Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
https://doi.org/10.5194/gmd-16-1553-2023
Model description paper
 | 
17 Mar 2023
Model description paper |  | 17 Mar 2023

Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats

Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen

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Cited articles

Aboelyazeed, D., Xu, C., Hoffman, F. M., Jones, A. W., Rackauckas, C., Lawson, K. E., and Shen, C.: A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2022-211, in review, 2022. 
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. 
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. 
Al-Yaari, A., Wigneron, J.-P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P. E., Jackson, T. J., De Lannoy, G. J. M., Al Bitar, A., Mialon, A., Richaume, P., Walker, J. P., Mahmoodi, A., and Yueh, S.: Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets, Remote Sens. Environ., 193, 257–273, https://doi.org/10.1016/j.rse.2017.03.010, 2017. 
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., and Jetz, W.: A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, Sci. Data, 5, 180040, https://doi.org/10.1038/sdata.2018.40, 2018. 
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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.
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