Abstract
Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.
Highlights
Land surface temperature (LST) becomes a vital physical variable of earth surface system, which is a pivotal variable for researching urban heat islands and the relevant environmental and climatic issues of urban environment [1,2,3]
The generic formulation of the RFATPK contains a trend component and a regression residual component, which is represented by the equation: z10m(x) = m 10m(x) + ε10m(x) where z10m(x) is the final downscaled 10 m LST, m 10m(x) is the trend component estimated by random forests (RF), and ε10m(x) is the regression residual component interpolated with area-to-point Kriging (ATPK)
The 90 m ASTER LST on 1 November 2017 over two different land cover types were downscaled to 10 m using the RFATPK
Summary
Land surface temperature (LST) becomes a vital physical variable of earth surface system, which is a pivotal variable for researching urban heat islands and the relevant environmental and climatic issues of urban environment [1,2,3]. Thermal infrared-based LST products of different spatial scales have been widely applied in meteorological, hydrological, and ecological research and urban heat island monitoring on both regional and global scales [4,5,6,7,8]. The spatial resolution of existing LST products from 60 m to 1 km, is insufficient to reveal the local intrinsic interactions between LST and land covers [9,10], especially in complex urban environments. A precise estimation of higher resolution LST is essential to understand the heat exchange processes in an urban environment. LST downscaling is an alternative approach to produce high-precision and high spatial resolution LST of the urban environment
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