Shade provision is the most effective strategy for mitigating heat in cities; yet its distribution remains highly uneven. Using high-resolution simulations of shade casting from buildings and trees on pedestrian areas, combined with socioeconomic data at the neighbourhood level, we assess shade availability across nine climatically and geographically diverse cities: Amsterdam, Barcelona, Belém, Boston, Hong Kong, Milan, Rio de Janeiro, Stockholm, and Sydney. Our results reveal a consistent pattern of spatial and socioeconomic inequality: lower-income and peripheral neighbourhoods tend to receive significantly less shade on sidewalks, despite facing greater heat vulnerability. Notably, inequality persists even in cities with high overall shade coverage, where wealthier areas benefit from disproportionate abundance. By focusing on public pedestrian spaces, rather than general coverage, this study highlights the importance of measuring heat burden through the lens of human experience. We call for equity-centred adaptation strategies that target shade provision where it is most needed, particularly in underserved and exposed communities.

Understanding the UHI effect in any city requires high-resolution temperature data. This data is often difficult to obtain as cities usually have only a few ground sensors, leaving large data gaps. To fill these gaps, we compare Landsat-derived land surface temperature (LST) with air temperature (Tair) measurements from urban weather stations in the two largest cities in the Netherlands. Previous studies of this kind have often been limited due to a few main factors: low spatial resolution, limited weather station data and small sample sizes (Chung et al., 2020, Mutiibwa, 2015; Sheng 2017; Xiong, 2017; Yang, 2020). As a result, findings have been inconsistent, albeit mostly promising. Addressing these issues and adding to Burnett and Chen’s (2021) extensive comparison on a regional scale in Ontario, Canada, we present a reproducible, code-based approach focusing on cities. Using 149 Landsat scenes and data from 33 urban weather stations in the Netherlands (24 in Amsterdam, 9 in Rotterdam) between 2013-2022, 1700 comparison points across all European seasons are established. We find that there is a strong positive and significant linear relationship between LST and Tair across the dataset (r = .89). OLS regression results indicate 80% of the Tair variation can be explained by the LST, with Tair increasing by 0.62°C for every 1°C increase in LST. Analyses were repeated to account for seasonality, each station's local climate zone (Stewart and Oke, 2012) as well as mean absolute error and root mean square error to interrogate the discrepancy, all of which will be highlighted in the presentation. Overall, our evidence suggests that LST can indeed be a suitable proxy for Tair and could consequently form an additional decision-making layer to assist climate monitoring and urban planning in the Netherlands as well as similar climates.