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Optimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning In Northwest Shandong Plain, China

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dc.contributor.authorHou, Chenglei
dc.date.accessioned2026-09-15T09:09:01Z
dc.date.available2026-09-15T09:09:01Z
dc.date.issued2025-08
dc.description.abstractIn 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.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/25014
dc.language.isoen
dc.subjectOptimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning
dc.subjectNorthwest Shandong Plain
dc.subjectChina
dc.titleOptimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning In Northwest Shandong Plain, China
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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