“Regression-then-Fusion” or “Fusion-then-Regression”? A Theoretical Analysis for Generating High Spatiotemporal Resolution Land Surface Temperatures

The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the “regression-then-fusion” (R-F) and “fusion-then-regression” (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa