A Surface Energy Balance-Derived Method for Temporal Normalization of Land Surface Temperature From Polar-Orbiting Satellites

Land surface temperature (LST) is a crucial parameter linked to water and carbon cycles. However, due to the wide swath widths of polar-orbiting satellites, the temporal inconsistencies in each pixel observation lead to significant LST variations, resulting in temporal incomparability in the current LST products acquired from these satellites. Therefore, a physical method based on the surface energy balance and Bowen ratio (SEB-BR) was developed to correct the temporal effects on the LST. The advantage of the SEB-BR approach lies in its ability to establish a physical connection between various physical parameters and the LST, quantifying each parameter’s contribution to LST variation and thereby mechanically linking physical variables that contribute to LST variation. A diurnal temperature cycle (DTC) model named GOT01-dT and random forest regression (RFR) were introduced for comparison. Results showed that the SEB-BR model achieved higher accuracy with an average RMSE (bias) of 2.85 (1.26) K, outperforming the RFR model (2.96 (1.36) K) and GOT01-dT model (2.94 (0.39) K). The SEB-BR method also demonstrated better agreement with in-situ LST across various local solar time differences, particularly for temporal differences between 0.8–0.9 hours, where the RMSE decreased significantly from 3.54 K to 2.63 K. The improvements in accuracy across all seasons, reflected in the RMSE reductions from 3.56 (2.08) K to 3.39 (1.80) K, were significant. These results highlight the effectiveness of the SEB-BR approach in correcting temporal effects and its potential for large-scale temporal normalization, offering strong interpretability and spatiotemporal extensibility.