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Radiative transfer models have been extensively applied in soil moisture studies; however, their application to L-band SAR data has not been fully explored. This research introduces a comprehensive approach for soil moisture retrieval using SAOCOM L-band SAR dual-polarization data (VV-VH). The novel bistatic radiative transfer modeling framework (RT1) is used, validated previously with Sentinel-1 C-band SAR and ASCAT scatterometer data. For the first time, the RT1 model is applied to SAOCOM L-band data over the Petacciato landslide area in Italy, covering the period from January 2021 to December 2023. A statistical comparison of soil moisture estimates derived from L-band SAR data (λ = 23 cm) is conducted, with the model's performance evaluated against multiple regional-scale soil moisture datasets, including ASCAT, ERA-5 Land, and SMAP. Validation is performed using soil moisture time series and advanced statistical methods. The study incorporates the Antecedent Precipitation Index (API), calculated from precipitation in the days leading up to an event, as an indicator of soil moisture, helping assess retained moisture from prior rainfall. The proposed methodology exhibits high accuracy, as evidenced by a strong correlation (r ≥ 0.67, RMSE = 0.0936 m3 /m3, MSE = 0.088 m3 /m3, and Bias = –0.0603 m3 /m3) between the RT1 soil moisture retrieval and reference datasets, such as ASCAT data. This approach provides a reliable tool for continuous soil moisture monitoring in landslide-prone regions, with SAOCOM L-band SAR and radiative transfer modeling enhancing retrieval in complex and agricultural terrains for improved landslide monitoring.
Rana et al. (Wed,) studied this question.