Surface urban heat islands (SUHIs) are influenced by the spatial distribution of green space, which in turn can be influenced by urban planning. When studying the relationship between structure and function it is critical that the scale of observation reflects the scale of the phenomenon being measured. To investigate the relationship between green space pattern and the SUHI in the Kansas City metropolitan area, we conducted a multi-resolution wavelet analysis of land surface temperature (LST) to determine the dominant length scales of LST production. We used these scales as extents for calculating landscape metrics on a high-resolution land cover map. We built regression models to investigate whether–controlling for the percent vegetated area–patch size, fragmentation, shape, complexity, and/or proximity can mitigate SUHIs. We found that while some of the relationships between landscape metrics and LST are significant, their explanatory power would be of little use in planning for green infrastructure. We also found that the relationships often reported between landscape metrics and LST are artifacts of the relationship between the percent of vegetation and LST. By using the dominant length scales of LST we provide a methodology for robust biophysically-based analysis of urban landscape pattern and demonstrate that the contributions of green space configuration to the SUHI are negligible. The simple result that increasing green space can lower LST regardless of configuration allows the prioritization of resources towards benefiting neighborhoods most vulnerable to the negative impacts of urban heat.

Heat exposure in outdoor work environments poses risks to worker health and productivity. Engineering solutions like cool surfaces that increase surface albedo and reduce temperatures may help mitigate these impacts. We conducted detailed micrometeorological modeling to analyze surface characteristics and heat exposure for outdoor workers at San Francisco International Airport (SFO) under current conditions and three hypothetical albedo-increase scenarios. Wet bulb globe temperature (WBGT) was used to estimate potential productivity loss based on established relationships between heat stress and loss in physical work capacity. For the month of August 2020, we quantified possible gained hours of productivity per worker per month under each hypothetical albedo-increase scenario. Across the entire area of SFO, the average campus albedo was 0.20 (range: 0.08–0.85). Adopting low, moderate, and high albedo modifications for SFO would reduce peak midday WBGT by 0.89, 1.25, and 1.59 °C, respectively. The largest temperature reductions occurred during the morning shift (7 AM–3 PM). In one shift, we found a potential of 5.20, 7.16, and 8.95 h gained per worker over the entire month in the low, moderate, and high albedo modification scenarios, respectively.

Extreme urban heat poses a growing threat to global health, yet a critical cooling intervention-reflective cool roofs-lacks the high-resolution data needed for building-level planning. While satellite-based albedo estimates are globally available, their coarse resolution cannot resolve individual rooftops, limiting targeted climate action. Here we show that fusing freely available 10-m Sentinel-2 imagery with high-resolution satellite data enables urban albedo mapping at 30-cm resolution. This approach combines the radiometric accuracy and global coverage of Sentinel-2 with the spatial detail of commercial imagery to produce actionable urban datasets. Validated against airborne hyperspectral measurements (root mean square error = 0.04), the method resolves albedo at individual building footprints across diverse urban environments. Applied to 12 global cities, our analysis shows that prioritizing large-footprint buildings for cool roof retrofits can reduce citywide temperatures by up to 0.5 °C. This work validates Sentinel-2 for city-scale albedo estimation and enables globally deployable, building-scale rooftop albedo mapping to support targeted cool roof interventions.