The US Southwest is one of the driest and hottest regions, with a recent upsurge in land surface temperature (LST). Further, with land-use changes and global warming, anthropogenic pollution also significantly contributes to the rise in surface temperatures. While the impact of pollution on LST has been studied only in specific urban regions, insights from a broader, more diverse topography remain limited. This research incorporates LST with land cover parameters (NDBI, MNDWI, NDBSI, SAVI, WET), surface albedo, air pollutants (NO2, SO2, O3, CO), aerosol particles, urban nighttime light, and digital elevation model to evaluate the non-linear spatial dependence of these variables for the summer (from June to August 2025) and winter (from December 2024 to February 2025) seasons in the US southwest. All multi-resolution inputs were harmonized by projecting to WGS84 and applying a ~11 km fishnet sampling grid commensurate with the coarsest-resolution dataset (Sentinel-5P), ensuring each sample captures a unique pixel value across all layers. AutoML was applied to benchmark learning algorithms, and we found that CatBoost, Extra Trees, LightGBM, HistGradientBoosting, and Random Forest were among the optimal models for predicting LST. After tuning these models using Bayesian optimization, we achieved a mean R2 of 0.86 during summer and 0.84 during winter. After developing the hyperparameter-optimized model, explainable AI, e.g., SHAP, was employed to understand the complex nonlinear dynamics and top contributing features. Landcover variables had a more dominant impact on the spatial distribution of summer LST, while winter LST was more influenced by pollutant parameters. Partial Dependency Plot and Accumulated Local Effect were further incorporated to examine the marginal effects of the top-contributing features on spatial LST prediction. By extending the study area to the entire US Southwest, this study effectively captures urban–rural contrasts, climate- and land-cover–dependent pollutant responses, and regional climatic influences. It presents explicit spatial dependencies among LST, pollutants, land cover, topography, and nighttime activity that will aid future researchers and policymakers in effectively developing sustainable thermal planning for urban activities.
Mitra et al. (Sun,) studied this question.