Abstract

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.

Highlights

  • Compared to the reflective spectral range between 0.35 and 2.5 μm, the radiant energy of ground objects from the thermal infrared (TIR) atmospheric window of 8–12 μm is mainly self-emission energy, which is related to a material’s molecular structure and vibration [1]

  • The experimental results indicated that the retrieved emissivity spectra from the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer

  • We describe three types of widely used temperature-emissivity separation (TES) algorithms, i.e., ISSTES, FLAASH-IR, and the modified ASTER-TES algorithm, and we extend an empirically constrained method for the Hyper-Cam LW imagery that was originally designed for multispectral TIR imagery with six bands

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Summary

Introduction

Compared to the reflective spectral range between 0.35 and 2.5 μm, the radiant energy of ground objects from the thermal infrared (TIR) atmospheric window of 8–12 μm is mainly self-emission energy, which is related to a material’s molecular structure and vibration [1]. 2021, 13, 4453 and contains the material composition information, retrieving accurate LSE from TIR remote sensing data plays a very important role in many remote sensing applications, such as geological mapping [2,3], object identification [4,5], and land-cover classification [6,7]. Various TIR imaging sensors with different spatial and spectral resolutions have been used to obtain remote sensing observations. The typical spaceborne TIR systems are Gaofen 5 (GF-5), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat 8, and the Moderate Resolution Imaging Spectroradiometer (MODIS), which provide TIR data with resolutions of 40, 90, 100, and 1000 m, respectively. Because the band number of most of the LSE products from these spaceborne sensors is less than six channels, the emissivity spectra cannot highlight subtle spectral features

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