# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 2 Literature Review  
The widespread adoption of UAVs and the integration of various sensors have significantly expanded their range of applications. A critical examination reveals important methodological patterns, technological advancements, and research gaps across diverse domains of these applications. UAV-based thermal platforms have evolved across multiple application domains, each contributing to the methodological advancement. In agricultural contexts, [PERSON] al. [15] pioneered the high-resolution monitoring of plant water status in vineyards, while [PERSON] et al. [16] and [PERSON] et al. [17] extended these approaches to wine cellars and crop phenotyping, respectively. These agricultural applications established fundamental approaches for surface temperature characterization. Concurrently, [PERSON] et al. [18] developed methodologies for evaporation studies, while [PERSON] et al. [19] demonstrated archaeological applications, and [PERSON] et al. [20] applied thermal imaging to building energy assessment. This cross-disciplinary development reflects the progressive refinement of UAV thermal methodology, though most early applications focused on limited temporal sampling. Sensor performance studies have been critical to methodological advancement. [PERSON] et al. [21] systematically documented internal temperature fluctuations in thermal sensors, while [PERSON] et al. [22] provided comparative analysis between ground and airborne thermal measurements. These foundational studies identified key measurement challenges subsequently addressed through radiometric calibration procedures developedby [PERSON] et al. [23] and [PERSON] and [PERSON] [24]. Further refinements addressed emissivity effects ([PERSON] and [PERSON] [25]) and camera angle influences ([PERSON] et al. [26]). This methodological progression reflects increasing sophistication in addressing measurement accuracy--a critical consideration when comparing results across studies conducted in different urban environments. The comparative assessment of urban thermal studies reveals considerable variation in spatiotemporal frameworks. [PERSON] and [PERSON] [27] documented from 3.9 to 15.8 \({}^{\circ}\)C LST differences across urban land covers in Milwaukee and El Paso through single-time-point monitoring. Similar methodological approaches by [PERSON] et al. [28] quantified tree shading effects, finding 12 \({}^{\circ}\)C reductions for asphalt and 5.7 \({}^{\circ}\)C for concrete surfaces in Medellin. These point-in-time studies provided valuable surface comparisons but lacked temporal dynamics. Material-specific thermal behaviors have been documented across diverse urban contexts. [PERSON] et al. [29] demonstrated that dark rooftops exhibited higher temperatures than light-colored alternatives, while [PERSON] et al. [30] confirmed similar patterns in cold climate regions--suggesting consistent physical principles despite contextual variation. [PERSON] and [PERSON] [31] documented extreme temperatures exceeding 50 \({}^{\circ}\)C in South Korean waterfront parks, while [PERSON] et al. [32] integrated thermal assessments with ventilation analysis across 31 Chinese cities, finding that improved ventilation could reduce energy consumption by 6%--highlighting the importance of combining thermal assessment with urban morphology analysis. Methodological comparisons between UAV and satellite-based approaches have established complementary strengths. [PERSON] et al. [33] demonstrated that UAV-derived LST revealed finer-scale details than MODIS and Landsat in Chilean cities, while [PERSON] et al. [14] used UAVs to observe how paved surfaces exhibited elevated LST and analyzed potential thermal runoff effects on nearby water bodies--capturing thermal relationships undetectable in coarser resolution data. [PERSON] et al. [34] quantified this advantage, finding UAV-derived LST significantly outperformed Landsat in South Korean parks. [PERSON] and [PERSON] [35] further confirmed this pattern for micro-urban heat island detection in Dallas-Fort Worth.