This dataset contains 10 years (2010-2019) global daily surface soil moisture . The resolution is 36 km , the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017，2021). This study transfers the merits of SMAP to FY-3B/MWRI through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with FY-3B/MWRI brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content，which is comparable with that of SMAP. (evaluation accuracy of 14 dense ground network globally.)

File naming and required software

File name: The soil moisture data is stored in netcdf format, and the file name is“ NNsm-FY-yyyyddd.nc ”, where yyyy stands for year and ddd stands for Julian date. For example, NNsm-FY-2013001.nc represents this document describe the global soil moisture distribution on the first day of 2013.
How to read data: The data is EASE-grid equal-area projection data (with varying latitude and longitude intervals)， rather than usual equal-latitude-longitude data. (for more information about EASE-grid projection, please see https://nsidc.org/data/ease). The NC file of data stores three variables: latitude matrix, longitude matrix and soil moisture matrix, which are latitude (406*1), longitude（964*1） and soil_moisture (406*964) respectively. Projection information is not stored.
A. NC file can be directly read using software such as Matlab. For more information about netcdf, please see http://www.unidata.ucar.edu/software/netcdf.
B. If you want to convert NC file to TIF format, you need a .tif template data with EASE-grid 36km. We provide this data named EASEGrid2_36km.tif, please see the data folder. Here is a tutorial transferring EASE_grid file to TIF, written by a student in our group: https://blog.csdn.net/weixin_38953602/article/details/101158084

Data Citations Data citation guideline What's data citation?

Cite as:

Yao, P., Lu, H., Zhao, T., Wu, S., Shi, J. (2021). A global daily soil moisture dataset derived from Chinese FengYun-3B Microwave Radiation Imager (MWRI) (2010-2019). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.271954. CSTR: 18406.11.Terre.tpdc.271954. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yao, P.P., Lu, H., Zhao, T.J., Wu, S.L., Shi, J.C., Yang K., Cosh, M.H., Zhang, P. (2022). A global daily soil moisture dataset derived from Chinese FengYun-3B Microwave Radiation Imager (MWRI) (2010-2019) . Scientific Data. (Under Review)( View Details | Bibtex)

2. Yao, P.P., Lu, H., Shi, J.C., Zhao, T.J., Yang K., Cosh, M.H., Gianotti, D.J.S., & Entekhabi, D. (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019). Scientific Data, 8, 143 (2021). https://doi.org/10.1038/s41597-021-00925-8( View Details | Bibtex)

3. Yao, P.P., Shi, J.C., Zhao, T.J., Lu, H. & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing 9(1), 35.( 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 Key Research and development Program (No:2018YFB0504905)

the Major Research plan of the National Natural Science Foundation of China (No:42090014)

2nd survey of Tibet plateau (No:2019QZKK0206)

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