Antinoise Estimation of Temperature and Emissivity for FTIR Spectrometer Data Using Spectral Polishing Filters: Design and Comparison

The ill-posed separation problem of land surface temperature and emissivity (LST and LSE) from ground-based Fourier transform infrared (FTIR) spectrometer data is vulnerable to random errors generated in the preceding measurement and calibration processes. Under the assumption that the influence of random radiative errors is mainly manifested in the overall stretching (multiplicative noise) and high frequency fluctuation (additive noise) upon at-ground radiance spectrum, the traditional iterative retrieval method is improved to an antinoise estimation method. A prepolishing strategy in radiance domain is employed to suppress the additive noise. A spectrally invariant coefficient is introduced during iteration for the estimation and correction of multiplicative noise. The Savitzky-Golay filtering is introduced to the proposed method first. For the simulation data of typical ground-based FTIR spectrometers (spectral sampling interval of 1-4 cm
^-1
, noise equivalent spectral radiance of 5 ×10
^-9
W/(cm
²
·sr ·cm
^-1
), and multiplicative noise error of 1.015), the root-mean-square error (RMSE) of LST is ≤ 0.4 K, the RMSE of LSE is <; 0.01 in the long-wave infrared range. For field data of a spectrometer under similar noise conditions, the RMSE of LST is about 0.7 K and consistent with the LST measured by a thermocouple thermometer, considering the measurement uncertainty. The proposed method is also generalized to use moving average filtering, derivation filtering, the Fourier transform, and wavelet transform. For the field data with both multiplicative and additive noises, the antinoise estimation methods with these tested filters have similar accuracy; among them, the moving average filtering and the Savitzky-Golay filtering are recommended.