Airborne thermal infrared hyperspectral image temperature and emissivity retrieval based on inter-channel correlated automatic atmospheric compensation and TES

Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are key properties of natural materials essential for scientific analysis. Existing retrieval techniques, however, impede the automatic retrieval of LST and LSE across various observational contexts due to the frequent unavailability of in-situ atmospheric data or blackbody references for atmospheric compensation. To address this, we propose the Inter-Channel Correlated Automatic Atmospheric Compensation (ICCAAC) method, seamlessly integrated with the ASTER-TES approach, enabling direct retrieval of LST and LSE from at-sensor radiance without prerequisite atmosphere and land surface information. ICCAAC innovatively models the inter-channel relationships among atmospheric constituents to streamline the Radiative Transfer Equation (RTE), which includes the transmittance reconstruction with neighboring channels and the atmospheric upwelling radiance simplification with the atmospheric vertical characteristics, tackling the complex challenge of retrieving ground-leaving radiance. Coupled with ASTER-TES and under the LSE smoothness constraint, along with a lookup table for atmospheric downwelling radiance, this method facilitates accurate LST and LSE retrieval. In controlled simulations, ICCAAC shows a maximum brightness temperature error of 1.6 K for Water Vapor Content (WVC) under 1.5 $g/{\mathrm{cm}}^2$. A spectral sensitivity analysis suggests the optimal performance of ICCAAC for full width at half maximum (FWHM) range extending beyond the channel interval. Applied to real-world airborne data from Hypercam-LW and HyTES, ICCAAC-TES validates its accuracy with an LST and LSE error margin of 1.2 K and 0.014, respectively, corroborated by seventeen distinct ground validations in Hypercam-LW imagery. Comparative analysis with five varied HyTES observation scenes reveals an LST discrepancy of around 1.2 K and notable emissivity textures in LSE images, particularly in mineral terrains. These outcomes underscore the efficacy of ICCAAC-TES, advocating its suitability for automated LST and LSE retrieval in airborne survey applications.