A Novel Pedestrian-Level Approach to Land Surface Temperature Estimation Using Wearable Environmental Monitoring and Urban Microclimate Modeling

Rapid urbanization and global warming exacerbate urban heat stress, demanding innovative approaches to understanding cities’ thermodynamics. Land Surface Temperature (LST) is an important parameter in urban overheating studies as it can be related to heat storage, which contributes to elevated air temperatures and increased thermal stress. LST is commonly derived from satellite images, but this technique is limited by cloud cover and low spatial resolution. Our study presents a novel methodology for estimating LST at the pedestrian level based on mobile monitoring integrated with fixed weather station data to drive high-resolution urban microclimate simulations and provide detailed LST. Specifically, air temperature data was gathered using a wearable system to ensure it was monitored near pedestrians. These data were integrated with air temperature registered with fixed weather stations (located above the canopy level) to obtain daily temperature profiles within the urban canyon. These values, together with a QGIS-generated 3D model of an urban area, were then used as input in the high-resolution urban modeling software ENVI-met, which provided the LST with a high spatial resolution (5m). This methodology was applied in three case studies in New York City (East Side, Financial District, and Queens), with mobile monitoring sessions conducted twice a day during the summer and fall of 2024. The modeled areas were approximately 240m × 120m to optimize the simulations. The LST estimated with the proposed framework demonstrated a strong correlation with satellite measurements, validated by GOES-16 and LANDSAT-9 data, with RMSE values ranging from 1.93K to 2.66K and uncertainty bands overlapping 88-96%. Moreover, the proposed method was effective in determining LST under all weather conditions, unlike satellite measurements. The primary contribution of this research lies in establishing a validated framework for obtaining high-resolution LST data at the pedestrian level, contributing to enhanced urban climate resilience through improved thermal monitoring.