Estimation of winter wheat yield from sentinel-1A time-series images using ensemble deep learning and a Gaussian process-Reference-Cited by-同舟云学术

Estimation of winter wheat yield from sentinel-1A time-series images using ensemble deep learning and a Gaussian process

Published:2024-07-31 Issue:8 Volume:15 Page:828-837
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Li Qian¹²³⁴,Lu Jing⁵,Zhao Jianhui¹²³⁴,Yang Huijin¹²³⁴,Li Ning¹²³⁴^ORCID

Affiliation:

1. School of Computer and Information Engineering, Henan University, Kaifeng, China

2. Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng, China

3. Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng, China

4. Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, China

5. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, China

Funder

the National Natural Science Foundation of China

the College Key Research Project of Henan Province

the Plan of Science and Technology of Henan Province

the Key Laboratory of Natural Resources Monitoring and Regulation in Southern Hilly Region, Ministry of Natural Resources of the People’s Republic of China

Plan of Science and Technology of Department of Natural Resources of Hunan Province

the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People’s Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2024.2384094

Reference12 articles.

1. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

2. First Demonstration of RFI Mitigation in the Phase Synchronization of LT-1 Bistatic SAR

3. Food Security: The Challenge of Feeding 9 Billion People

4. Long Short-Term Memory