This dataset is an 8-year (2011-2018) global spatiotemporally consistent surface soil moisture dataset with a 25km spatial grid resolution and daily temporal step in unit of cm3/cm3. This dataset is developed by applying a linear weight fusion algorithm based on the Triple Collocation Analysis (TCA) to merge the five soil moisture data products, i.e., SMOS, ASCAT, FY3B, CCI and SMAP in two steps. The first step is to fuse the SMOS, ASCAT and FY3B soil moisture products from 2011 to 2018. The second step is to refuse the merged soil moisture product in the first step, CCI and SMAP products from 2015 to 2018, and to obtain the finally merged soil moisture product from 2011 to 2018. In addition, the measured soil moisture data from seven ground observation networks around the world are used to evaluate and analyze the merged soil moisture product. The fused soil moisture product has the global spatial coverage ratio of more than 80%. With rhe minimum RMSE (root mean square error) of 0.036 cm3/cm3.

File naming and required software

The soil moisture dataset is stored using TIF format with the file name fusion_ SMOS_ FY3B_ ASCAT_ ESACCI_ SMAP_ Version_ yymmdd.tif, where the yy represents year, the mm represents month and the dd represents day.
For example, "Fusion_ SMOS_ FY3B_ ASCAT_ ESACCI_ SMAP_ V1_ 20110101.tif". It represents the global soil moisture distribution with version No. 1 on January 1, 2011 with TIF format.
All TIF files in this dataset can be opened directly with ArcGIS / ENVI softwares. The main content of TIF image is numerical value ranging from 0 to 0.6 representing soil moisture content.

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Cite as:

Jia, L., Xie, Q., Hu, G. (2021). Global daily-scale soil moisture fusion dataset based on Triple Collocation Analysis (2011-2018). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.271935. CSTR: 18406.11.Terre.tpdc.271935. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Xie, Q., Jia, L., Menenti, M. et al (2022). Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018. Sci Data 9, 687 (2022). https://doi.org/10.1038/s41597-022-01772-x( 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

MOST High-Level Foreign Expert Program (No:GL20200161002)

Chinese Academy of Science President's International Fellowship Initiative (No:2020VTA0001)

Strategic Priority Research Program of the Chinese Academy of Sciences (No:XDA19030203)

None (No:2019QZKK0103)

Copyright & License

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