New framework for land surface temperature and emissivity inversion based on hyperspectral thermal infrared data-Reference-Cited by-同舟云学术

New framework for land surface temperature and emissivity inversion based on hyperspectral thermal infrared data

Published:2023-06-02 Issue: Volume: Page:1-18
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Li Xiujuan¹²,Wu Hua¹²,Ni Li³,Zhang Yuze⁴,Jiang Xiaoguang²

Affiliation:

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

2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

3. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

4. National Engineering Research Center for Transportation Safety and Emergency Informatics, China Transport Telecommunications & Information Center, Beijing, China

Funder

Strategic Priority Research Program of Chinese Academy of Sciences

Guangxi Science and Technology Project

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2216855

Reference36 articles.

1. A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument

2. Dimension reduction of satellite observations for remote sensing. Part 1: A comparison of compression, channel selection and bottleneck channel approaches

3. A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations

4. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales

5. A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data