SGD-SM

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 the zip file).
To further validate the effectiveness of these productions, 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.

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.

Dataset Download

Link 1: Baidu Yun (Extracting Code: fu8f)
Link 2: Google Drive
Link 3: Zenodo

Environments and Dependencies

Windows 10
Python 3.7.4
netCDF4
numpy

Toolkit Installation

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

Data Reading

It should be noted that the original and reconstructed soil moisture data are both recorded in a NC file. User can read the original data, reconstructed data, and mask data as follows (more details can be viewed in Example.py):

    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)

Data Visualization

Users can visualize *.nc format file through Panoply software. Before visualizing, you must install Java SE Development Kit.

Contact Information

If you have any query for this work, please directly contact me.

Author: Qiang Zhang, Wuhan Unviversity.

E-mail: whuqzhang@gmail.com

Homepage: qzhang95.github.io

qzhang95/SGD-SM