Preprints
https://doi.org/10.5194/gmd-2022-211
https://doi.org/10.5194/gmd-2022-211
Submitted as: model description paper
30 Sep 2022
Submitted as: model description paper | 30 Sep 2022
Status: this preprint is currently under review for the journal GMD.

Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops

Jiangtao Liu1, David Hughes2,3,4, Farshid Rahmani1, Kathryn Lawson1, and Chaopeng Shen1 Jiangtao Liu et al.
  • 1Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
  • 2Department of Entomology, The Pennsylvania State University, University Park, PA, USA
  • 3Department of Biology, The Pennsylvania State University, University Park, PA, USA
  • 4The Current and Emerging Threat to Crop Innovation Lab, The Pennsylvania State University, University Park, PA, USA

Abstract. Climate change threatens our ability to grow food for a growing population. There are concurrent droughts and floods happening globally, with the greatest impacts felt in Africa. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. Yet it is unclear if soil moisture processes are globally similar enough to allow our models to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based and in-situ soil moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but performed relatively poorly in Alaska where rapid changes are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9-km product. Factorial analysis shows that the LSTM model’s accuracy with representing impacts of terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently provides information to 13 million African farmers.

Jiangtao Liu et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-211', Anonymous Referee #1, 24 Oct 2022 reply
    • AC1: 'Reply on RC1', Chaopeng Shen, 06 Dec 2022 reply

Jiangtao Liu et al.

Data sets

Global Soil Moisture Dataset From a Multitask Model (GSM3) Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, Chaopeng Shen https://doi.org/10.5281/zenodo.7026036

Model code and software

Global Soil Moisture Dataset From a Multitask Model (GSM3) Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, Chaopeng Shen https://doi.org/10.5281/zenodo.7026036

Jiangtao Liu et al.

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Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in-situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.