Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation

• Published in: IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 16
• Main Authors: Xu, Lei, Ye, Zhenni, Dai, Jin, Li, Qi, Hong, Youting, Tao, Yun, Yu, Hongchu, Zhang, Chong, Chen, Zeqiang, Chen, Nengcheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Agricultural management; Artificial intelligence; Collocation; Computational modeling; Correlation coefficient; Correlation coefficients; Data assimilation; Data assimilation (DA); Data collection; Data models; Datasets; Drought; Environmental monitoring; Estimation; Hydrologic data; in situ evaluation; Land surface; Learning algorithms; light gradient boosting machine (LightGBM); Machine learning; Moisture content; Satellites; Simulation; Soil; Soil moisture; soil moisture (SM); Soil surfaces; Spatiotemporal data; Spatiotemporal phenomena; Water management

Accurate and spatiotemporal seamless soil moisture (SM) products are important for hydrological drought monitoring and agricultural water management. Currently, physically-based process models with data assimilation (DA) are widely used for global seamless SM generation, such as SM Active Passive Le...