Spatiotemporal statistical evaluation of recent active and passive satellite-derived soil moisture products across Central Asia under multiple scenarios
Mousa B.G. Samat A. Du P. Abuduwaili J. Liu X. Al-Masnay Y.A. Nasri A. Abdelhamid M.M.A.
February 2026Elsevier B.V.
Ecological Informatics
2026#93
Algorithms for retrieving Surface Soil Moisture (SSM) from microwave remote sensing are continually refined, necessitating the evaluation of newly released products to identify their optimal applications and potential for improvement. This study evaluates ASCAT H119, SMAP DCA, SMAP MTDCA, SMAP-IB, AMSR2, and SMOS-IC products across Central Asia (CA) using a multi-scenario approach. First, the products were validated against three independent reference datasets: ERA5, ESA CCI (combined), and GLDAS-Noah. Second, Triple Collocation Analysis (TCA) was employed to estimate the Fractional Mean Squared Error (fMSE) and the error variance of each product. Third, Hovmöller diagrams were used to identify regional spatiotemporal trends and anomalies. Additionally, the quality of SSM products was examined in relation to key ecological variables across CA. All evaluations were conducted during the SSM growing season (April–October) from 2016 to 2019. Results showed that SMAP products, particularly SMAP DCA and SMAP-IB, delivered the most accurate SSM estimates, achieving the highest average correlation (0.63 to 0.85), low average bias (−0.059 to −0.077 m3 m−3), and the lowest average ubRMSE (0.030 to 0.049 m3 m−3) across the three reference datasets. ASCAT and AMSR2 exhibited moderate performance, while SMOS-IC performed the weakest overall. All products performed best against ESA CCI, followed by ERA5 and then GLDAS-Noah. The performance ranking derived from TCA was generally consistent with the reference-based validation, except for ASCAT and AMSR2. The proposed evaluation framework offers a reliable alternative for assessing SSM products in regions with sparse ground measurements. This study provides valuable insights for enhancing SSM monitoring, hydrological modeling, and agricultural management in the CA ecosystem.
Central Asia (CA) , Ecology , Remote sensing , Soil moisture , Triple Collocation Analysis (TCA)
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State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
Department of Mining and Petroleum Engineering, Faculty of Engineering, Al-Azhar University, Cairo, 11884, Egypt
China-Kazakhstan Joint Laboratory for RS Technology and Application, Al-Farabi Kazakh National University, Almaty, 050012, Kazakhstan
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, 830011, China
University of Chinese Academy of Sciences, Beijing, 100049, China
School of Geography and Ocean Sciences, Nanjing University, Nanjing, 210093, China
INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-dOrnon, France
Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
Guangzhou Huali College, Guangzhou, 511325, China
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
Department of Mining and Petroleum Engineering
China-Kazakhstan Joint Laboratory for RS Technology and Application
Research Center for Ecology and Environment of Central Asia
University of Chinese Academy of Sciences
School of Geography and Ocean Sciences
INRAE
Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region
Guangzhou Huali College
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