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...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)m^(3)m^(-3)a^(-1)),with some datasets indicating short-term wetting(7.17×10^(-4)m^(3)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.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0720400)the Third Xinjiang Scientific Expedition Program(2022xjkk0107)+2 种基金the West Light Foundation of the Chinese Academy of Sciences(xbzgzdsys-202208)the Program of China Scholarship Council(ICPIT-International Cooperative Program for Innovative Talents,202410630006)support from the European Research Council(ERC)Consolidator grant HEAT(101088405)。
文摘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)m^(3)m^(-3)a^(-1)),with some datasets indicating short-term wetting(7.17×10^(-4)m^(3)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.
