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Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures

Xia, Haiping, Chen, Yunhao, Li, Ying, Quan, Jinling
Remote sensing of environment 2019 v.224 pp. 259-274
Landsat, moderate resolution imaging spectroradiometer, prediction, quantitative analysis, remote sensing, surface temperature, time series analysis
High-spatiotemporal-resolution land surface temperatures (LSTs) are required in various environmental applications. However, due to the trade-off between the spatial and temporal resolutions in remote sensing, such data are still unavailable. Many studies have been conducted to resolve this dilemma, but difficulties remain in generating high-spatiotemporal-resolution (i.e., diurnal, e.g., 30 m resolution) LSTs. Accordingly, this study proposes a weighted combination of kernel-driven and fusion-based methods (termed CKFM) to enhance the resolution of time series LSTs; the kernel-driven process can obtain abundant spatial details from visible bands, while the fusion-based process is applied for its spatiotemporal prediction ability. CKFM contains three parts. First, a kernel-driven method is applied to predict high-resolution LSTs via a regression relationship between simulated medium-resolution LSTs and kernels. Second, a fusion-based method is applied to predict the medium-resolution LST, the result of which is used for thin plate spline (TPS) downscaling. Finally, the results of the kernel-driven and fusion-based processes are combined via weights calculated from error estimations. Compared with existing thermal sharpening methods, CKFM has the following strengths: (1) it fully utilizes the available visible and thermal bands from multiple sensors, thereby obtaining spatial details in a variety of ways; (2) it downscales LSTs in a dynamic manner; and (3) it is suitable for heterogeneous regions. CKFM is tested with Landsat 8 and MODIS data and successfully downscales the 1 km resolution MODIS LSTs into 30 m resolution data. In both visual and quantitative evaluations, CKFM is more accurate and robust than the kernel-driven method with an improvement of 0.1–0.6 K, and it reconstructs more spatial details than the fusion-based process. Based on these characteristics, CKFM is a promising method for generating daily high spatial resolution LSTs for various environmental studies.