A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. 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. It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

An edited version of this paper was published by AGU. Copyright 2022 American Geophysical Union [A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data. Geophysical Research Letters 49, 7 (2022)]

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Work Title A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data
Access
Open Access
Creators
  1. Jiangtao Liu
  2. Farshid Rahmani
  3. Kathryn Lawson
  4. Chaopeng Shen
Keyword
  1. Multiscale
  2. deep learning
  3. LSTM
  4. soil moisture
  5. in-situ
  6. resolution effect
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Geophysical Research Letters
Publication Date March 14, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1029/2021GL096847
Deposited November 07, 2024

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Version 1
published

  • Created
  • Added Liu_2022__Multi-Scale_finalmanuscript.pdf
  • Added Creator Jiangtao Liu
  • Added Creator Farshid Rahmani
  • Added Creator Kathryn Lawson
  • Added Creator Chaopeng Shen
  • Published
  • Updated
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • Multiscale , deep learning , LSTM , soil moisture, in-situ , resolution effect
    Publication Date
    • 2022-04-16
    • 2022-03-14