Abstract
Aiming at low spectral contrast materials, the Optimized Smoothing for Temperature Emissivity Separation (OSTES) method was developed to improve the Temperature and Emissivity Separation (TES) algorithm based on the linear relationship between brightness temperature and emissivity features, but there was little smoothing improvement for higher spectral contrast materials. In this paper, a new nonlinear-relationship based algorithm is presented, focusing on improving the performance of the OSTES method for materials with middle or high spectral contrast. This novel approach is a two-step procedure. Firstly, by introducing atmospheric impact factor, the nonlinear relationship is mathematically proved using first-order Taylor series approximation. Moreover, it is proven that nonlinear model has stronger universality than linear model. Secondly, a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) is proposed based on the nonlinear model for smoothing temperature and emissivity retrieval. The key procedure of TESNC is the lowest emissivity smoothing estimation based on nonlinear model and retrieved by minimizing the reconstruction error of the Planck radiance. TESNC was tested on a series of synthetic data with different kinds of natural materials representing several multispectral and hyperspectral infrared sensors. It is shown that, especially for materials with higher spectral contrast, the proposed method is less sensitive to changes in atmospheric conditions and sample temperatures. Furthermore, the standard Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) products in different kind of atmospheric conditions were used for verifying the improvement. TESNC is more accurate and stable with the decrease of emissivity and changes of atmospheric conditions compared with TES, Adjusted Normalized Emissivity Method (ANEM), and OSTES methods.
Highlights
Multispectral and hyperspectral remote sensing in the thermal infrared (TIR) range have emerged over the past decades providing valuable information for remotely identifying materials
Three thermal infrared sensors, including an airborne and spaceborne imager, were chosen as examples to analyze the performance of proposed method Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) and compared with the Optimized Smoothing for Temperature Emissivity Separation (OSTES) and temperature and emissivity separation (TES) methods; namely, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scanner on NASA’s Earth Observing System (EOS)-AM1 satellite [12], the Airborne Hyperspectral Scanner (AHS) operated by Spanish Institute of Aeronauics (INTA) and developed by ArgonST (Fairfax, USA) [19], and the Telops Hyper-Cam, an airborne long-wave infrared hyperspectral imager [20]
ASTER consists of 15 bands, of which the last five bands are situated in the TIR region for surface temperatures and emissivity spectra estimation with noise equivalent temperature difference (NE∆T) ≈ 0.3 K
Summary
Multispectral and hyperspectral remote sensing in the thermal infrared (TIR) range have emerged over the past decades providing valuable information for remotely identifying materials. In order to get both the LST and LSE of materials, temperature and emissivity separation (TES) algorithm is developed based on data from multispectral and hyperspectral sensors. OSTES enhances the performance of TES in temperature and emissivity retrievals for samples with low spectral contrast based on the linear relationship between brightness temperature and emissivity [17,18]. The concept in the ANEM is to adjust guessed emissivity by estimating channel emissivity in a pixel-by-pixel basis accounting for the spatial variation of emissivity with different land types, such as natural areas, urban areas and water Both OSTES and ANEM can get better retrieval results for materials with low spectral contrast. To solve the problem mentioned above, this paper proposes a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC), focusing on improving the performance of OSTES for middle and high spectral contrast materials.
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