# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 4 Discussion
### Implications for Understanding Extreme Heat in Mid-Atlantic Cities  
Official air temperature maxima reported for our study areas (Richmond, Baltimore, and Washington DC's National airports) on the dates of our heat island assessments were systematically lower by an average of 4.6 \({}^{\circ}\)C (>8 \({}^{\circ}\)F) than the maximum temperatures observed during our traverses (Table 1), and those modeled in our heat surfaces. Static, sensor-based studies of urban minimum air \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Sentinel-2 Band**} & \multicolumn{2}{c}{**Index**} \\ \cline{2-3}  & **NDVI** & **NBAI** \\ \hline
**B2** & \(-\)0.3249 & 0.7011 \\
**B3** & \(-\)0.2452 & 0.6884 \\
**B4** & \(-\)0.2941 & 0.7378 \\
**B5** & 0.0019 & \(-\)0.0002 \\
**B6** & 0.0085 & \(-\)0.0003 \\
**B7** & 0.0080 & \(-\)0.0004 \\
**B8** & 0.8896 & \(-\)0.4800 \\
**B8A** & 0.0073 & \(-\)0.0004 \\
**B11** & 0.0051 & \(-\)0.0008 \\
**B12** & 0.0036 & \(-\)0.0006 \\ \hline \end{tabular}
\end{table} 
Table 4: Correlations between common land use-linked indices and raw Sentinel-2 bands. temperatures in the City of Baltimore [42] previously identified LULC as a significant predictor of temperature minimums, though it has a three limitations, which the present study addresses. First, while this and earlier studies confirmed a distribution of temperatures across urban areas, they often failed to correlate those differences to land use types such as tree canopy and distance from park areas. Second, the placement of stationary sensors may provide high temporal variability, though with a large physical extent like Baltimore, spatial extent to capture diversity LULC is essential for model validity. Finally, while an ordinary least square regression provides specific coefficients, the integration of RF is a significant advancement in the capacity for predicting temperatures, and hence, offers a means for actively addressing planning options. In addition, our modeled and observed minimum temperature range for Baltimore (Figure 3b) far exceeded that of previous studies, which provides further evidence for applying RF to produce highly predictive temperature surfaces as well as traverse observations. Ongoing work with other cities across the country will likely corroborate findings using sensor-based estimates of ambient urban temperatures. The results suggest that some neighborhoods contain higher heat during an individual event, and may also have significantly longer heat seasons. Extreme heat warnings issued by the National Weather Service before and during heat waves may rely solely on temperatures from these official stations, thereby inadvertently underestimating the apparent health risk to communities that suffer higher urban heat. These temperature maps can help to prioritize action areas for engaging those neighborhoods that are consistently hotter than official temperature records. Additionally, long-term temperature measurement and monitoring can help to inform the timing of public health warnings about heat. Understanding how the built environments amplify or mitigate extreme heat within specific neighborhoods is critical to building resilience to future heat extremes. Additionally, our findings highlight the clear need to better understand the interactions between areas containing extreme urban heat and socioeconomic factors that may describe the adaptive capacity of the community. Throughout the campaign, volunteers noted the relationship between lower income areas of each city and the lack of heat mitigating features, including trees, open space, and/or lighter coloring on surfaces. While an increasing body of literature is confirming these anecdotal claims, having precise and accurate predictions of urban heat within all urban areas will help to advance preventative measures for reducing far-reaching impacts from future heat events.