Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

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.

Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager

“…Therefore, it is imperative to quantitatively evaluate instrument performance before a field measurement campaign and to conduct a data acquisition procedure in the field to acquire useful TIR hyperspectral imagery. In addition, compared to the data obtained by non-imaging spectrometers, TIR hyperspectral imagery often contain a high noise level, and previous studies [30][31][32][33] have shown that measurement noise can have a great influence on final emissivity estimations. Spectral filtering methods [18,34] have been effectively applied to the denoising of multispectral TIR data in order to im-prove image quality.…”

Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

“…The latest advances in data fusion, downscaling and disaggregation techniques provide a new dimension to LST applications in water resource and agronomic management thanks to the improvement in both the temporal and spatial resolution of thermal products [8][9][10]. However, at the same time, continuous research into LST estimation algorithms, as well as continuous calibration and validation, are still required to improve the accuracy of ground LST data and satellite LST products [1][2][3][4][5]13,14].…”

“…Published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation [1][2][3][4], improving long-term consistency in satellite LST [5][6][7], downscaling LST [8][9][10], LST applications [11,12] and land surface emissivity research [13,14].…”

“…Field and laboratory emissivity measurements are essential for improving and validating LST retrievals [13,14]. Temperature and emissivity separation algorithms can be applied when multispectral thermal radiances are available.…”

“…Temperature and emissivity separation algorithms can be applied when multispectral thermal radiances are available. A manuscript in this Special Issue by [13] explored the influence mechanism of noise on the LST and surface emissivity retrieval errors of the ARTEMISS algorithm. The authors proposed an improved method for thermal data with a high noise level and high spectral resolution, which can reduce LST and emissivity uncertainties.…”