【摘要】 Soil moisture is critical for climate prediction,ecological management,and disaster warning.However,multi-source datasets show spatiotemporal inconsistencies and uncertain regional applicability due to algorithmic and observational limitations.We assess the statistical performance and spatiotemporal variations of 23 global surface soil moisture datasets（1980-2023） from reanalysis,land surface models,and microwave remote sensing across global and regional scales（classified by K?ppen climates and IPCC land uses）.Results show a slight long-term（1980-2023） global surface soil moisture decline(-4.30 × 10^-4 m³ m^-3 a^-1),with some datasets indicating short-term wetting(7.17 × 10^-4 m³ m^-3 a^-1)post-2010（2010-2023）.A dual-validation against 992 and a filtered subset of 483 highly representative in situ stations shows that most products perform moderately well（Pearson R ≈ 0.5-0.7）.Microwave remote sensing products,especially those based on SMAP,consistently demonstrate superior performance in capturing temporal dynamics（R ≈ 0.7）.Our analysis demonstrates that spatial representativeness error can mask true performance,with validation in the tropics improving dramatically after site filtering（mean R increase of 0.41）.The findings highlight product-specific strengths and weaknesses,underscoring the necessity of a science-informed,application-specific approach to dataset selection for robust hydrological and climatic research.更多还原

【Abstract】 Soil moisture is critical for climate prediction,ecological management,and disaster warning.However,multi-source datasets show spatiotemporal inconsistencies and uncertain regional applicability due to algorithmic and observational limitations.We assess the statistical performance and spatiotemporal variations of 23 global surface soil moisture datasets（1980-2023） from reanalysis,land surface models,and microwave remote sensing across global and regional scales（classified by K?ppen climates and IPCC land uses）.Results show a slight long-term（1980-2023） global surface soil moisture decline(-4.30 × 10^-4 m³ m^-3 a^-1),with some datasets indicating short-term wetting(7.17 × 10^-4 m³ m^-3 a^-1)post-2010（2010-2023）.A dual-validation against 992 and a filtered subset of 483 highly representative in situ stations shows that most products perform moderately well（Pearson R ≈ 0.5-0.7）.Microwave remote sensing products,especially those based on SMAP,consistently demonstrate superior performance in capturing temporal dynamics（R ≈ 0.7）.Our analysis demonstrates that spatial representativeness error can mask true performance,with validation in the tropics improving dramatically after site filtering（mean R increase of 0.41）.The findings highlight product-specific strengths and weaknesses,underscoring the necessity of a science-informed,application-specific approach to dataset selection for robust hydrological and climatic research.更多还原