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A Study on Soil Moisture Inversion in Hetao Region of Inner Mongolia Using Sentinel-1 Data over Past Decade
LIYaochen, HANXiantao, WANGYing, LILu
Chin Agric Sci Bull ›› 2026, Vol. 42 ›› Issue (17) : 113-120.
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Abbreviation (ISO4): Chin Agric Sci Bull
Editor in chief: Yulong YIN
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A Study on Soil Moisture Inversion in Hetao Region of Inner Mongolia Using Sentinel-1 Data over Past Decade
This study evaluates the potential of Sentinel-1 synthetic aperture radar (SAR) data for retrieving surface soil moisture in the Hetao region of Inner Mongolia, a typical arid and semi-arid agricultural area, over a 10-year period from 2015 to 2024. A total of 99 Sentinel-1 GRD images were processed to obtain backscattering coefficients for both VV and VH polarizations. The backscattering coefficients were correlated with in-situ soil moisture measurements from 10 meteorological stations at depths of 10 cm and 20 cm, including volumetric water content, gravimetric water content, effective water storage, and relative humidity. The water cloud model (WCM) was applied to assess its effectiveness in vegetation correction. The results demonstrate that the VV-polarized backscattering coefficient exhibits a strong negative correlation with soil moisture at 10 cm depth, with coefficients of determination (R²) exceeding 0.75 across all moisture metrics, significantly outperforming VH polarization. In contrast, correlations with 20 cm soil moisture were weak, confirming the limited penetration depth of C-band SAR. Seasonal analysis reveals distinct temporal patterns: the highest correlations occur in July and August, while the lowest correlations appear in May during the crop emergence phase. Land cover comparison indicates robust performance across both cropland (R²=0.80) and grassland (R²=0.83) sites, though croplands exhibit stronger monthly variability due to crop phenological dynamics. The WCM showed negligible improvement in retrieval accuracy when using default empirical parameters, suggesting the need for locally calibrated vegetation parameters. These findings confirm that Sentinel-1 VV polarization provides reliable surface soil moisture estimates (0-10 cm) in semi-arid agricultural regions, with performance strongly modulated by seasonal precipitation patterns and land cover type. The methodology enables operational soil moisture monitoring to support irrigation scheduling and drought early warning in data-scarce regions.
soil moisture / Sentinel-1 SAR / retrieval / the Hetao region / data fusion / machine learning / WCM
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