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Multiscale

Code Release

zenodo version

Short Summary

Here we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross-validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root-mean-square error of 0.034 m3/m3.

Bibtex Citation

@article{liu2022multiscale,
  title={A multiscale deep learning model for soil moisture integrating satellite and in situ data},
  author={Liu, Jiangtao and Rahmani, Farshid and Lawson, Kathryn and Shen, Chaopeng},
  journal={Geophysical Research Letters},
  volume={49},
  number={7},
  pages={e2021GL096847},
  year={2022},
  publisher={Wiley Online Library}
}