We developed a 1-km resolution long-term soil moisture dataset of China derived through machine learning trained with in-situ measurements of 1,648 stations, named as SMCI1.0 (Soil moisture of China based on In-situ data, Li et al, 2022). SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000-2020. Random Forest is used to predict soil moisture using ERA5-land time series, leaf area index, land cover type, topography and soil properties as covariates. Using in-situ soil moisture as the benchmark (The data comes from China Meteorological Administration), two independent experiments are conducted to investigate the estimation accuracy of the SMCI1.0: year-to-year experiment (ubRMSE ranges from 0.041-0.052 and R ranges from 0.883-0.919) and station-to-station experiment (ubRMSE ranges from 0.045-0.051 and R ranges from 0.866-0.893). As SMCI1.0 is based on in-situ data, it can be useful complements of existing model-based and satellite-based datasets for various hydrological, meteorological, and ecological analyses and modeling, especially for those applications requiring high resolution SM maps. Please read the readme file for more details. We provided two versions with different resolution, i.e., 30 arc seconds (~1km) and 0.1 degree (~9km).

File naming and required software

File name: SMCI1.0 in stored in NetCDF (.nc) format, the file name is "SMCI"_ sskm_ yyyy_ ddcm.nc ", where ss stands for SMCI1.0's spatial resolution (1 or 9), yyyy stands for year, dd stands for the depth.
Data reading method: here are many freely available softwares or programming language such as python ,NCL, Panoply for manipulating or displaying NetCDF Data.

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Cite as:

Shangguan, W., Li, Q., Shi, G. (2022). China Soil Moisture Dataset (2000-2020). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272415. CSTR: 18406.11.Terre.tpdc.272415. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Li, Q., Shi, G., Shangguan, W., Nourani, V., Li, J., Li, L., Huang, F., Zhang, Y., Wang, C., Wang, D., Qiu, J., Lu, X., & Dai, Y. (2022). A 1 km daily soil moisture dataset over China using in situ measurement and machine learning. Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

National Natural Science Foundation of China (42105144) (No:42105144)

National Natural Science Foundation of China (41975122) (No:41975122)

None (No:U1811464)

Copyright & License

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)