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Urban surface energy fluxes are closely associated with land-cover types (LCTs) and critical biophysical compositions. This study aims to assess the contribution of LCTs, vegetation fractional coverage (VFC) and percentage of impervious surface area (ISA%) to urban surface energy fluxes using remote sensing. An advanced urban surface energy flux algorithm was used to combine satellite imagery and meteorological station data to investigate the thermal environments in the city of Suzhou, China. The land cover abundances retrieved by multiple endmember spectral unmixing analysis (MESMA) were used to retrieve the per-pixel sensible heat flux (H) and latent heat flux (LE). The resultant heat fluxes were assessed using evaporation pan data collected from meteorological stations and ratios of the heat fluxes to the net radiation (Rn). Furthermore, spatial patterns of urban heat energy were investigated using an integrated analysis among land surface temperature (LST), heat fluxes, LCTs, VFC and ISA%. The high values of H and LST were found over the urbanized areas, which also had low values of LE. Conversely, the vegetated area was characterized with high LEs, as well as low LSTs and Hs. Moreover, a statistically-significant correlation (p < 0.05; R 2 = 0.88) was observed between LE and VFC at the zonal level, and a statistically-significant correlation (p < 0.05; R 2 = 0.90) was exhibited between H and ISA%. It is concluded that VFC, ISA% and LCTs are promising for delineating urban heat fluxes. Overall, this study indicates that remote sensing techniques can be used to quantify urban thermal environments.

In Fourier transform infrared spectroscopy (FTIR), the original interferometric image needs to be pre-processed by apodization, trend term removal and phase correction before the gas irradiance signal can be obtained by Fourier transform, of which trend term removal is the most important. The common method is least squares (LS), which requires high initial values and is susceptible to noise interference. In this paper, M-estimated sample consistency (MSAC) and genetic algorithm (GA) are used to remove the trend term from methane FTIR simulated interference data and compare them with the least squares method. The results show that: compared with the least squares method, the MSAC algorithm can improve the trend term fit by about 20%, but the trend term pattern needs to be known in advance; compared with the MSAC algorithm, the GA algorithm has a slightly lower fit effect of about 5%, but requires lower initial values, is more robust and is suitable for situations where the trend term pattern is unknown; combining the two, the GA-MSAC algorithm proposed in this paper, which both reduces the initial value requirement and greatly improves the accuracy of the trend term removal, is of great importance to Fourier transform infrared spectroscopy.