Abstract
Land surface temperature (LST) is a crucial biophysical parameter related closely to the land–atmosphere interface. Satellite thermal infrared measurement provides an effective method to derive LST on regional and global scales, but it is very hard to acquire simultaneously high spatiotemporal resolution LST due to its limitation in the sensor design. Recently, many LST downscaling and spatiotemporal image fusion methods have been widely proposed to solve this problem. However, most methods ignored the spatial heterogeneity of LST distribution, and there are inconsistent image textures and LST values over heterogeneous regions. Thus, this study aims to propose one framework to derive high spatiotemporal resolution LSTs in heterogeneous areas by considering the optimal selection of LST predictors, the downscaling of MODIS LST, and the spatiotemporal fusion of Landsat 8 LST. A total of eight periods of MODIS and Landsat 8 data were used to predict the 100-m resolution LST at prediction time tp in Zhangye and Beijing of China. Further, the predicted LST at tp was quantitatively contrasted with the LSTs predicted by the regression-then-fusion strategy, STARFM-based fusion, and random forest-based regression, and was validated with the actual Landsat 8 LST product at tp. Results indicated that the proposed framework performed better in characterizing LST texture than the referenced three methods, and the root mean square error (RMSE) varied from 0.85 K to 2.29 K, and relative RMSE varied from 0.18 K to 0.69 K, where the correlation coefficients were all greater than 0.84. Furthermore, the distribution error analysis indicated the proposed new framework generated the most area proportion at 0~1 K in some heterogeneous regions, especially in artificial impermeable surfaces and bare lands. This means that this framework can provide a set of LST dataset with reasonable accuracy and a high spatiotemporal resolution over heterogeneous areas.
Highlights
IntroductionLand surface temperature (LST) is a crucial terrestrial geophysical variable that affects the heat transformation process between the land surface and the atmospheric boundary layer [1]
By considering the spatial downscaling of Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) and spatial heterogeneity of LST, this study developed a new framework to predict the
Three key points are involved in this study: (1) the optimal selection of LST predictors; (2) the downscaling of MODIS LST; and (3) the implementation of the flexible spatiotemporal data fusion (FSDAF) algorithm
Summary
Land surface temperature (LST) is a crucial terrestrial geophysical variable that affects the heat transformation process between the land surface and the atmospheric boundary layer [1]. Its spatiotemporal dynamics play an important role in impacting the surface energy balance, soil moisture content, evapotranspiration, and surface thermal environment [2,3]. Continuous spatiotemporal estimation of LST is essential for related fields of terrestrial surface process on a regional or global scale, such as soil moisture content monitoring, vegetation evaporation estimation, water and heat flux measurement, and urban heat island (UHI) monitoring [4].
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