Temperature and emissivity retrieval from ground-based thermal infrared hyperspectral imagery of open scene

Since the measured radiance in ground-based thermal infrared hyperspectral imagery of open scenes is simultaneously influenced by target surface emission, atmospheric path emission, and environment-reflected radiance, accurately retrieving surface temperature and emissivity for close-range targets remains challenging. To mitigate this coupling effect, this letter proposes a lightweight sky-radiance-assisted retrieval method that leverages the spectral similarity between background sky radiance and a standard atmospheric radiance template. Sky pixels are automatically identified from the hyperspectral image, and their averaged spectrum is used to estimate the ambient radiance directly, eliminating the need for auxiliary atmospheric measurements or external sensors. Based on the estimated ambient radiance, surface temperature and emissivity are subsequently retrieved using the ASTER-TES framework, which is further refined through wavelet-based suppression of residual atmospheric line absorption effects. Validation using HypercamLW data acquired over a short horizontal path demonstrates that, under favourable conditions with relative humidity below 28% and temperature contrast exceeding 6 K, the proposed method achieves root-mean-square errors below 0.5 K for temperature and 0.009 for emissivity. These results indicate that the proposed approach provides a practical and lightweight solution for temperature and emissivity retrieval in close-range open-scene thermal infrared hyperspectral applications.

Authors

Institutions

Publication Details

Journal
Remote Sensing Letters
Published
2026-10-03
DOI
https://doi.org/10.1080/2150704x.2026.2734332
Primary Topic
Urban Heat Island Mitigation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Temperature and emissivity retrieval from ground-based thermal infrared hyperspectral imagery of open scene

Chuan Zhang, Jiejun Huang, Yanfei Zhong, Haiyang Xiong et al.
Remote Sensing Letters
Urban Heat Island Mitigation
article

Temperature and emissivity retrieval from ground-based thermal infrared hyperspectral imagery of open scene

Chuan Zhang, Jiejun Huang, Yanfei Zhong, Haiyang Xiong, Li-Qin Cao, Du Wang
article en

Abstract

Since the measured radiance in ground-based thermal infrared hyperspectral imagery of open scenes is simultaneously influenced by target surface emission, atmospheric path emission, and environment-reflected radiance, accurately retrieving surface temperature and emissivity for close-range targets remains challenging. To mitigate this coupling effect, this letter proposes a lightweight sky-radiance-assisted retrieval method that leverages the spectral similarity between background sky radiance and a standard atmospheric radiance template. Sky pixels are automatically identified from the hyperspectral image, and their averaged spectrum is used to estimate the ambient radiance directly, eliminating the need for auxiliary atmospheric measurements or external sensors. Based on the estimated ambient radiance, surface temperature and emissivity are subsequently retrieved using the ASTER-TES framework, which is further refined through wavelet-based suppression of residual atmospheric line absorption effects. Validation using HypercamLW data acquired over a short horizontal path demonstrates that, under favourable conditions with relative humidity below 28% and temperature contrast exceeding 6 K, the proposed method achieves root-mean-square errors below 0.5 K for temperature and 0.009 for emissivity. These results indicate that the proposed approach provides a practical and lightweight solution for temperature and emissivity retrieval in close-range open-scene thermal infrared hyperspectral applications.

Remote Sensing LettersVol. 17(11)
Wuhan University of Technology (CN), Wuhan University (CN), Beijing Research Institute of Uranium Geology (CN)
Openalex Percentile: Top 19%
Urban Heat Island Mitigation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.