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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Errors analysis on temperature and emissivity determination from hyperspectral thermal infrared data.

Xiaoying OuYang¹, Ning Wang, Hua Wu

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

|February 23, 2010

Summary

This summary is machine-generated.

This study analyzes errors in hyperspectral temperature-emissivity separation. Accurate atmospheric correction is crucial for reliable land surface temperature and emissivity retrieval, especially over heterogeneous surfaces.

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Area of Science:

Earth Science
Remote Sensing
Atmospheric Science

Background:

Accurate land surface temperature (LST) and emissivity retrieval from remote sensing data is vital for various applications.
Hyperspectral data offers potential for improved LST and emissivity estimation but is sensitive to errors.

Purpose of the Study:

To perform a sensitivity analysis of the temperature-emissivity separation method applied to hyperspectral data.
To quantify the impact of various error sources on LST retrieval accuracy.

Main Methods:

Sensitivity analysis of the temperature-emissivity separation method.
Evaluation of errors arising from atmospheric conditions, water vapor uncertainty, and atmospheric correction quality.

Main Results:

Satisfactory LST retrieval is achievable for heterogeneous land surfaces across all atmospheric conditions.
The method shows low sensitivity to uncertainties in atmospheric water vapor (WV), with temperature errors within -0.2 to 0.6K for a 50% WV uncertainty.
Errors from inadequate atmospheric correction significantly impact LST and emissivity retrieval accuracy.

Conclusions:

Robust LST retrieval is possible for heterogeneous surfaces.
Precise atmospheric correction is essential for accurate land surface temperature and emissivity separation using hyperspectral data.