Area-adjusted influence of built-up pattern on urban land surface temperature in an arid landscape

Urban heat intensification threatens environmental quality and human health, particularly in arid urban areas where cooling resources are scarce. While impervious surface area is a well-established driver of urban land surface temperature (LST), the influence of built-up spatial patterns beyond their total area remains poorly understood. This study aimed to disentangle the effect of built-up pattern after accounting for built-up area on LST using a dual-scale analysis in an arid urban–barren mosaic. The spatial pattern of Sentinel-2 10 m–derived built-up areas, measured as Area, Mean Patch Size (MPS), Shape Index (SHI), Nearest Neighbor Distance (ENN), and Number of Patches (NP), was extracted within each 100 m Landsat thermal pixel, creating a dataset of 8,130 observations. Landsat-derived summer LST served as the dependent variable in a set of flexible regression models designed to evaluate both the total extent of built-up land and the spatial structure of urban patches. Results showed that adding configuration metrics to Area-only models improves LST prediction accuracy (R² = 0.589 to 0.693) and reduces RMSE (2.792 °C to 2.409 °C). Additional models examined how different spatial metrics interact, allowing the influence of built-up patterns to be evaluated independently of total built-up area. Unsupervised clustering (average silhouette width = 0.295) revealed that, when controlling for area, Compact configurations, low NP with high MPS, SHI, and ENN, were on average 2.7 °C warmer than Fragmented or Balanced types. These findings demonstrate that, after adjusting for built-up extent, built-up pattern exerts a measurable and spatially distinct influence on LST, underscoring its importance for climate-responsive urban design in arid regions.