Soil Moisture Forecasting integrating Physical-based model and Deep Learning

<p>Dataset used in &quot;Soil Moisture Forecasting integrating Physical-based model and Deep Learning&quot;.</p> <p>(1) <strong>1-24.tar</strong> is training/test data (after preprocessing) over 24 sub-regions in China.</p> <p>(2) <strong>GFS*</strong>&nbsp;is 3-day forecast of Global Forecast System (GFS) over 2015-2017 and 2018 years.</p> <p>(3) <strong>auxiliary.json</strong> is utility data (e.g., land mask for sub-task).</p> <p>(4) <strong>valid_data.tar</strong>&nbsp;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.</p> <p>&nbsp;</p>