Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature

Abstract

Three methods are currently used to retrieve land surface temperatures (LSTs) from thermal infrared data supplied by the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors: the radiative transfer equation, mono-window, and generalized single-channel algorithms. Most retrieval results obtained using these three methods have an average error of more than 1 K. But if the regional mean atmospheric water vapour content and temperature are supplied by in situ radiosounding observations, the mono-window algorithm is able to provide better results, with a mean error of 0.5 K. However, there are no in situ radiosounding data for most regions. This article provides an improved method to retrieve LST from Landsat TM and ETM+ data using atmospheric water vapour content and atmospheric temperature, which can be obtained from remote-sensing data. The atmospheric water vapour content at the pixel scale was first calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The emissivities of various land covers and uses were then defined by Landsat TM or ETM+ data. In addition, the temperature–vegetation index method was applied to map area-wide instantaneous near-surface air temperatures. The parameters of mean atmospheric water vapour content and temperature and land surface emissivity were finally inputted to the mono-window algorithm to improve the LST retrieval precision. Our results indicate that this improved mono-window algorithm gave a significantly better retrieval of the estimated LST than that using the standard mono-window algorithm, not only in dry and elevated mountain regions but also in humid regions, as shown by the bias, standard deviation (σ), and root mean square deviation (RMSD). In Madoi County, the improved mono-window algorithm validated against the LST values measured in situ produced a bias and RMSD of –0.63 K and 0.91 K, respectively, compared with the mono-window algorithm’s bias and RMSD of –1.08 K and 1.27 K. Validated against the radiance-based method, the improved algorithm shows bias and RMSD values of –1.08 K and 1.27 K, respectively, compared with the initial algorithm’s bias and RMSD –1.65 K and 1.75 K. Additionally, the improved mono-window algorithm also appeared to be more accurate than the mono-window algorithm, with lower error values when validated against in situ measurement and the radiance-based method in the validation area in Zhangye City, Gansu Province, China. Remarkable LST accuracy improvements are shown by the improved mono-window algorithm, with better agreement not only with the in situ measurements but also with the simulated LSTs in the two validation areas, indicating the soundness and suitability of this method.