## 2 Materials and Methods
### Urban Landscape Data Collection and Processing  
This study considered urban morphometric features such as vegetation coverage and built-up characteristic (building height and building volume) that were derived from a variety of geospatial datasets and used for later spatial modeling. LiDAR point cloud data collected in the state of Iowa during the years 2019-2022 were used to estimate urban  
Figure 2: Temperature sensor devices (Adafruit Sensirion SHT40).  
vegetation and building morphology. The original data have a minimum of 2 points per square meter with a vertical and horizontal accuracy of 95% [34].  
Using ArcGIS Pro (3.0.1, ESRI, Redlands, CA, USA) (3D Analyst tools) and arcpy/Python automation, the LiDAR point cloud data were classified into ground points, vegetation points, and building points, which were then rasterized to represent ground density, vegetation density, and buildings; leading to the creation of data models representing the bare-earth (digital elevation models), and above ground features existent in the study areas (digital surface models). In addition, in order to accurately represent the shape and surface area of buildings, the LiDAR point cloud data were also processed along with building vector polygons obtained from individual counties and cities GIS departments, to construct the final building multi-patch volumetric feature classes. Finally, according to the literature, an important component in explaining urban temperature patterns is vegetation. For that reason, the 60 cm 2021 Iowa NAIP imagery downloaded from the National Oceanic and Atmospheric Administration [35] was used to create individual Normalized Difference Vegetation Index (NDVI) raster files for each urban area. Other studies have used NDVI also either from aerial imagery (e.g., [17]) or satellite imagery (e.g., [24]).  
A total of five main independent variables were created from LiDAR point cloud data, and NAIP Imagery: Canopy Cover (CC): the proportion of vegetation compared to the amount of bare-earth in a given area (derived from LiDAR point cloud data classified as bare-earth and as vegetation points). The results are expressed in pixel values ranging from 0 to 1, where 0 represents no vegetation, and 1 represents fully vegetated area; Canopy Density Metric (CDM): the result of pixel values of CC multiplied by pixel values found in the normalized digital surface model (nDSM) which represents the relative height of features from the digital surface models; Building Height (BH): the average height of buildings expressed in meters for each individual structure; Building Volume (BV): the volume of buildings expressed in cubic meters; Normalized Difference Vegetation Index (NDVI): the value per pixel ranging from minus one (\(-1\)) to plus one (\(+1\)) representing the density, intensity of vegetation as well as vegetation health. The Focal Statistics tool in ArcGIS Pro Spatial Analyst Extension was used to derive neighborhood raster datasets for the five independent variables Neighborhood statistics (mean and standard deviation) were derived per 1 m\({}^{2}\) cell with fixed neighborhood distances of 50, 100, 200, 400, and 800 m. This methodology has been utilized previously when using RF for modeling to investigate the surrounding urban conditions effect on the variation of air temperature [17; 23; 24].  
To model the temperature patterns over various Iowa cities, the measured temperature was used as the dependent variable while neighborhood statistics for each of the independent variables was derived as a raster dataset (described below) after being resampled to be 1 m resolution.