An Improved Integrated Model for Temporal Normalization of Satellite-Derived Land Surface Temperature

Temporally incomparability across the scan lines in polar-orbiting satellite-derived land surface temperature (LST) affects their widespread application. Some challenges persist in the existing research on this issue, such as the absence of a universal algorithm applicable for the partly clear-sky condition in the daytime and scale inconsistency of the used datasets, when LST varies nonlinearly over time. Given this situation, we proposed an improved approach for temporal normalization, integrating ensemble regression models and a new LST variation rate model (RM), which captures typical LST variation characteristics over time during polar-orbiting satellite overpass periods. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) LST data across the contiguous United States (CONUS) were collected to investigate its effectiveness. Moreover, cross-validation was conducted using the time-interpolated Geostationary Operational Environmental Satellite 16 (GOES-16) advanced baseline imager (ABI) LST. The normalized LST had remarkable consistency with the GOES-16 LST, with superior accuracy in contrast with the original LST. The root-mean-squared error (RMSE) was improved by approximately 1.56 K, and bias was enhanced up to 1.80 K. This study exhibited relatively superior performance in terms of quantitative outcomes and spatial distribution of LST compared with the previous studies. These evaluations indicate that the proposed method could be a dependable and general solution for addressing temporal inconsistencies in clear-sky LST during polar-orbiting satellite overpass periods.