Soil Moisture Forecasting integrating Physical-based model and Deep Learning
收藏NIAID Data Ecosystem2026-05-01 收录
下载链接：
https://zenodo.org/record/7174125
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资源简介：
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) auxiliary.json is utility data (e.g., land mask for sub-task).
(4) 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.
创建时间：
2023-08-08
