@article{han_global_2023,
	title = {Global long term daily 1 km surface soil moisture dataset with physics informed machine learning},
	volume = {10},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-023-02011-7},
	doi = {10.1038/s41597-023-02011-7},
	abstract = {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.},
	language = {en},
	number = {1},
	urldate = {2024-11-15},
	journal = {Scientific Data},
	author = {Han, Qianqian and Zeng, Yijian and Zhang, Lijie and Wang, Chao and Prikaziuk, Egor and Niu, Zhenguo and Su, Bob},
	month = feb,
	year = {2023},
	pages = {101},
}

{"_id":"SECnZ2X2XY3sHhvyu","bibbaseid":"han-zeng-zhang-wang-prikaziuk-niu-su-globallongtermdaily1kmsurfacesoilmoisturedatasetwithphysicsinformedmachinelearning-2023","author_short":["Han, Q.","Zeng, Y.","Zhang, L.","Wang, C.","Prikaziuk, E.","Niu, Z.","Su, B."],"bibdata":{"bibtype":"article","type":"article","title":"Global long term daily 1 km surface soil moisture dataset with physics informed machine learning","volume":"10","issn":"2052-4463","url":"https://www.nature.com/articles/s41597-023-02011-7","doi":"10.1038/s41597-023-02011-7","abstract":"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.","language":"en","number":"1","urldate":"2024-11-15","journal":"Scientific Data","author":[{"propositions":[],"lastnames":["Han"],"firstnames":["Qianqian"],"suffixes":[]},{"propositions":[],"lastnames":["Zeng"],"firstnames":["Yijian"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Lijie"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Chao"],"suffixes":[]},{"propositions":[],"lastnames":["Prikaziuk"],"firstnames":["Egor"],"suffixes":[]},{"propositions":[],"lastnames":["Niu"],"firstnames":["Zhenguo"],"suffixes":[]},{"propositions":[],"lastnames":["Su"],"firstnames":["Bob"],"suffixes":[]}],"month":"February","year":"2023","pages":"101","bibtex":"@article{han_global_2023,\n\ttitle = {Global long term daily 1 km surface soil moisture dataset with physics informed machine learning},\n\tvolume = {10},\n\tissn = {2052-4463},\n\turl = {https://www.nature.com/articles/s41597-023-02011-7},\n\tdoi = {10.1038/s41597-023-02011-7},\n\tabstract = {Abstract\n \n 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\n 3\n /cm\n 3\n , 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.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-11-15},\n\tjournal = {Scientific Data},\n\tauthor = {Han, Qianqian and Zeng, Yijian and Zhang, Lijie and Wang, Chao and Prikaziuk, Egor and Niu, Zhenguo and Su, Bob},\n\tmonth = feb,\n\tyear = {2023},\n\tpages = {101},\n}\n\n\n\n\n\n\n\n","author_short":["Han, Q.","Zeng, Y.","Zhang, L.","Wang, C.","Prikaziuk, E.","Niu, Z.","Su, B."],"key":"han_global_2023","id":"han_global_2023","bibbaseid":"han-zeng-zhang-wang-prikaziuk-niu-su-globallongtermdaily1kmsurfacesoilmoisturedatasetwithphysicsinformedmachinelearning-2023","role":"author","urls":{"Paper":"https://www.nature.com/articles/s41597-023-02011-7"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["global","long","term","daily","surface","soil","moisture","dataset","physics","informed","machine","learning","han","zeng","zhang","wang","prikaziuk","niu","su"],"title":"Global long term daily 1 km surface soil moisture dataset with physics informed machine learning","year":2023}