Preliminary evaluation of linear spectral emissivity constraint temperature and emissivity separation method for contrast samples from hyperspectral thermal infrared data

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared remote sensing. Current TES methods were proposed and succeeded to apply for the retrieval of land surface temperature and emissivity for the materials with emissivity close to 1. This work addressed the performance of linear spectral emissivity constraint (LSEC) method proposed by wang et al. (2011) for the TES of hyperspectral TIR data for contrast samples (high- and low- emissivity materials). The simulated hyperspectral TIR data are used for analysis and generated with six MODTRAN standard atmospheric profiles by hyperspectral atmospheric radiative transfer model (4A/OP). The influence of initial emissivity estimation is considered in this paper. The results show that initial emissivity estimation has a great impact on the performance of LSEC. LSEC method performs a fairly good result when the initial emissivity is close to the true value, and the RMSEs of temperature and emissivity are smaller than 0.5K and 0.01 when initial emissivity is good. However, the performance is worst when the initial emissivity has a great deviation.