The objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric synthetic aperture radar (SAR) and optical data. A huge data set of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual-, and full-polarizations configurations) and the normalized difference vegetation index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters [Shannon entropy (SE) and Pauli components] on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested. The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. A root-mean-square error (RMSE) for soil moisture estimates of approximately ό vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately 0.37 m2/m2. The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and 1.5 kg/m ² , respectively (RMSE is of 0.38 kg/m ² for BIO and 0.32 kg/m ² for VWC). In addition, a high underestimate was observed for BIO and VWC higher than 2 and 1.5 kg/m ² , respectively, (a bias of -0.65 kg/m ² on BIO estimates and -0.49 kg/m ² on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm.

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