Abstract
The main objective of this study is to develop an inversion technique based on neural networks to estimate soil surface moisture and leaf area index (LAI) in irrigated grasslands by combining fully polarimetric RADARSAT-2 C-band SAR and optical data (LANDSAT). The benefits of having data in dual-polarization or in full-polarization mode for the SAR images were evaluated in comparison to the single-polarization mode. In addition, the use of polarimetric parameters, mainly Shannon entropy and Pauli components, was also studied. In addition, 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 is the polarization most relevant to soil moisture estimates (RMSE∼6 vol.%). The use of in situ FAPAR and FCover only improved the estimate of LAI (RMSE∼0.37 m2/m2. The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters.
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