieval utilizing the next-generation compact polarimetric (CP) modes available from the RADARSAT Constellation Mission (RCM). This work first uses the conventional semi-empirical Water Cloud Model (WCM) modified by exploiting the scattering power decompositions of CP measurements to estimate the Plant Area Index (PAI) for rice. The modified WCM (MWCM) is then inverted using the scattering power components from the (Formula presented.) decomposition. We compare the PAI estimates using MWCM- (Formula presented.) between the estimates obtained from (1) the conventional WCM using the RH and RV backscatter intensities and (2) MWCM- (Formula presented.) decomposition scattering powers. We exploit a time series of simulated compact-pol SAR data over the JECAM test site in Vijayawada, India, throughout 2018 and 2019. We use the C-band RADARSAT-2 full-pol data to simulate the RADARSAT Constellation Mission (RCM) compact-pol mode data. Utilizing the advantage of systematically collected multi-year SAR data and in-situ measurements, the present research also assesses the calibrated model transferability performances to another data set and cross-validation of a model in a multi-year experiment setting. The comparative analysis indicates potential improvements in PAI estimation with MWCM- (Formula presented.) scattering powers. A high range of correlation coefficient ((Formula presented.)) between the estimated and observed PAI is observed with good Root Mean Square Error (RMSE) of (Formula presented.) m² m⁻², and Mean Absolute Error (MAE) of (Formula presented.) m² m⁻².