Publication: Optimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning In Northwest Shandong Plain, China
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Date
2025-08
Authors
Hou, Chenglei
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Abstract
In semi-humid regions, soil moisture (SM) plays a crucial role in influencing hydrological cycle processes and energy balance. While machine learning (ML) and Synthetic Aperture Radar (SAR) offer potential for SM information retrieval, their performance and applicability are limited by challenges such as feature selection and sensitivity to environmental variations across different seasons. This challenge is exacerbated by the lack of comprehensive frameworks that effectively integrate multiple polarimetric parameters with traditional backscatter coefficients, which could improve the accuracy of SAR-based SM retrieval. Hence, this research aims to develop an innovative method that combines advanced polarimetric decomposition techniques with ML algorithms for robust SM retrieval in the Northwest Shandong Plain, China.
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Optimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning , Northwest Shandong Plain , China