Enhanced Generalized Regression Neural Network With Backward Sequential Feature Selection for Machine-Learning-Driven Soil Moisture Estimation: A Case Study Over the Qinghai-Tibet Plateau
Author:
Affiliation:
1. School of Naval Architecture & Ocean Engineering, Jiangsu Maritime Institute, Nanjing, China
2. School of Earth Sciences and Engineering, Hohai University, Nanjing, China
3. Sichuan Academy of Safety Science and Technology, Chengdu, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Natural Science Foundation of the Jiangsu Higher Education Institutions of China
Sichuan Provincial Key Research and Development Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10195153.pdf?arnumber=10195153
Reference34 articles.
1. Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture
2. Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method
3. Toward the true near-surface wind speed: Error modeling and calibration using triple collocation
4. A pixel-wise calculation of soil evaporative efficiency with thermal/optical remote sensing and meteorological reanalysis data for downscaling microwave soil moisture
5. Effects of the new Priestly-Taylor equation on determining the boundary of LST/FVC space for soil moisture monitoring
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