Global long term daily 1km surface soil moisture dataset with physics informed machine learning
收藏国家青藏高原科学数据中心2025-11-17 更新2025-11-29 收录
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https://data.tpdc.ac.cn/zh-hans/data/a11479c5-b0a8-40b8-8092-f47719a6c882
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资源简介：
(1) Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. In this study, we present a new dataset Global Surface Soil Moisture (GSSM1km). GSSM1km provides surface soil moisture (0-5 cm) at 1 km spatial and daily temporal resolution over the period 2000-2020.
(2) We use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics.
(3) The performance of the GSSM1km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9
(4) GSSM1km product can support the investigation of large-scale climate extremes and long-term trend analyses.
提供机构：
韩倩倩,曾亦键,Bob Su
创建时间：
2025-10-08
