Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.

File naming and required software

File name: SMY+(year)+DECA+(the ordinal number of 10-day period in a year). For example, SMY2003DECA15 is the surface soil moisture data for May 21st~31st in 2003.
The unit is volumetric fraction, or m3/m3.
The data can represent soil moisture in the top 5 cm of soil.
Sea, inland surface water and snow or ice cover are masked by NaN values. In winters, high latitude and high altitude areas are usually masked by NaNs because surface soil water are in frozen state (i.e., snow/ice), and are not meaninful for the ecosystem.

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Cite as:

Chen, Y., Feng, X., Fu, B. (2021). A Remote Sensing-based global 10-day resolution Surface Soil Moisture dataset (RSSSM, 2003~2020). National Tibetan Plateau Data Center, DOI: 10.11888/Hydro.tpdc.271775. CSTR: 18406.11.Hydro.tpdc.271775. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Chen, Y., Feng, X., & Fu, B. (2021). An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018, Earth Syst. Sci. Data, 13, 1–31, https://doi.org/10.5194/essd-13-1-2021.( View Details | Bibtex)

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

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