Abstract
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.