High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

Jinkang Hu; Dailiang Peng; Jing M Chen; Alfredo R Huete; Le Yu; Zihang Lou; Enhui Cheng; Xuan Yang; Bing Zhang

doi:10.1016/j.xinn.2025.100868

High-precision inversion of vegetation parameters in the AI era: Integrating hyperspectral remote sensing and deep learning

Innovation (Camb). 2025 Mar 5;6(6):100868. doi: 10.1016/j.xinn.2025.100868. eCollection 2025 Jun 2.

Authors

Jinkang Hu^{1

2

3}, Dailiang Peng^{1

2}, Jing M Chen^{4

5}, Alfredo R Huete⁶, Le Yu⁷, Zihang Lou⁸, Enhui Cheng^{1

2

3}, Xuan Yang⁹, Bing Zhang^{1

2

3}

Affiliations

¹ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
² International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China.
³ College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100094, China.
⁴ School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China.
⁵ Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada.
⁶ School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.
⁷ Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China.
⁸ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
⁹ China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

No abstract available

Publication types

Review