Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data-Reference-Cited by-同舟云学术

Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data

Published:2022-10-14 Issue:1 Volume:15 Page:1784-1816
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Li Shaopeng¹²,Jiang Bo¹²,Liang Shunlin³,Peng Jianghai¹²,Liang Hui¹²,Han Jiakun¹²,Yin Xiuwan¹²,Yao Yunjun¹²,Zhang Xiaotong¹²,Cheng Jie¹²^ORCID,Zhao Xiang¹²,Liu Qiang¹⁴,Jia Kun¹²

Affiliation:

1. The State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, People’s Republic of China

2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of China

3. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

4. Beijing Engineering Research Center for Global Land Remote Sensing Products, College of Global Change and Earth System Science, Beijing Normal University, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2022.2130460

Reference114 articles.

1. Guide to Convolutional Neural Networks

2. Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

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5. SURFRAD—A National Surface Radiation Budget Network for Atmospheric Research

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