Published May 5, 2021 | Version 3.0
Software Open

A fine-resolution soil moisture dataset for China in 2002~2018

  • 1. School of Physics and Electronic-Engineerring, Ningxia University; School of Earth Sciences and Engineering, Hohai University
  • 2. School of Physics and Electronic-Engineerring, Ningxia University; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences
  • 3. School of Surveying and Geo-Informatics, Shandong Jianzhu University
  • 4. National Space Science Center, Chinese Academy of Sciences; State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University
  • 5. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University
  • 6. Civil and Environmental Engineering, University of Connecticut
  • 7. School of Earth and Space Sciences, Peking University
  • 8. School of Physics and Electronic-Engineerring, Ningxia University

Description

Soil moisture is one of the key parameters for flood forecast, drought detection, crop yield estimation, weather prediction and hydrological modeling. Passive microwave remote sensing technology can quickly obtain soil moisture over large areas, but the coarse spatial resolution imposes great limitations. In order to improve the temporal and spatial resolution of soil moisture products, we built a spatial weight decomposition model to improve the resolution of soil moisture products. The validation and application analysis indicate that new product can meet application needs. SMC version 3.0 combines the previous two versions, and corrects and optimizes in some areas.

Files

program.zip

Files (892.3 MB)

Name Size Download all
md5:f166c2af9bc75c80d0787e3c51386b44
26.1 kB Download
md5:a8963e832c9fc4d4f0c0e8ebb3a79cb4
16.8 kB Preview Download
md5:84a14b297c96a063e32ea9b356450a25
221.4 MB Download
md5:72f4528bbdcb0859de47ef7e83ed4f26
266.0 MB Preview Download
md5:4a3f063458ff9c597939658d90c27c6a
404.9 MB Preview Download