Soil Moisture Forecasting integrating Physical-based model and Deep Learning
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Dataset used in "Soil Moisture Forecasting integrating Physical-based model and Deep Learning". (1) <strong>1-24.tar</strong> is training/test data (after preprocessing) over 24 sub-regions in China. (2) <strong>GFS*</strong> is 3-day forecast of Global Forecast System (GFS) over 2015-2017 and 2018 years. (3) <strong>DEM*</strong> and <strong>LC*</strong> is DEM and land cover in EASE 9km grids. (4) <strong>auxiliary.json</strong> is utility data (e.g., land mask for sub-task). (5) <strong>valid_data.tar</strong> 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.
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Zenodo创建时间：
2022-10-21
