Soil Moisture Forecasting integrating Physical-based model and Deep Learning

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.

本数据集为《融合物理模型与深度学习的土壤湿度预报》（Soil Moisture Forecasting integrating Physical-based model and Deep Learning）一文所使用的数据集。
(1) 1-24.tar 为中国境内24个子区域经预处理后的训练与测试数据集。
(2) GFS* 为全球预报系统（Global Forecast System，GFS）在2015-2017年及2018年的3天预报数据集。
(3) DEM* 与 LC* 分别为EASE 9km格网下的数字高程模型（Digital Elevation Model，DEM）和土地覆盖数据。
(4) auxiliary.json 为辅助数据（例如用于子任务的陆地掩膜数据）。
(5) valid_data.tar 包含2018年的SoMo.ml、ERA5-Land、SMOS L3、LPRM-AMSR2数据集，为本研究中用于三重组合分析的数据集。中国气象局（China Meteorological Administration，CMA）原位观测数据集仅可在获得CMA相关许可后，通过我方申请获取。