Published October 22, 2022
| Version 2.0
Dataset
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Soil Moisture Forecasting integrating Physical-based model and Deep Learning
Description
Dataset used in "Soil Moisture Forecasting integrating Physical-based model and Deep Learning".
(1) 1-24.tar is training/test data (after preprocessing) over 24 sub-regions in China.
(2) GFS* is 3-day forecast of Global Forecast System (GFS) over 2015-2017 and 2018 years.
(3) DEM* and LC* is DEM and land cover in EASE 9km grids.
(4) auxiliary.json is utility data (e.g., land mask for sub-task).
(5) valid_data.tar contains 2018 year of SoMo.ml, ERA5-Land, SMOS L3, LPRM-AMSR2, which were used to triple collocation analysis in our study. The CMA in-situ datasets only could be available from us after certain permission in CMA.
Notes
Codes of "Soil Moisture Forecasting integrating Physical-based model and Deep Learning", Journal of Hydrometeorology are open source in https://github.com/leelew/HybridHydro.
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