Integration of SMAP L-band VOD and Multi-source Satellite Data for Improved Sentinel-1 Soil Moisture Retrieval in Complex Terrains

Sentinel-1 SAR enables high-resolution soil moisture estimation using the C-band backscatter coefficient. To account for attenuation and volume scattering effects in densely vegetated areas, soil moisture retrieval methods using the Water Cloud Model (WCM) are widely employed. Traditional WCM utilizes the Normalized Difference Vegetation Index (NDVI) and C-band Radar Vegetation Index (RVI) as vegetation parameters, which have limitations due to the saturation of the NDVI and low penetration of C-band. To overcome these problems, this study introduced SMAP L-band Vegetation Optical Depth (VOD) as a vegetation parameter for WCM and applied it to the complex mountainous terrain of the Korean Peninsula. In the parameter estimation process of the WCM, the quantitative relationship between in-situ observations and soil texture was established, enabling the dynamic spatial extension of model parameters to ungauged regions. The validation results with in-situ soil moisture data showed improved correlation coefficient R and ubRMSE compared to existing WCM methods. It was found to enhance the accuracy of soil moisture estimation by more precisely correcting signal attenuation caused by vegetation in complex terrain. This study demonstrates the validity of high-resolution hydrological parameter estimation in complex terrain through satellite data fusion and is expected to provide essential foundational information for precise drought monitoring and water resource management in the future.

 

Keywords: Soil Moisture, Sentinel-1, Vegetation Optical Depth, Water Cloud Model, Multi-source fusion, Complex terrain

 

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Climate, Energy and Environment (MCEE)(RS-2023-00230286). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339).