Abstract Soil Moisture is a vital state variable influencing land surface dynamics across hydrological, meteorological, and climatological contexts. The Soil Moisture Operational Product System (SMOPS), developed by the National Environmental Satellite, Data, and Information Service (NESDIS) of National Oceanic and Atmospheric Administration (NOAA), has been operationally providing satellite soil moisture observational data products for scientific studies and numerical weather and water predictions. However, the lack of a high-quality long-term SMOPS product has led to pronounced fluctuations in data quality across distinct versions and notable uncertainties for climatological studies and prolonged data assimilation operations. To address these issues, NESDIS has reprocessed SMOPS with all available satellite soil moisture observations to generate a Climate Data Record (SMOPScdr). SMOPScdr incorporates advancements of using machine learning approaches, satellite radiances calibration, inter-satellite bias correction, and observation-driven quality control. The reprocessed product offers improved accuracy, expanded spatial coverage, and an extended observation period from 2002 to present. The advancement makes this product valuable for both meteorological and climatological studies. SMOPScdr has been compared to in situ observations and Soil Moisture Active and Passive data, demonstrating consistent performance and superior spatiotemporal coverage. We showcase a range of successful scientific and operational applications of this new product in climate change research, flood and drought monitoring. The initial release of the SMOPScdr data is now available to the public and will undergo further refinement based on feedback from the scientific, operational and industrial communities. This paper outlines the development and evaluation of the SMOPScdr product, highlights its potential applications, and invites users to shape future directions for its improvement.
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