%0 Dataset
%T Global Surface Soil Moisture Estimation Dataset Enhanced by Conditional Variation Autoencoder (2015-2021)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/22b6af04-e52c-42e6-a758-14a1215940fc
%W NCDC
%R 10.5281/zenodo.8000601
%A Shi Changjiang
%A Zhang Wanchang
%K Soil moisture (SM);conditional variation autoencoder;machine learning;surface variables
%X High quality soil moisture (SM) estimation is crucial for various applications such as drought monitoring, environmental assessment, and agricultural management. The advancement of remote sensing technology has made it possible to use active and passive sensors to retrieve near real-time soil moisture on the Earth's surface. However, the European Space Agency's Climate Change Initiative (CCI) SM product combines data from multiple sensors, but sacrifices spatiotemporal resolution and coverage due to satellite orbit limitations and retrieval algorithms. To address this issue, a SM reconstruction method based on a conditional variation autoencoder model was developed by utilizing the high spatial resolution of SMAP L4 data and the accuracy of CCI fusion products in different land cover types. Through this method, a global three-day SM product with a temperature range of 0.0625 ° was created, spanning from 2015 to 2021.