Upscaling of Soil Moisture over Highly Heterogeneous Surfaces and Validation of SMAP Product

Jiakai Qin, Zhongli Zhu (), Qingxia Wu, Julong Ma, Shaomin Liu, Linna Chai and Ziwei Xu
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Jiakai Qin: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Zhongli Zhu: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Qingxia Wu: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Julong Ma: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Shaomin Liu: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Linna Chai: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China
Ziwei Xu: State Key Laboratory of Earth Surface Processes and Hazards Risk Governance (ESPHR), Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

Land, 2025, vol. 14, issue 10, 1-23

Abstract: Soil moisture (SM) is a critical component of the global water cycle, profoundly influencing carbon fluxes and energy exchanges between the land surface and the atmosphere. NASA’s Soil Moisture Active/Passive (SMAP) mission provides soil moisture products at the global scale; however, validation of SMAP faces significant challenges due to scale mismatches between in situ measurements and satellite pixels, particularly in highly heterogeneous regions such as the Qinghai–Tibet Plateau. This study leverages high-spatiotemporal-resolution Harmonized Landsat–Sentinel-2 (HLS v2.0) data and the QLB-NET observation network, employing multiple machine learning models to generate pixel-scale ground-truth soil moisture from in situ measurements. The results indicate that XGBoost performs best (R = 0.941, RMSE = 0.047 m 3 /m 3 ), and SHAP analysis identifies elevation and DOY as the primary drivers of the spatial patterns and dynamics of soil moisture. The XGBoost-upscaled soil moisture was employed as a validation benchmark to assess the accuracy of the SMAP 9 km and 36 km products, with the following key findings: (1) the proposed upscaling method effectively bridges the scale gap, yielding a correlation of 0.858 between the 36 km SMAP product and the pixel-scale soil moisture reference derived from XGBoost, surpassing the 0.818 correlation obtained using the traditional in situ averaging approach; (2) descending-orbit data generally outperform ascending-orbit data. In the 9 km SMAP product, 15 descending-orbit grids meet the scientific standard, compared to 10 ascending-orbit grids. For the 36 km product, only descending orbits satisfy the scientific standard.

Keywords: QLB-NET; topographic heterogeneity; machine learning; HLS (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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