Land Surface Component Temperature Retrieval for Urban Scale Based on ASTER Image

Land surface component temperature has more significant physical meaning,and it reflects the actual distribution of temperature more significantly.Meanwhile,its retrieval algorithms have no need for hypothesis that components in pixels have the same temperature.Although the multi-angle retrieval algorithm of component temperature has become mature gradually,its application in the studies on urban thermal environment is restricted due to the difficulty in acquiring urban-scale multi-angle thermal infrared data.Therefore,based on the existing multi-band remote sensing data,access to appropriate urban-scale component temperature is an urgent issue to be solved in current studies on urban thermal infrared remote sensing.In this paper,a new algorithm to retrieve land surface component temperature for urban area had been proposed.It took advantage of ASTER data,and evaluated mean emissivity of pixels based on linear spectral unmixing,retrieved atmospheric water vapor content from MODIS NIR bands,and used Newton's iterative method to obtain atmosphere average temperature.Finally,an experimental study of this algorithm had been conducted and the retrieval result had been validated using some measured data.The results showed that:(1) the results of component temperature retrieval algorithm and split window algorithm of pure pixels have high correlation coefficient and the correlation coefficient of vegetation is the highest;(2) compared with the measured data,biases of the retrieval result ranged between 0.2 and 1.4℃,and the vegetation component temperature among different components had the smallest bias value.