Retrieval of Soil Moisture in the Yutian Oasis, Northwest China by 3D Feature Space Based on Optical and Radar Remote Sensing Data

Yilizhati Aili, Ilyas Nurmemet (), Shiqin Li, Xiaobo Lv, Xinru Yu, Aihepa Aihaiti and Yu Qin
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Yilizhati Aili: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Ilyas Nurmemet: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Shiqin Li: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Xiaobo Lv: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Xinru Yu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Aihepa Aihaiti: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Yu Qin: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

Land, 2025, vol. 14, issue 3, 1-29

Abstract: Soil moisture in arid areas serves as a vital indicator for assessing hydrological scarcity and ecosystem vulnerability, particularly in Northwest China (NW China), where water resource deficits critically exacerbate environmental fragility. Soil moisture retrieval through remote sensing techniques proves essential for formulating sustainable strategies to enhance local environmental management. This study presents an innovative fusion framework integrating Sentinel-2 optical data and Radarsat-2 PolSAR (Polarimetric Synthetic Aperture Radar) data to establish a three-dimensional (3D) optical–radar feature space. The feature space synergistically combines SAR backscattering coefficients (HH polarization modes), polarimetric decomposition (volume scattering components of van Zyl), and optical remote sensing indices (MSAVI and NDVI). Through systematic analysis of feature space partitioning patterns across soil moisture gradients, the Optical–Radar Soil Moisture Retrieval Index (ORSMRI) was proposed, and fitting analysis was conducted by measured soil moisture. The results confirmed consistency between ORSMRI-derived retrieved soil moisture and measured soil moisture, with ORSMRI1 attaining R 2 = 0.797 (RMSE = 3.329%) and ORSMRI2 reaching R 2 = 0.721 (RMSE = 3.905%). The soil moisture in the study area was retrieved by applying the proposed ORSMRI and utilizing its linear correlation with soil moisture. The distribution of soil moisture showed a trend of being higher in the south than in the north, and higher in the west than in the east. Specifically, low soil moisture is generally concentrated in the northern and southwestern parts of the oasis, while high soil moisture is primarily concentrated in the central part of the oasis.

Keywords: arid area; feature space; optical remote sensing; retrieval; SAR; soil moisture (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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