Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5267-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-5267-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Nov 2022
Data description paper |
| 30 Nov 2022
| 30 Nov 2022
A 1 km daily soil moisture dataset over China using in situ measurement and machine learning
Qingliang Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Gaosong Shi
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Vahid Nourani
Center of Excellence in Hydroinformatics and Faculty of Civil
Engineering, University of Tabriz, Tabriz 51368, Iran
Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard, Nicosia 99628, Turkey
Jianduo Li
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 10081, China
Lu Li
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Feini Huang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Ye Zhang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Chunyan Wang
College of Computer Science and Technology, Changchun Normal
University, Changchun 130032, China
Dagang Wang
School of Geography and Planning, Sun Yat-sen University, Guangzhou
510275, China
Jianxiu Qiu
School of Geography and Planning, Sun Yat-sen University, Guangzhou
510275, China
Xingjie Lu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
Yongjiu Dai
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Guangdong Province Key Laboratory for Climate Change and Natural Disaster
Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510275, China
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Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived...
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