Estimating High-Resolution Urban Surface Temperature Using a Hyperspectral Thermal Mixing (HTM) Approach

Abstract

Land surface temperature (LST) plays an important role in characterizing surface energy fluxes as well as climatology. High-spatial-resolution LST values are needed to profoundly understand urban climatology through identifying its detailed thermal distribution patterns. In this paper, a simple method that uses hyperspectral spectral mixture analysis (SMA) to estimate fine resolution urban LST is presented. The proposed approach takes account of the universal existence of thermally mixed pixels in complex urban areas and includes mainly two steps. In the first step, multiple endmember SMA (MESMA) is applied to calculate land composition fractions. With the retrieved component abundances and associated thermal responses, high-resolution urban LST is estimated through a linear combination of each thermal response component multiplied by its respective abundance in the second step. In particular, hyperspectral data were used to enhance the spatial information extraction through improved accuracy in retrieving land cover abundance. Evaluation of the proposed method was performed at four spatial scales (4, 8, 16, and 32 m) over two scenes with different land cover characteristics. Results show a relatively modest agreement between the estimated LST and the reference LST by both visual interpretation and quantitative accuracy metrics, with a root-mean-square error (RMSE) of 4.18 and 1.71 K as well as the mean absolute error (MAE) of 3.62 and 0.89 K for the two scenes separately. Overall, this approach enhances the application of hyperspectral data for estimating high-spatial-resolution LST; therefore, it can serve as a promising technique in urban applications.