If you used our dataset, please cite our reference: Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021. Description: A seamless global daily (SGD) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include 2553 global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about 20GB memory after uncompressing this zip file). To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at SGD-SM. An example Python code of extracting this dataset is also available at https://github.com/qzhang95/SGD-SM. Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13). This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install netCDF4 toolkit before reading the data: pip install netCDF4 pip install numpy It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows: Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)

{"references": ["Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013\u20132019, Earth Syst. Sci. Data, 13, 1385\u20131401, https://doi.org/10.5194/essd-13-1385-2021, 2021."]}