Soil Moisture Retrieval in Slow-Moving Landslide Region Using SAOCOM <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-Band: A Radiative Transfer Model Approach
Radiative transfer models have been extensively applied in soil moisture studies; however, their application to <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-band synthetic aperture radar (SAR) data has not been fully explored. This rese...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045878/ |
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| Summary: | Radiative transfer models have been extensively applied in soil moisture studies; however, their application to <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-band synthetic aperture radar (SAR) data has not been fully explored. This research introduces a comprehensive approach for soil moisture retrieval using SAOCOM <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-band SAR dual-polarization data (VV–VH). The novel bistatic radiative transfer modeling framework (RT1) is used, validated previously with Sentinel-1 <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula>-band SAR and advanced scatterometer (ASCAT) data. For the first time, the RT1 model is applied to SAOCOM <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-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 <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-band SAR data (<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\lambda } = \text{23 cm}$</tex-math></inline-formula>) is conducted, with the model’s performance evaluated against multiple regional-scale soil moisture datasets, including ASCAT, ERA-5 Land, and soil moisture active passive. 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 (<inline-formula><tex-math notation="LaTeX">$r \geq \text{0.67}$</tex-math></inline-formula>, RMSE = 0.0936 m<sup>3</sup>/m<sup>3</sup>, MSE = 0.088 m<sup>3</sup>/m<sup>3</sup>, and Bias = –0.0603 m<sup>3</sup>/m<sup>3</sup>) 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 <italic>L</italic>-band SAR and radiative transfer modeling enhancing retrieval in complex and agricultural terrains for improved landslide monitoring. |
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| ISSN: | 1939-1404 2151-1535 |