Abstract
There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.
Highlights
Land surface temperature (LST) and land surface emissivity (LSE) are two key physical parameters characterizing the state of the land surface, which are applied in various areas such as mineral mapping [1,2,3], gas plume detection [4], soil moisture inversion [5] and oil-film thicknesses measurement [6]
Overall, when the noise level was greater than 0.1 K, the decline in the percentage of the retrieved LSE median absolute deviation (MAD) of resolution-degrade-based spectral smoothness (RDSS) was greater than 10%, which proved the effectiveness of the RDSS algorithm
We focused on the study of instrument noise, a widely existing factor that is difficult to eliminate losslessly in hyperspectral thermal infrared data, and focused on optimizing instrument design
Summary
Land surface temperature (LST) and land surface emissivity (LSE) are two key physical parameters characterizing the state of the land surface, which are applied in various areas such as mineral mapping [1,2,3], gas plume detection [4], soil moisture inversion [5] and oil-film thicknesses measurement [6]. Based on the segmented linear constraints, three TES methods have been proposed, namely, the linear spectral emissivity constraint (LSEC) [30], the improved LSEC (I-LSEC) [31] and the pre-estimate shape LSEC (PES-LSEC) [32], which all take the sum of squared residuals of the estimated and true ground-leaving radiance as cost function Another way to reduce the dimensionality is using the wavelet transform. The sensor altitude, spectral range and resolution depend on the equipment development level and the external conditions during data acquisition They mainly affect errors of the atmospheric parameters (input parameters for separating temperature and emissivity), and the accuracy of atmospheric correction increases with the improvement of technology [40,50,51].
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