# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 1 Introduction  
Currently, more than half of the world's population lives in cities, a figure projected to increase to two-thirds by 2050 [1]. This rapid urbanization presents one of the most pressing challenges of the 21 st century: the sustainable management of urban planning and the enhancement of urban public spaces to promote both physical and mental well-being at individual and societal levels [2]. In response to these challenges, the convergence of effective urban governance and the digital revolution has precipitated the metamorphosis of 'traditional cities' into'smart cities'. These cities leverage environmental data collection, integration, and analysis to improve citizens' quality of life and optimize decision-making and management processes [3]. Numerous networks and organizations play a critical role in fostering global collaboration among stakeholders to advance and implement smart city technologies. Key initiatives include Open and Agile Smart Cities [4], Global Smart Cities Alliance [5], Program on Smart Cities and Inclusive Growth [6], and The Smart Communities Network [7]. Within this framework, the Internet of Things (IoT) has enabled diverse applications in smart cities and buildings, spanning mobility [8], waste management [9], healthcare [10], environment, and more [11]. Monitoring indoor comfort and occupant well-being has seen significant progress [12]. The Public Space Charter underscores the importance of adapting public spaces to local climatic conditions, not only to enhance environmental quality but also to mitigate climate change and urban heat island (UHI) effects [13]. This issue has become increasingly salient due to the significant impact of climate change on urban environments. The IPCC warns of rising global temperatures and more frequent, intense heatwaves, which are expected to increase heat-related mortality, especially in densely populated cities--necessitating robust adaptation strategies [14]. A key phenomenon in this context is the urban heat island effect, characterized by elevated temperatures in urban areas compared to their rural surroundings, particularly during summer [15]. The phenomenon is primarily attributable to anthropogenic activity and urban infrastructure, deforestation and shading, and using heat-absorbing materials [16]. Long-term, high-resolution studies of diurnal surface temperatures reveal that 3-day extreme temperature differences between urban and rural areas can exceed the seasonal median by more than double, with local variations reaching up to 10 \({}^{\circ}\)C [17]. In small and medium-sized cities, these differences typically range from 2 to 7 \({}^{\circ}\)C [18; 19; 20; 21; 22]. To define and assess UHIs and develop mitigation strategies, it is essential to analyze hydrothermal data from sources such as satellites, meteorological stations, climatological records, and Earth observation systems [23]. The WMO provides guidelines for measuring climatic variables at different scales to ensure measurements' accuracy, consistency, and quality from in situ sensors (e.g., those used in weather stations) and complementary sources such as satellites, weather radars, and climate prediction models. These guidelines encompass a range of aspects, including measurement instruments (the definition of sensor requirements in terms of accuracy, consistency, quality standards, calibration, and so forth) and the measurement techniques, which encompass both manual observations (with traditional weather stations) and automated methods (with remote monitoring) [24]. Satellite and remote sensing data have enabled systematic SUHI studies [25], including MODIS [26], Landsat [27], and, to a lesser extent, Sentinel [28]. However, satellite data face inherent limitations, including inadequate resolution, the difficulty correlating surface and canopy temperatures [29], the necessity of clear sky conditions for precise measurements, and the complexity of providing three-dimensional urban information [30].