A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R² and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.