**1. Introduction**

Urban areas across the world are subject to thermal stresses caused by the surface urban heat island (SUHI) effect where urban land surfaces experience higher temperatures than their surrounding rural areas. This is in large part due to the replacement of undeveloped vegetated land with anthropogenic materials that absorb more solar radiation and have different heat capacity and surface radiative properties [1]. This results in higher surface temperatures that pose a significant threat to human health [2], as well as higher storm runoff temperatures that can harm aquatic life [3–5]. These stresses are only expected to grow with increases in global temperatures and urban populations; therefore, it is critical that we understand the fundamental processes that drive land surface temperature (LST) to develop solutions that can protect human and environmental health.

To that end, thermal remote sensing is an important tool for evaluating urban land surface temperatures. This includes satellite sensors such as ASTER, MODIS and Landsat that can capture land surface temperatures at 30 m–1 km resolutions [6]. Data from these satellites have been used to extensively study urban land surface temperatures and their effects [7–14]. However, while satellite remote sensing is valuable for evaluating LST across a city scale, the spatial resolution precludes its applications to smaller spatial scales that better reflect the spatial complexity of the urban environment. To acquire higher resolution thermal data, studies have used aerial reconnaissance or downscaling

techniques [15,16]; however, these are still at resolutions (4–10 m) that cannot capture changes that occur on a sub meter resolution. Furthermore, satellite remote sensing is temporally constrained to intervals between 1–14 days. Aerial flights do not have the same temporal constraints; however, doing so at on-demand temporal resolutions would not be economically practical. Therefore, these methods are inadequate for evaluating changes in urban LST that occur throughout the day or capturing the spatial heterogeneity of urban LST at small scales.

This challenge is important to overcome as urban land surfaces are spatially complex and significant variations in land cover can occur on a sub meter spatial resolution [17]. While existing research has demonstrated that the spatial configuration of land use classifications at a city scale are important (i.e. industrial, residential, forest) [18,19], less is known about the importance of the spatial configuration and variations in LST at smaller scales (i.e., sidewalks, grass medians, flowerbeds, etc.). In addition, the urban environment is dynamic and land surface temperatures can be significantly influenced by other factors besides land cover material properties [20]. Land surface temperature may therefore vary significantly across small spatial scales; however, the factors that control this variation are not well defined. Doing so requires direct measurements of surface temperatures across wide spatial and temporal scales, yet little research to date has evaluated the spatial variability in temperature among urban land use types in sub-meter resolutions. This may be due to measurement limitations, as satellite data is too coarse and in-situ temperature probes are too expensive to densely distribute across an urban landscape. Therefore, new and innovative approaches to measuring land surface temperatures at small spatial and temporal scales are needed to assess thermal variability across land use types in the urban environment.

Unmanned Aerial Vehicles (UAVs) or drones, are a technology that can meet this challenge. Recent advances in UAVs and radiometric thermal cameras have made it possible to capture land surface temperatures on-demand and at sub-meter spatial resolutions that accurately reflect the spatial complexity and detail of land surface temperatures in the urban environment [21]. UAVs also have advantages in that they can be flown on demand to capture LST at temporal resolutions unmatched by satellite or aerial imagery. While the limited battery life of around 30 minutes for quad-copter UAVs constrains the area that can be captured in a single flight, their spatial and temporal resolutions offer significant advantages for evaluating the variability of LST in the urban environment at fine spatial and temporal scales.

We therefore present a study to evaluate the variability of temperatures across urban land surfaces using a UAV. In this study, we apply a UAV and radiometric thermal camera to capture land surface temperatures at high-resolutions (13 cm) in two case study locations: Milwaukee, Wisconsin and El Paso, Texas. Using data collected throughout a calendar year, we evaluate the variability in land surface temperatures, develop models to predict mean land surface temperature based upon weather parameters and evaluate the diurnal trends in urban land surface temperature. To do so, we (1) quantify land surface temperature variability across different surface types, (2) evaluate variance in temperature across different surface types based upon meteorological and/or other derived parameters (e.g., albedo, normalized difference vegetation index, apparent thermal inertia, etc.), (3) predict land surface temperature based upon meteorological parameters and (4) assess diurnal variability in land surface temperature magnitude and uncertainty. Ultimately, this study helps to elucidate factors that contribute to land surface temperature variability in the urban environment at small spatial scales, which can then be applied to develop better temperature mitigation strategies.

#### **2. Materials and Methods**

### *2.1. Case Study Locations*

Two case study locations were chosen for this project: (1) a portion of Marquette University's campus in Milwaukee, WI and (2) a portion University of Texas El Paso's (UTEP) campus in El Paso, Texas (Figure 1). The Marquette and UTEP case study areas were roughly 21,300 m<sup>2</sup> and 27,300 m<sup>2</sup> , respectively and included a balance of both natural landscape and impervious gray surfaces. Surface types within each case study location were manually delineated using ESRI's ArcMap software. The nine surfaces types identified at Marquette and UTEP and their respective surface areas are listed in Table 1. The specific locations on each campus were chosen for their variety of surface types, similarities in land use between the two locations and suitability for drone takeoff/landing and flying. In addition, these locations provide a contrast in geography, climate and weather that are helpful in testing the generalizability of our findings. For example, Milwaukee's climate is classified by Koppen and Geiger as Dfa (Humid Continental Hot Summers With Year Around Precipitation) and receives 870 mm of precipitation annually, while El Paso is classified as BWk (Cold Desert Climate) and receives 221 mm of precipitation annually [22,23].

**Figure 1.** Visual imagery of the case study locations: Marquette University (**a**) and University of Texas El Paso (UTEP) (**b**). Visual imagery of Marquette was captured from a drone on 11 August 2018. Visual imagery of UTEP was pulled from Google Maps on 13 March 2019.


**Table 1.** Surface types and surface areas within each case study location.
