A Hierarchical Reconstruction Framework for the Gap Area of ESA CCI Soil Moisture Product Using Deep Learning Model
Author:
Affiliation:
1. Chinese Academy of Sciences,Institute of Mountain Hazards and Environment,Chengdu,China,610299
Funder
National Natural Science Foundation of China
Institute of Mountain Hazards and Environment
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10616250/10617814/10617950.pdf?arnumber=10617950
Reference14 articles.
1. Comparison of two satellite-based soil moisture reconstruction algorithms: A case study in the state of Oklahoma, USA
2. Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology
3. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression
4. High-resolution observations from space to address new applications in hydrology
5. Drought in agriculture: Preservation, adaptation, migration
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