TY  - Data
T1  - Global Surface Soil Moisture Estimation Dataset Enhanced by Conditional Variation Autoencoder (2015-2021)
A1  - Shi Changjiang
A1  - Zhang Wanchang
DO  - 10.5281/zenodo.8000601
PY  - 2024
DA  - 2024-06-18
PB  - National Cryosphere Desert Data Center
AB  - 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.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/22b6af04-e52c-42e6-a758-14a1215940fc
ER  -