Advancing Satellite-Derived Precipitation Downscaling in Data-Sparse Area Through Deep Transfer Learning
Author:
Affiliation:
1. Department of Geography, Hong Kong Baptist University, Hong Kong, China
Funder
Natural Science Foundation of China (NSFC) General Program
Hong Kong Research Grant Council (RGC) General Research Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10440025.pdf?arnumber=10440025
Reference57 articles.
1. Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy
2. Estimation of Satellite Rainfall Error Variance Using Readily Available Geophysical Features
3. A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging
4. A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information
5. Spatial Downscaling of IMERG Considering Vegetation Index Based on Adaptive Lag Phase
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