*3.3. Surface Properties and Land Surface Temperature*

Drone data was applied to derive surface properties including albedo, NDVI and ATI, of the surface types in the case study (Table 6). The light concrete parking lot exhibited the highest albedo (0.673) while grass exhibited the lowest (0.317). The spatial distribution of temperature, albedo, NDVI and ATI at the Milwaukee, WI case study location is shown in Figure 7. As illustrated, these surface material properties have a large degree of variation across the case study area.

**Table 6.** Average albedo, normalized difference vegetation index (NDVI) and apparent thermal inertia (ATI) values for each surface type.


**Figure 7.** Spatial distribution of tempearture (**a**), albedo (**b**), NDVI (**c**) and ATI (**d**) for a flight recorded on 11 August 2018.

To further explore this variability and assess its impact on surface temperatures, we plotted these surface properties against land surface temperature. Figure 8 illustrates temperature plotted against its respective albedo for the 611,460 total data points captured by the drone imagery and results show clusters that form for different surface types. Some of these clusters exhibit either a (1) low range in albedo and high range in temperature or (2) high range in albedo and low range in temperature. For example, the road exhibits a low range in albedo and high range in temperature, implying the variability in roadway temperatures are more dependent on meteorological (e.g., exposure to solar radiation) and human (e.g., traffic) variables than physical properties (e.g., albedo). On the other hand, the parking lot has a higher but similar range in albedo, yet it has a much lower variability in temperature. This could be due to the fact that the parking lot has a range of materials from asphalt to concrete coupled with a much lower level of traffic as compared to the roadway, which is more homogenous and experiences constant vehicular traffic that intercepts land surface exposure to solar radiation. Therefore, this graphic may support the previous statement that there are anthropogenic variables, such as intermittent human foot or vehicular traffic, that are significant to land surface

temperature processes. Overall these results suggest that patterns in the physical properties of urban materials may provide insight into surface temperature variability.

**Figure 8.** Surface temperature data plotted against albedo from a flight recorded on 11 August 2018.

#### *3.4. Temperature Prediction Models*

Drone observations were applied to develop empirical models of land surface temperature. These include (1) a regression model to predict spatially averaged surface temperatures at 12:00 PM based upon environmental variables and (2) a diurnal model to predict surface temperatures throughout a given day.
