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A Remote Sensing Based Data Set of Surface Soil Water Decay in Southwest China (2003-2020)

The original remote sensing based global surface soil water decadence data set (RSSSM, 2003~2020) is based on 11 commonly used global microwave remote sensing soil water data products in the world, using neural network method, and incorporating 9 microwave remote sensing inversion soil water quality impact factors. The spatial resolution of data is 0.1 degree, and the temporal resolution is ten days. The original data covers 2003-2018 and is now updated to 2020. The RSSSM data set has outstanding time continuity, achieving full space coverage except for ice, snow and water. Through the test of global measured data, it can be proved that RSSSM data set has higher spatial and temporal pattern accuracy than the existing global or regional long time series topsoil aquatic products. In addition, although the RSSSM data is based on remote sensing and does not incorporate any precipitation data, its interannual variation is well consistent with the temporal variation of precipitation (such as GPM IMERG precipitation data) and standardized evapotranspiration index (SPEI). RSSSM data can also reflect the impact of human activities such as urbanization, farmland irrigation and vegetation restoration on soil moisture to a certain extent. The data is in tiff format, and the compressed data volume is 2.48 GB. The data paper will be published in Earth System Science Data in 2021. This dataset is obtained by regional tailoring based on the original global dataset

File naming and required software

Naming method: SMY+(year)+DECA+(ten day number). For example, the data of SMY2003DECA15 is the surface soil water from May 21 to 31, 2003.
The unit of data is volume water content, i.e. m3/m3.
The data represents the water content of the surface 5cm soil.

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Cite as:

Chen, Y., Feng, X., Fu, B. (2022). A Remote Sensing Based Data Set of Surface Soil Water Decay in Southwest China (2003-2020). Upper Yangtze River Scientific Data Center, (Download the reference：

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)

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