@article{175802e0f2bc4dac9c83caa6a73c4d49,
title = "Global long term daily 1 km surface soil moisture dataset with physics informed machine learning",
abstract = "Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here 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. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm 3/cm 3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.",
keywords = "ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD",
author = "Qianqian Han and Yijian Zeng and Lijie Zhang and Chao Wang and E. Prikaziuk and Zhenguo Niu and Zhongbo Su",
note = "Funding Information: The research presented in this paper was funded in part by the China Scholarship Council (grant no.202004910427). The authors would like to thank the European Commission and The Netherlands Organization for Scientific Research (NWO, ENWWW.2018.5) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the WaterWorks2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI.We are grateful for the freely available data at GEE, and the in-situ data from ISMN. We really appreciate Nicholas Clinton, Justin Braaten, and other people from GEE for providing storage of GEE, which is crucial for us to generate this big dataset. We also appreciate GEE providing such a nice platform which allows us to perform this kind of global study efficiently. Funding Information: The research presented in this paper was funded in part by the China Scholarship Council (grant no.202004910427). The authors would like to thank the European Commission and The Netherlands Organization for Scientific Research (NWO, ENWWW.2018.5) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the WaterWorks2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI.We are grateful for the freely available data at GEE, and the in-situ data from ISMN. Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = feb,
day = "17",
doi = "10.1038/s41597-023-02011-7",
language = "English",
volume = "10",
journal = "Scientific Data",
issn = "2052-4463",
publisher = "Nature Publishing Group",
}