# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 1 Introduction  
Projections of global climate change under a high carbon emissions scenario [1] suggest that much of the world's population will eventually endure an average annual surface temperature warming of 2 \({}^{\circ}\)C relative to the preindustrial period with significant amplification and lengthening of extreme heat waves. The impacts of high temperatures and increasing rate of heat waves affect many elements of urban infrastructure, including transport networks, power, potable water supply, food distribution networks, waste management facilities, and telecommunication networks. Urban heat poses an additional threat to human life, since it is the leading cause of weather-related fatalities and a major contributor to summertime morbidity and has specific impacts on those communities that are more vulnerable, such as those with pre-existing health conditions (e.g., chronic obstructive pulmonary disease, asthma, cardiovascular disease, etc. ), limited access to resources, and the elderly. Excess heat limits the human body's ability to regulate its internal temperature, which can result in increased casesof heat cramps, heat exhaustion, and heatstroke and may exacerbate other nervous system, respiratory, cardiovascular, genitourinary, and diabetes-related conditions [2]. While air conditioning provides relief from intense heat, the resulting increased energy use can place pressure on electricity infrastructure and raise the cost of electricity production [3; 4]. Furthermore, higher electricity prices for low-income households can lead to energy poverty, reducing the capacity to live with proper environmental protection and raising the risk of excess mortality in both the summertime and wintertime [4]. As these conditions worsen with the rising frequency, duration, and intensity of extreme heat events, the need for methods to understand and adequately address the associated disparate impacts becomes essential. Current approaches to describing the spatial distribution of urban heat can be characterized into two categories: ground-based measurements of ambient air temperature and satellite-derived land surface temperatures. Common methods of intra-urban heat analysis use thermal infrared (TIR) satellite data [5; 6; 7; 8]. However, these methods do not present human-experienced ground-level temperature predictions due to the low resolution of satellite-based TIR band. Thus, it is inappropriate to use TIR-capable platforms such as Landsat or Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for urban heat island effect (UHI) analyses in which human comfort and/or health are concerned [6]. The alternative approach is to collect ground-based measurements (\"traverses\") and create models of observed temperatures across urban areas. This collection method has been used successfully both recently and before the existence of satellite-based TIR data [9; 10; 11; 12; 13; 14; 15]. Although the exact techniques vary, the overall methodology persists throughout the traverse-based literature: the use of moving temperature measurement devices throughout an urban environment to assess the changes in temperature, based on land use/land cover (LULC) configuration. Within the traverse-based UHI literature, there has been a trend toward using spatial interpolation techniques to estimate the temperature in areas not visited by the mobile traverse [16]. We argue that an interpolation-based technique for traverse-based UHI is inadequate. Though some interpolation techniques include advanced features such as decay functions (Inverse Distance Weighting) and directionality (Kriging), they require spatial uniformity of observations in order to accurately estimate values for unobserved locations [17]. For a traverse-based temperature observation technique, no such uniform distributions of observation points have been observed in the literature at a city-wide scale.