以北京市Landsat TM为数据源, 提出了一种新的地表温度光谱分解模型(Temperature Unmixing with Spectral, TUS), 以期将地表温度的空间分辨率提高到30 m.首先, 基于线性光谱混合模型获得地表组分的丰度值.然后, 基于温度/植被指数选取典型端元的地表温度.最后, 综合地表组分的比辐射率数据实现地表温度的分解.结果表明, TUS模型能够有效地提高地表温度的空间分辨率, 反映不同地表组分地表温度的空间差异性, 平均绝对误差(MAE)和均方根误差(RMSE)分别为1.25 K和2.27 K, 非常适合于复杂地表覆盖地区的地表温度降尺度处理.

This paper proposed a new pixel decomposition model of Temperature Unmixing with Spectral (TUS). Landsat TM data acquired in Beijing were used for the study. Firstly, land surface fraction was obtained based on the Linear Spectral Mixing Model.Secondly and LST of typical endmember was selected through Temperature Vegetation Index. Finally, pixel decomposition of LST can be achieved integrated emissivity with different surface components. Our results indicated that TUS can effectively improve the spatial resolution of land surface temperature, reflecting the spatial differences of surface components, with MAE and RMSE 1.25K and 2.27K respectively. Therefore we conclude that TUS model is applicable for decomposition of LST images for high spatial resolution in the complex surface coverage area.