Measurand-Aware Inter-Platform Consistency Assessment of GOES Land Surface Temperature Harmonization

Cross-platform land surface temperature (LST) harmonization is often evaluated by agreement of individual observations, although urban analyses commonly rely on spatial differences and temporal changes. We investigated whether improving agreement in urban-core and surrounding-ring LST also improves their core–ring contrast and pre-to-post-sunset transition. Using Advanced Baseline Imager observations from Geostationary Operational Environmental Satellites (GOES)-16, -17, -18, and -19 over western North American cities during 2019–2026, we evaluated five cohorts using established mean-squared-error (MSE) decomposition and cross-validated model selection at each measurand. Component root-mean-squared error (RMSE) decreased by 29.3–59.7%, whereas core–ring contrast RMSE ranged from an 11.5% reduction to a 13.1% increase. Loss of residual covariance offset 94.6–114.0% of gross variance-plus-bias gains. In the post hoc 2026 temporal evaluation of the same 75 cities used for 2025 calibration, core and ring RMSE declined by 59.7% and 56.1%, but contrast RMSE reduction was −0.14% (95% crossed city–interval bootstrap interval, −6.75% to +6.48%, with cities and intervals resampled independently). The historical 2021 evaluation likewise showed only a small, uncertain contrast improvement (2.9%; 95% interval, −4.3% to +9.8%). Calibration-based selection retained raw contrasts and transitions, while a residual interval rule withheld uncertain transition direction claims. Independent three-station radiometric comparisons assessed raw satellite products at station scale rather than the complete urban workflow. These findings demonstrate why harmonization performance should be evaluated at the derived scientific quantity, with processing decisions based on its observed cross-platform consistency.