Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France-Reference-Cited by-同舟云学术

Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

Published:2020-10 Issue:10 Volume:7 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth and Space Science

Author:

Liu Yangxiaoyue¹²^ORCID,Xia Xiaolin¹²,Yao Ling³,Jing Wenlong¹²^ORCID,Zhou Chenghu³,Huang Wumeng¹²,Li Yong¹²,Yang Ji¹²

Affiliation:

1. Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou China

2. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Funder

National Postdoctoral Program for Innovative Talents

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020EA001267

Reference110 articles.

1. Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

2. From near‐surface to root‐zone soil moisture using an exponential filter: An assessment of the method based on insitu observations and model simulations;Albergel C.;Hydrology & Earth System Sciences Discussions,2008