Land surface emissivity (LSE) is a prerequisite for retrieving land surface temperature (LST) through single channel methods. According to error model, a 0.01 (1%) uncertainty of LSE may result in a 0.5 K error in LST under a moderate condition, while an obvious error (approximately 1 K) is possible under a warmer and less humid situation. Significant emissivity variations are presented among the anthropogenic materials in three spectral libraries, which raise a critical question that whether urban LSE can be estimated accurately to meet the needs for LST retrieval. Methods widely used for urban LSE estimation are investigated, including the classification-based method, the spectral-index based method, and the linear spectral mixture model (LSMM). Results indicate that the classification-based method may not be effectively applicable for urban LSE estimation, due mainly to the insignificant relation between the short-wave multispectral reflectance and the long-wave thermal emissivity shown by the spectra. Compared with the classification-based method, the LSMM shows relatively more accurate predictions, whereas, the performance of the LSMM largely depends on the determination of endmembers. Obvious uncertainties in LSE estimation likely appear if endmembers are determined improperly. Increasing the spectra for endmembers is a practical and beneficial means for LSMM when there is not a priori knowledge, which emphasizes the necessity of building a comprehensive spectral library of urban materials. Furthermore, the LST retrieval from a single channel of Landsat 8 is more challenging as compared with the retrieval from the channels of its predecessors-Landsat 4/5/7.
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