Land surface temperature (LST) derived from thermal infrared (TIR) satellite data is widely used in urban climate research due to the repetitive availability of data over large areas worldwide. LST directly reflects the interactions between urban surfaces, the atmosphere and human activities, supporting hotspots identification, comfort indices estimation or mitigation strategies planning. However, its use is limited by the spatial and temporal resolutions of current spaceborne sensors. The upcoming TIR satellite missions (LSTM, TRISHNA, SBG), with spatial resolution between 37 and 60 m and up to 3-days revisit, open up new opportunities to study urban climate at the neighborhood scale. At this scale, retrieving accurate and comparable LST over cities remains a challenge. Urban heterogeneity and 3D structure greatly impact satellite measurements, requiring a good understanding of 3D radiative processes for reliable LST estimates. Another challenge is the transition to air temperature, which is essential for improving comfort and quality of life in cities.

To address these challenges, a model chaining approach is implemented to generate physically coherent datasets linking remote sensing measurements to microclimate variables over any urban configuration in order to investigate how they relate to each other. On the one hand, the DART radiative transfer model simulates radiative exchanges in the urban canopy and the corresponding remotely sensed images, provided that the surface temperature distribution in the 3D urban scene is known. On the other hand, the thermo-radiative model SOLENE-microclimat simulates the surface temperature distribution in the 3D scene required by DART, as well as the air temperature in the canopy but does not allow the simulation of multispectral satellite data. Chaining the two models bridges the gap between remotely sensed TIR parameters and microclimate variables. This presentation gives an overview of the modelling chain and presents some concrete examples of its application to urban climate studies.

How to cite: Roupioz, L., Rodler, A., Lauret, N., Musy, M., Gastellu-Etchegorry, J.-P., and Briottet, X.: Chaining the DART and SOLENE-microclimat models to support the use of TIR satellite data in urban climate studies, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-136, https://doi.org/10.5194/icuc12-136, 2025.

Urban thermal anisotropy (UTA) significantly distorts satellite-derived land skin-surface temperatures (LST) and surface flux estimates, posing a major challenge to understanding global urban climate dynamics. Despite decades of research, a comprehensive map of urban thermal anisotropy across global cities with varying climatic contexts remains a knowledge gap in Earth observation. This gap hinders accurate estimation of critical urban climate variables and undermines the reliability of a wide range of urban climate studies that increasingly rely on satellite-based thermal data. Leveraging the extensive archive of multi-angle thermal remote sensing data, here we present a novel, statistically robust, data-driven approach that departs from traditional complex model-based methods to directly quantify urban thermal anisotropy across global cities. Our findings reveal that the global mean UTA intensity exhibits seasonal variation, peaking at 5.1 K during summer daytime. Compared to nadir LST measurements, UTA-induced biases in satellite-derived urban sensible heat flux and surface urban heat island intensity can lead to substantial underestimations (> 40.0%) when using LST data from sensor viewing zenith angles (VZAs) of ±60°. However, using LST data from sensor VZAs within ±30° can limit these errors to within ±10%. Finally, we formulate a data-driven, globally applicable approach to correcting angle-dependent biases in satellite thermal observations across global cities.

How to cite: Zhan, W. and Du, H.: Potential biases in remotely sensed urban climates as revealed by urban thermal anisotropy mapping across global cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-644, https://doi.org/10.5194/icuc12-644, 2025.

The most representative sensors that make up the Autonomous Vehicle sensors’ ecosystem are RADAR, LiDAR (Light Detection and Ranging), ultrasonic, GNSS and cameras. These onboard sensors measure wave sources (mostly based on phase observations), allow for redundancies, and have distinct properties to perform specific tasks, such as positioning or obstacle detection. They either measure one-dimensional ranges, record 3D point clouds of their environment or process signals from medium Earth orbit satellites. Each of those phase observations is affected by their path through the atmosphere and allows, with suitable manipulation and filtering, the derivation of the spectrum of turbulent phase fluctuations and the estimation of its parameters, such as the cutoff frequency using the valuable von Kárman assumption. The strength of the fluctuations can be additionally estimated, a quantity related to the structure constant of the refractive index. In this presentation, I will show how it would be possible to “extract” the turbulence spectrum from AV sensors measurements with the goal to advance atmospheric turbulence research, especially within urban settings, by enhancing real-time monitoring through a dense network of sensors deployed via Autonomous Vehicle fleets. I will present some outcomes, spanning from noise analysis, Large Eddy Simulation validation, and extreme weather nowcasting uncertainty reduction.

How to cite: kermarrec, G.: Turbulence Mapping with Autonomous Vehicle Fleets, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-39, https://doi.org/10.5194/icuc12-39, 2025.

Urban heat storage (Qs) is an essential component of urban surface energy balance. Qs is the main factor for urban heat island (UHI) at nighttime. The quantitative contribution of Qs to UHI is still unclear, due to the lack of a spatio-temporal continuous Qs dataset. In this study, firstly, we developed an urban surface thermal inertia model using hourly LST of Himawari-8. Secondly, the hourly Qs in three urban agglomerations in China was simulated by the heat diffusion equation and Fourier’s law for heat conduction, using the simulated urban thermal inertia and Himawari-8 LST. Thirdly, the relationship between Qs and air temperature (Ta) was studied. Based on the in-situ observation, the accuracy of urban thermal inertial in this study was higher than other model, RMSE, MAE, R² were improved from 4.65 K, 3.58 K and 0.88 to 1.86 K, 1.53 K and 0.97. In addition, Qs were validated by the observed Qs (from flux tower observation) in Beijing, Shanghai and Guangzhou, R² could be up to 0.92. Results showed that, Qs was more consistent with Ta at nighttime than daytime, with R² of 0.96 and 0.1, respectively. During nighttime, the high-rise building has higher Ta than low-rise building, due to higher Qs and release more energy than low-rise. In natural surfaces, water has larger Qs and higher Ta than dense trees. The loop (scatterplot of hourly Qs and Ta) shape were different at LCZs. Based on the loop area and slope, we found that high-rise building had higher UHI but varied quickly, however, low-rise UHI is lower but would last longer. The water surface in night is also heat source and has a longer time UHI. Therefore, the high-rise building and water surface are not conductive to alleviating the nighttime UHI.

How to cite: Li, N., Guo, F., Dou, J., Ma, Y., and Miao, S.: Remote sensing-driven analysis of hourly urban heat storage and its effects on urban heat islands in Chinaemote sensing-driven analysis of hourly urban heat storage and its effects on urban heat islands in China, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-75, https://doi.org/10.5194/icuc12-75, 2025.

This study advances remote sensing and microclimate modeling through the development of VoxPy, a novel approach to derive spatially-explicit leaf area density (LAD) profiles from terrestrial (TLS) and airborne (ALS) LiDAR data for integration into the PALM Large Eddy Simulation (LES) microclimate model. The research was conducted in a managed forest plot in Britz, Brandenburg, Germany, focusing on Sessile Oak (Quercus petraea) stands.

The study compares two methodologies for processing LiDAR point cloud data: our newly developed VoxPy, which implements an efficient TLS proportional leaf area density scaling method, and the established AMAPVox model, which utilizes Free Path Length estimation derived from Beer-Lambert Law. The point clouds underwent preprocessing through noise filtering, crown segmentation, and machine learning-based classification of woody mass and foliage using K-nearest neighbor algorithms.

Initial validation of VoxPy against ground-based leaf area index measurements and the VoxLAD Beer-Lambert model shows strong agreement, demonstrating the method's reliability for LAD estimation. Preliminary results from AMAPVox processing indicate

promising alignment with ground truth data, with full validation ongoing. Statistical analysis reveals platform-specific characteristics, with ALS-derived profiles showing higher sensitivity in upper crown regions and TLS-derived profiles demonstrating stronger accuracy in lower canopy layers.

The first PALM LES microclimate simulation using these LAD profiles during a heatwave period shows encouraging agreement with meteorological tower observations for air temperature through a single diurnal cycle. The simulation employs ICON-D2 mesoscale forcing, with additional three-day simulations planned.

This research establishes a framework for generating high-fidelity canopy structure data essential for microclimate modeling. The preliminary findings underscore the potential impact of precise LAD profiles in simulating forest-atmosphere interactions, with implications for climate-sensitive planning. Future work will focus on comprehensive validation across extended simulation periods.

How to cite: Brook-Lawson, J., Schubert, S., Jackisch, R., Meier, F., Kershaw, T., Sanders, T., Natkhin, M., and Stöckigt, B.: Validating ALS/TLS LiDAR derived leaf area density profiles against multisource ground truth data and PALM LES microclimate model simulations, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-473, https://doi.org/10.5194/icuc12-473, 2025.

As extreme weather intensifies, the increasing frequency of summer heatwaves make the study of human thermal exposure essential. Mean radiant temperature (T_mrt) is crucial for assessing human thermal exposure, because it quantifies the largest sources of spatial variability in pedestrian-perceived thermal stress and comfort in complicated urban environments. Despite the availability of existing methods for evaluating T_mrt through measurements and numerical simulations, the lack of detailed urban three-dimensional (3D) models and spatially and temporally resolved pedestrian-level irradiance from urban surfaces poses a significant challenge in obtaining high-resolution T_mrt data. This paper introduces a methodology that combines LiDAR and thermal infrared scanning with data-driven simulations. The approach was applied to a street canyon segment in Salt Lake City during the summer, to enable assessment of high spatial resolution (0.3 m²) shortwave and longwave radiant fluxes of urban surfaces at different periods. Based on the refined 3D radiation field, different irradiance sources received by the human body at different locations was sampled and then a high-spatial-resolution field (0.5 m²) of pedestrian-level sampled T_mrt (T_{mrt_sampled}) was generated. Such a method for calculating T_{mrt_sampled} is efficient, requiring only 30 seconds of computational time for each simulated instant. Results indicated significant variations of  across heterogeneous urban spaces, with the largest difference exceeding 35 ℃. Spatiotemporal variations in longwave irradiance from urban surfaces significantly influenced T_{mrt_sampled}. Exposure of ground and wall materials to direct sunlight, coupled with their substantial thermal  inertia, drove peak human thermal stress by 17:00. Furthermore, T_{mrt_sampled} was compared with SOLWEIG-simulated  (T_{mrt_simulated} ) for the same meteorological conditions. Due to differences in mesh and mechanism for quantifying T_mrt,  T_{mrt_sampled} values typically were 4 ~ 6  ℃  higher than T_{mrt_simulated} over sunlit surfaces, and their root mean square error reached 4.71  ℃ when the solar elevation was high and ground shadows were minimal.

How to cite: zhong, X., voogt, J., and bailey, B.: Evaluating high-resolution mean radiant temperature within an urban street canopy: Resolving spatiotemporal variations with LiDAR/thermal infrared scanning and data-driven simulation, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-557, https://doi.org/10.5194/icuc12-557, 2025.

Precisely estimating high-resolution land surface temperature (LST) is crucial for understanding urban heat dynamics and developing climate adaptation strategies. This study applies machine learning to enhance LST resolution from 30 m (Landsat 8/9) to 3 m by integrating PlanetScope multispectral imagery and elevation data. To ensure model robustness and interpretability, we assess the relative importance of predictors before performing Regression. We utilize Random Forest Regression to combine high-resolution predictors and refine temperature estimations, capturing complex urban heat patterns more accurately. LST is derived from Landsat thermal data, while high-resolution multispectral and topographic features serve as independent variables. After applying a stratified random sampling method, we train and evaluate the model using independent test data. The model achieves R² = 0.603 and MAE = 1.03°C, demonstrating its effectiveness in improving spatial LST resolution. Our findings highlight the potential of machine learning-based downscaling for urban climate research. By generating a 3m-resolution LST map, we provide key insights into urban heat islands, temperature variations, and land-atmosphere interactions. This approach empowers policymakers and researchers to develop data-driven heat mitigation strategies and climate-resilient urban designs. Ultimately, our research shows that combining high-resolution optical and thermal remote sensing with machine learning significantly enhances urban climate analysis, particularly in data-scarce environments.

How to cite: Moukomla, S.: Enhancing Land Surface Temperature Downscaling with Machine Learning: A Case Study in Bangkok’s Heterogeneous Urban Environment, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-980, https://doi.org/10.5194/icuc12-980, 2025.

Being able to identify urban surface materials using Earth Observation data has the potential to support urban weather and climate modelling, urban planning and to help assess climate resilience strategies. However, the varied composition of these materials, the three-dimensionality of the urban canopy and the spatial scales cause high likelihood of mixed pixels, each pose numerous challenges. Existing spectral libraries do not cover the diversity of urban materials needed for image-based surface cover classification. Here, we develop an urban hyperspectral library in the frame of the European Research Council project urbisphere with high-resolution spectral data collected from a wide range of urban materials across Europe.

The urbisphere spectral library v1.0 contains more than 10,000 in-situ hyperspectral measurements from various natural and artificial materials collected from several European cities, such as Heraklion, Paris, and Berlin, and is set to be expanded in the coming years. These measurements were captured under varying conditions of shading, weathering, and viewing angles, using the HySpex Mjolnir VS-620 hyperspectral camera harmonized with the RS-3500 spectroradiometer.

In an exploratory study, hyperspectral signatures from the urbisphere library were adjusted to align with the multispectral bands of the WorldView-3 and Sentinel-2 satellites, as well as the hyperspectral bands of PRISMA and EnMap. These adjusted signatures were used to train separate X-SVM classifiers for each satellite. The trained models were then applied to classify the respective satellite imagery acquired over Heraklion, Greece. The results highlight the library's capability to detect various natural and artificial materials in urban environments and reveal the limitations associated with differing spatial and spectral resolutions. This methodology demonstrates precise identification of urban surface materials while reducing reliance on labour-intensive, image-based end-member extraction.

How to cite: Lantzanakis, G., Chrysoulakis, N., Christen, A., Grimmond, S., and Birkmann, J.: The urbisphere Spectral Library v1.0: Enabling Urban Material Identification Across Cities Using Multi-Sensor Satellite Imaging, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-436, https://doi.org/10.5194/icuc12-436, 2025.

Urban areas, with their abundance of materials that absorb solar radiation and high population density, are particularly vulnerable to the heat island effect. Mitigating this phenomenon requires accurate estimates of surface albedo and emissivity, yet such measurements are often unavailable at a citywide scale. High-resolution optical and thermal-infrared satellite sensors offer a solution, overcoming the spatial and temporal limitations of in-situ observations. However, satellite measurements are constrained by limited acquisition geometries and spectral bands, which prevents the accurate computation of neded quantities such as urban albedo maps.

This contribution describes the SuaBe project (https://stereoiv-suabe.eu/en), which focuses on designing and implementing a fast, and robust algorithm, that uses the 3D radiative transfer code DART (https://dart.omp.eu/#/) to invert remote sensing images of cities as a 3D distribution of optical properties and temperature, using a geometric urban database. This approach enables us to obtain a 3D model of a city to simulate urban surface albedo, emissivity, and net radiation maps at any date as long as the optical properties of urban elements remain constant. These optical properties can be up-dated with the inversion of recently acquired satellite imagery.

As a case study, we will apply it to Brussels to retrieve  surface albedo and emissivity maps at a neighbourhood scale. These results can be considered an asset to be used by urban planners and decision-makers to identify what urban areas should be considered priority candidates for an intervention to mitigate heat pollution, which in turn, shall allow authorities or civil organisations to maximise benefits from limited financial resources. Since the SuaBe’s methodology is based on a robust and rigorous physical model, it can be seamlessly implemented in any other city worldwide, provided that a geometric urban database is available.

How to cite: Leon-Tavares, J., Zhen, Z., Lauret, N., and Gastellu-Etchegorry, J. P.: Surface albedo and emissivity for Belgian cities (SuaBe), 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-81, https://doi.org/10.5194/icuc12-81, 2025.

With more than 50% of the global population living in cities and the continued urbanization trends, urban areas represent critical hotspots of water, energy, and health challenges facing humanity in the 21st century. A better understanding and prediction of urban microclimate and hydrology within the context of global environmental change plays a key role in tackling these challenges. Correctly characterizing the albedo of building materials is identified as the most important factor in improving urban simulation results. Most urban land surface models used in weather and climate models (e.g., the single-layer urban canopy model in the WRF model) still employ tabulated albedo values, which have limited spatial variability. We used remotely sensed albedo data to improve urban albedo characterization in weather models. We developed a new high-resolution urban albedo dataset based on Landsat and Sentinel-2 data. The new dataset separates the roof from the impervious ground in the NLCD impervious surface dataset using global building footprint data. We then estimated rood and ground albedos at various spatial scales ranging from 1-10 km. The new dataset will improve the characterization of the albedo parameters in the WRF model, improve the simulation of urban meteorological variables at the weather scale, and thus empower stakeholders and researchers to better navigate urban planning and policies in a changing climate.

How to cite: Tang, X., Li, D., Erb, A., and He, C.: Improving Urban Climate Simulation by Integrating Remotely Sensed Albedo into the WRF Model, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-315, https://doi.org/10.5194/icuc12-315, 2025.

The Local Climate Zone (LCZ) framework standardizes a common descriptive methodology to classify land surfaces into ten built and seven natural land cover types, each associated with some prescribed ranges of values for a subset of parameters, providing a more detailed alternative to supply spatially explicit urban parameters to regional and global models than conventional binary or density-class based urban typologies. While recent high-resolution LCZ maps have greatly advanced our understanding of urban areas at large scales in a “universal” way, challenges remain in determining accurate urban canopy parameters (UCPs) from these classifications. The common look-up-table approach, which assigns predefined value ranges to each LCZ type regardless of geographic location, oversimplifies the complexity and heterogeneity of urban surfaces within and across nations with different construction policies and building codes. Furthermore, LCZs, by their very nature, describe primarily urban morphologies, which can be frequently decoupled from radiative properties and construction materials, making the model inputs internally inconsistent. This study explores these limitations using the newly developed global 1km spatially continuous urban surface property dataset (U-Surf). U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for urban canopy models at the facet- and canopy-level. Our analysis reveals substantial variabilities and uncertainties in LCZ-derived UCPs across regions and raises questions about the wide adoption of coarse-grained urban representation in urban climate modeling. Through simulations using the Community Earth System Model (CESM), we further discuss the implications of these discrepancies for detecting urban-specific meteorological signals from local to regional scales. Our results highlight the importance of spatially continuous, internally consistent UCPs in high-resolution urban climate modeling.

How to cite: Cheng, Y., Zhao, L., and Chakraborty, T. ".: Large uncertainties of LCZ-based urban canopy parameters in urban climate modeling, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-455, https://doi.org/10.5194/icuc12-455, 2025.

Remotely sensed spectral and thermal measurements are valuable in vegetation related research with a spectrum of applications from ecology to urban landscape studies. Their success depends on our understanding of the complex dynamics between plants and their surroundings as well as the plant’s spectral properties - both are scale dependent challenges. In cases when remote sensing applications are deployed in conditions with sparse vegetation, e.g. trees in urban areas where multiple components within a pixel need to be considered, the contained spectral information can be difficult to interpret.

In this study we present a ground-based experimental layout consisting of a spectrometer and a thermal camera mounted on a portable crane. The experimental layout was deployed in two applications:

(i) thermal images were used to characterize the thermal status of different parts of a dense circular cluster of containerized trees and their spectral reflectance was measured. A statistically significant difference of both VSWIR reflectance and absorption features related to the chlorophyll, carotenoid, and water absorption bands was found between the warmer and cooler parts of the canopy.

(ii) the contribution of the thermal signatures of the tree canopy and the underlying urban surface to the spectral reflectance variation, was studied with two groups of five identically arranged containerised trees placed into two adjacent built and non-built local microenvironments. It was found that strong correlations between the canopy-background temperatures and the spectral reflectance indicate that synergies between thermal and spectral measurements in the fine scale is a promising method for disentangling the combined signal components.

With the resolution of data products from air- and space-borne instruments increasingly improving, results of this study indicate the potential of leveraging the synergy between thermal and spectral measurements for the purpose of more accurately assessing the complex biochemical and biophysical characteristics of measured urban vegetation canopies.

How to cite: Halios, C. H., Smith, S. T., Pickles, B. J., Shao, L., Haghparast, Y., and Mortimer, H.: Studying the interactions between the spectral composition and thermal properties for urban trees, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-753, https://doi.org/10.5194/icuc12-753, 2025.

Understanding accurate urban land cover is essential for thermal environment research. Previous studies, however, have often overlooked the distinctive thermal features among urban built-up areas when conducting land cover classification.

To address this gap, this study proposes and evaluates a land cover classification scheme based on the thermal characteristic, thermal inertia. The study employs a multi-step methodology to quantify thermal properties, compare classification methods, and analyze challenges in the proposed approach.

(1) Land surface temperature and thermal inertia were quantified using ECOSTRESS (Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station) thermal infrared (TIR) data and Pleiades visible near-infrared (VNIR) data acquired during the summer and autumn of 2022 in Beijing;

(2) Conventional classification was conducted using VNIR data alone, while the proposed classification method incorporated both VNIR and TIR data. Their performance was evaluated using thermal inertia, surface albedo metrics, and confusion matrices;

(3) The challenges of the proposed classification were examined through statistical analysis by comparing the thermal features of pixels classified as wooden structures with those identified through visual interpretation;

(4) The underlying causes of classification challenges were explored and evaluated through thermal environment simulations using microscale urban canyon models.

The results indicate that while classifying wooden structures in urban built-up areas remains challenging, the proposed method was particularly effective in clustered wooden structure areas, where distinct thermal feature differences between wooden structures and other land covers were more pronounced. However, shaded wooden structures with high thermal inertia and low albedo were often misclassified as other impervious areas.

This study underscores the necessity of distinguishing land covers with unique thermal features in urban built-up areas and enhances the understanding of classification methods that integrate TIR and VNIR data.

How to cite: Xu, W. and Murakami, A.: Incorporating the ECOSTRESS diurnal thermal infrared data in urban land cover classification, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-756, https://doi.org/10.5194/icuc12-756, 2025.

The complexity of urban environments requires advanced methods to assess human outdoor thermal comfort (OTC). Remote sensing of Land Surface Temperature (LST) has been extensively used to map urban thermal patterns, yet its applicability in assessing OTC remains controversial due to inherent limitations. This study aims to bridge the gap between LST observations and high-resolution OTC assessments using the Universal Thermal Climate Index (UTCI).

We compare Landsat LST data with high-resolution UTCI maps from the deep learning-based OTC model HTC-NN (Briegel et al., 2024) to evaluate LST as a proxy for OTC across various urban and rural areas. Cluster analysis examines spatial variations in the LST-UTCI relationship, while random forest modeling assesses the predictive ability of LST, meteorological data (ERA5 Land), and spatial variables for UTCI values and heat stress classes, using the ERA5 HEAT dataset (Di Napoli et al., 2020) as a baseline.

Our findings show a linear relationship between LST and UTCI under non-heat stress conditions, which becomes non-linear during heat stress events. Cluster analysis identifies distinct spatial patterns in the LST-UTCI relationship, influenced by land cover and urban form. In densely built-up areas, LST and UTCI show less agreement, with an average difference of ~10K, compared to -0.8K in vegetated areas, which highlights the limitations of LST in capturing pedestrian-level thermal stress in dense urban environments. Random forest models using LST alone show low predictive power for UTCI and heat stress classes, but performance improves when combined with ERA5 Land data. Models incorporating both achieve 82% accuracy for UTCI stress classes, surpassing the 77% accuracy of the ERA5 HEAT dataset. In urban clusters, the random forest model demonstrates significantly lower error (RMSE 2.6K) compared to ERA5 HEAT (RMSE 4.8K). Overall, this study underscores the potential limitations of LST as a standalone metric for OTC evaluation. 

How to cite: Briegel, F., G. Pinto, J., and Christen, A.: Land Surface Temperature as a Proxy for Outdoor Thermal Comfort?, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-680, https://doi.org/10.5194/icuc12-680, 2025.

The urbanization process causes various socio-environmental impacts, as it incorporates anthropogenic elements with low albedo, affecting the local microclimate and contributing to the formation of Urban Heat Islands (UHIs). The use of thermal data obtained from sensors onboard Earth Observation satellites is a methodology for studying UHI, with the most common satellites being Landsat (since Landsat 4, with Landsat-8 and 9 as the current operational models, equipped with Thermal Infrared Sensors (TIRS and TIRS-2), respectively); Aqua (Moderate Resolution Imaging Spectroradiometer (MODIS) sensor); and Terra (MODIS and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors). Each sensor has its spatial, spectral, radiometric and temporal resolutions, which must be considered to ensure more accurate results. The objective of this study was to analyze the TIRS, TIRS-2, MODIS, and ASTER sensors in terms of their resolutions, limitations, challenges, and opportunities in Bragança (Portugal). We divided the data by seasonality and used Google Earth Engine to calculate the Land Surface Temperature (LST) for each image, comparing the results with in situ Air Temperature (Ta) data. As a result, all LSTs showed a strong/very strong correlation with Ta. Although the MODIS sensor has the higher temporal resolution, its low spatial resolution (1 km per pixel) was a limitation, considering the heterogeneity in Land Use and Land Cover (LULC) and Bragança´s area. ASTER provided a more detailed resolution (90m), but the limited number of images available for Bragança was challenging. TIR and TIR-2 provided better spatial resolution compared to MODIS (the data is collected at 100m and resampled to 30m), but it was also unable to detail LULC, especially in transition areas. For more robust studies, we suggest combining data from multiple sensors, taking advantage of the complementary benefits of their different resolutions and, eventually, incorporating higher-precision data, such as Unmanned Aerial Vehicles (UAVs).

How to cite: Rodrigues De Almeida, C., Goncalves, A., and Teodoro, A. C.: Challenges in the Use of Satellite-Derived Thermal Data in Urban Heat Island (UHI) Studies: A Case Study of Braganca, Portugal, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-177, https://doi.org/10.5194/icuc12-177, 2025.

The increasing availability of high-quality, free-of-charge satellite data—such as the Sentinels from the European Copernicus program—combined with advances in automated data processing, image analysis, and cloud computing, is transforming the monitoring of urban environments worldwide. The integration of Earth Observation (EO) data from diverse satellite sensors (radar, optical, SAR/InSAR, thermal, hyperspectral, LiDAR), along with ancillary sources like ground-based sensors, drones, and citizen science, is continuously unlocking new possibilities for urban analytics.

Urban remote sensing is therefore progressively evolving from traditional urban extent and land cover mapping into advanced urban applications, providing critical insights into urban resilience by covering aspects such as impervious surface expansion, green and blue infrastructure, air quality, and urban heat islands. This presentation will showcase key urban applications developed through past and ongoing ESA projects and initiatives, addressing topics including urban climate, air pollution, human settlements, and natural hazard monitoring.

As municipalities and urban practitioners increasingly seek EO-based solutions for decision-making, the discussion will highlight how these applications are bridging the gap between technical experts and end-users. Through collaborative initiatives between cities, industry and academia, there is indeed an increasing focus in not only developing innovative methods for urban environments monitoring, but also on transforming satellite data into actionable information for sustainable urban development.

How to cite: Leonelli, F. E.: The role of Earth Observation in enhancing urban resilience: insights from ESA’s projects , 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-882, https://doi.org/10.5194/icuc12-882, 2025.

Accurate air temperature (T_a) measurements are essential for analyzing phenomena such as the urban heat island (UHI) effect, which can lead to critical conditions in cities during summer. However, in-situ sensors offer limited spatial coverage due to their uneven distribution. In contrast, satellite-derived land surface temperature (LST) provides detailed spatial information but does not directly correspond to T_a. This study introduces a machine learning approach to derive T_a from LST using data from geostationary satellites, with Rome, Italy, as a case study. A gradient boosting algorithm, trained on T_a observations from 15 weather stations, was applied. The model incorporated variables such as instantaneous and lagged LST (1–4 hours) alongside other factors to estimate T_a in areas lacking direct measurements. The predicted T_a achieved an average error of 0.9°C, with a spatial resolution of 3 km and an hourly temporal resolution. This dataset allowed for a more detailed investigation of UHI intensity and dynamics during summer, significantly improving both spatial and temporal resolution compared to previous studies based solely on in-situ observations. The findings also indicate a slightly higher nocturnal UHI intensity than previously reported, likely due to the inclusion of rural areas with minimal impervious surfaces, made possible by the comprehensive T_a mapping now available across the study domain.

How to cite: Cecilia, A., Casasanta, G., Petenko, I., and Argentini, S.: A Machine earning Algorithm to Convert Geostationary Satellite LST to Air Temperature Using In Situ Measurements: A Case Study in Rome and High-Resolution Spatio-Temporal UHI Analysis, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-777, https://doi.org/10.5194/icuc12-777, 2025.

The urban environment possesses special characteristics in terms of the energy and radiation budget. Due to direct impacts on human comfort, it is especially important during extreme heat events in summer, which have become and are projected to be longer lasting, more frequent, and more intense in extratropical regions. The 22-year-long time series of continuous MODIS measurements of Terra and Aqua satellites are used to study the surface temperature (ST) as well as the consequent surface urban heat island (SUHI) pattern. In addition, humidity is also involved due to its connection to the energy budget. Here, the urban environment of Budapest (a Central/Eastern European city with 1.7 million inhabitants) is analysed. A significant warming trend can be identified in ST. However, the summer SUHI intensity decreases as the surrounding rural area becomes warmer. When less water is available in the rural area in a drought event, the lack of latent heat facilitates the warming of ST in the rural area similarly to the urban area. Consequently, the SUHI is very weak during hot and dry summers. Such conditions are analysed for selected years (2003, 2007, and 2022), when the compound events of heat and drought occurred. Such detailed analysis aiming to understand the complex processes in the urban environment is essential in order to develop effective adaptation strategies to the upcoming challenges of climate change with possible adverse effects to the urban population’s comfort. As a specific example, the analysis of in-situ local measurements is also added to the study in order to evaluate children’s playground areas during such extreme heat conditions.

This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary's National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union.

How to cite: Pongracz, R., Dezso, Z., and Soos, V.: Analysing the surface temperature of the urban environment during extreme summer heat, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-727, https://doi.org/10.5194/icuc12-727, 2025.

Urban areas often experience higher temperatures than the surrounding rural regions, particularly at night, due to factors such as heat accumulation in buildings and pavements, reduced green spaces, and intensified human activities. This phenomenon, exacerbated by global warming, highlights the need for daily monitoring of urban temperatures, especially when the effects of Urban Heat Islands (UHIs) reach their peak intensity. The UHI effect currently impacts over 4.4 billion people and is projected to affect 9.5 billion (75% of the global population) by 2050.

To address this need, it is essential to design and launch high-resolution thermal infrared Earth Observation space missions that enable daily monitoring of urban temperatures.

This study analyzes the characteristics that a spaceborne sensor must satisfy in terms of spatial resolution, thermal band configuration, frequency, and overpass time to properly monitor the Surface Urban Heat Island (SUHI) effect at the district level in a city. Additionally, it includes suggestions on the products that these missions should provide to the scientific community and decision-makers.

In this context, we present the SIRIUS mission (Space-based InfraRed Imager for Urban Sustainability). The SIRIUS mission combines a fast development time, innovative instrument design, and a flight-proven platform, all developed with a new space approach, and provides two unique characteristics:

• Satellite pas over targets (cities) adapted to retrieve temperature when the UHI effect is most relevant during night.
• Daily revisit of targets at high spatial resolution in urban areas to maximize the opportunities to follow closely the temporal evolution of the heath waves.

As global urban populations continue to grow, it is essential to monitor these environments. Urban heat management, climate resilience, public health, and informed decision-making, particularly as extreme heat and UHI effects intensify, urgently require access to high-resolution thermal data from space.

How to cite: Sobrino, J. A., Daganzo, A. I., Vidal, M. A., Callejo, E., Navarro, R., Wei, L., and Skokovic, D.: Monitoring Urban Heat Islands: The Need for High-Resolution Thermal Satellite Missions, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-593, https://doi.org/10.5194/icuc12-593, 2025.

Urban heat is a significant challenge, arising from the combined effect of global climate change and urban development. Many urban heat studies have been conducted on a city-by-city basis, with a primary focus on summer days. These studies often overlook the broader impact of background climate, seasonality, and diurnal cycle. To address these gaps, we explore Land Surface Temperature (LST) growth in over 1400 cities around the world from 2002 to 2021 using Aqua MODIS satellite. Results show that cities are generally getting warmer across the globe but at different rates. The highest rate of temperature change over the last two decades was found in cold climate cities, with a more rapid increase during winter. These cities are predominantly located in Eastern Europe, extending into parts of Western Asia. However, the lowest rate of change during the day was mostly seen in cities in India and northeastern China. We also quantify the annual rate of change in population exposure to extreme heat across cities, distinguishing the contributions of urban population growth and climate change to exposure trends. These findings provide new insights into identifying regions most vulnerable to global climate change and urban warming, as well as the key factors contributing to these vulnerabilities across the world.

How to cite: Naserikia, M., Nazarian, N., Hart, M., Sismanidis, P., and Bechtel, B.: Surface urban heat growth and population exposure across global cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-242, https://doi.org/10.5194/icuc12-242, 2025.

Extensive research has established that urban land cover plays a critical role in modulating land surface temperature (LST), with urban morphology and surface heterogeneity driving its spatial and temporal variations. Studies have also highlighted that the increasing frequency, intensity, and duration of extreme heat events exacerbate urban thermal stress, significantly impacting livability and thermal comfort. This study utilizes high-resolution (70 m) LST data from NASA’s Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to investigate the diurnal cycling of LST and its variations across local climate zones (LCZs) in two major South Asian cities, Delhi and Lahore. ECOSTRESS LST images from four distinct heatwave events (2020–2023) were analyzed to quantify intra-LCZ variations and surface urban heat island (SUHI) intensity at different times of the day and night. Results indicate significant diurnal thermal disparities among LCZs, with the highest average SUHI observed in Lahore (3.48°C), while Delhi also exhibited a positive SUHI effect (0.46°C). Findings revealed distinct spatial LST patterns across LCZs, highlighting the role of urban vegetation and impervious surfaces in regulating SUHI intensity. Additionally, urban heat hotspots and coldspots were identified, largely influenced by the characteristics of the underlying LCZ types. The study provides critical insights into the spatiotemporal dynamics of SUHI during extreme heat events, offering a valuable reference for urban planners and policymakers in designing climate-resilient cities and mitigating urban heat stress.

How to cite: Pandey, A. and Banerjee, T.: Exploring the impact of urban features on land surface temperature over South Asian cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-624, https://doi.org/10.5194/icuc12-624, 2025.

This study examines the Surface Urban Heat Island (SUHI) effect, characterized by elevated land surface temperatures in urban areas compared to their surrounding rural environments. SUHI is a key indicator of anthropogenic environmental changes, with significant implications for urban climate and sustainability. A comparative analysis on several Italian metropolitan cities is conducted using high-resolution satellite data, notably from ECOSTRESS, to capture the diurnal and seasonal thermal dynamics of SUHI. ECOSTRESS provides a high horizontal spatial resolution of 70m along with continuous thermal cycle observations, enabling a detailed assessment of SUHI intensity and variability. Remote sensing data are integrated with the Corine Land Cover (CLC) classifications and detailed urban geometry to assess SUHI intensity gradients across different levels of urbanisation.

Through a long-term analysis, this study evaluates the feasibility of using ECOSTRESS data to assess SUHI dynamics across different temporal scales. The extensive datasets make it possible to study the impacts of heat waves on various urban fabrics, identifying the areas which are most vulnerable to excessive overheating.

This study provides valuable insights into urban thermal dynamics by enhancing the understanding of SUHI mechanisms and their spatiotemporal variability. It also offers a scientific foundation for urban planning and policymaking by identifying key areas for targeted mitigation, such as the implementation of green infrastructure and climate-responsive urban design, tailored to improve urban resilience to climate change.

This work is supported by: Progetto “RETE - Resilience of the Electric Transmission grid to Extreme events” (PNRR innovation grants) (CUP F83C22000740001);

The first author acknowledges the PON “Ricerca e Innovazione 2014–2020—Asse IV”—PhD course in “Scienze e Tecnologie Biologiche ed Ambientali”–XXXVII cycle-University of Salento.

How to cite: Esposito, A., Pappaccogli, G., Lionello, P., and Buccolieri, R.: Assessing SUHI dynamics in Italian Cities using ECOSTRESS data, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-730, https://doi.org/10.5194/icuc12-730, 2025.

Thermal comfort of urban residents under changing climate scenarios, varying near-surface aerosol loading and modified urban infrastructure is a raising concern. This is especially true for the cities in South Asia where urbanization induced change in land use, change in urban microclimate and elevated aerosol loading intensifies the frequency, intensity and impact of climate extremes. This study investigates the thermal behavior of South Asian cities, focusing on satellite retrieved geospatial databases including land surface temperature (LST), surface albedo, urban built-up, urban greenery, columnar aerosol loading and co-emitting short-lived trace gases. Spatial patterns of urban microclimate and landscape variables were explored in between heatwave periods during 2020 to 2024 to assess its attribution on the urban thermal environment. Surface urban heat island intensity was spatially correlated with multiple climate and landscape variables over two distinct Indian cities. Using NASA's ECOSTRESS and MODIS-based aerosol optical depth (AOD) data, the study analyzes seasonal and diurnal variations in LST and AOD across urban and rural areas. Urban and non-urban areas were delineated using the city clustering algorithm applied to MERIS Land Cover datasets. Significant diurnal thermal disparities are observed between urban cores and rural areas, with Lahore exhibiting the highest average SUHI (3.48°C). The nexus between urban aerosol loading and aerosol optical property with localized urban heating was explored. Notable aerosol loading disparities were identified, particularly in Lahore (range: 0.53 to 0.73). The findings highlight the critical influence of urban features and urban pollution on urban heat island dynamics across South Asian cities.

How to cite: Banerjee, T.: Quantifying urban heat island and pollutant nexus over South Asian cities, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-789, https://doi.org/10.5194/icuc12-789, 2025.

Urban heat waves pose increasing challenges to cities worldwide, with Brussels experiencing significant thermal stress events over the past two decades. This study investigates the relationship between heat wave occurrence, vegetation cover, and built-up density in the Brussels Capital Region using remote sensing data from 2004 to 2024. Land surface temperature (LST) data derived from Landsat 8 satellite imagery were analyzed alongside normalized difference vegetation index (NDVI) and built-up indices to understand their spatial and temporal correlations during heat wave events.

The analysis revealed that areas with vegetation cover below 30% consistently experienced elevated temperatures, with surface temperatures during heat wave events reaching up to 2.5°C higher compared to well-vegetated neighborhoods. In regions where built-up density exceeded 75%, LST values were on average 2.0-3.2°C higher than in areas with moderate urbanization (40-60% built-up cover). Temporal analysis indicated a 20% increase in the frequency of heat wave events and a 15% rise in their average duration from 2004 to 2024, with the most significant impacts observed in high-density urban districts. Furthermore, neighborhoods with vegetation loss exceeding 10% over the 20-year period saw an additional 0.8-1.2°C rise in surface temperatures during heat waves, highlighting the critical role of green spaces in moderating UHI intensities. These findings underscore the disproportionate thermal stress faced by densely urbanized areas, emphasizing the necessity for targeted urban greening interventions.

How to cite: Matallah, M. E., Wang, T., Wyard, C., and Attia, S.: Urban heat wave patterns in Brussels (2004-2024): exploring the interplay between vegetation cover and built environment through remote sensing approach , 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-830, https://doi.org/10.5194/icuc12-830, 2025.

As global warming intensifies, urban areas are experiencing rising temperatures, with notable effects on the residents' health and well-being. Satellite-derived Land Surface Temperatures (LST) provide an independent measure of surface temperature change, with long time series of LST becoming indispensable for studying the impacts of climate warming at local, regional, and global level. Recent studies have shown a consistent rise in LST across cities; however, it remains unclear how this change compares to that of the surrounding rural areas. In this work, we aim to determine whether the LST of cities is warming faster, slower, or at the same rate as the surrounding rural areas. To answer this question, we analyze 19 years (2002–2021) of daily Aqua MODIS data from the European Space Agency’s Climate Change Initiative project on LST (LST_cci). Our focus is on nighttime conditions, as the relationship between LST and near-surface air temperature over cities is strongest during this time-of-day. We analyze our results as a function of climate, considering the Local Climate Zone (LCZ) makeup of each city, as well as changes in city size, cloud cover, Normalized Difference Vegetation Index (NDVI), and air temperature (obtained from reanalysis data) during the same period. Our findings suggest that the LST of rural areas is warming slightly faster than that of urban areas, especially in tropical and dry climates.

How to cite: Sismanidis, P., Bechtel, B., Naserikia, M., and Nazarian, N.: Are Urban Surface Temperatures Warming Faster Than Rural?, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-924, https://doi.org/10.5194/icuc12-924, 2025.

Land surface temperature (LST) satellite products offer extensive spatial and temporal coverage, making it possible to map temperature contrasts over different territories and evolution according to weather conditions. This data is frequently used for monitoring the surface urban heat island (SUHI) of cities. But SUHI maps can raise issues of misinterpretation when trying to make the link with air temperature and related canopy-level urban heat island (UHI), and impacts on thermal comfort and heat exposure.

The aim here is to explore the potential contribution of LST satellite data for monitoring and studying regional to local-scale urban climate in the Paris region during summer 2022. Especially, we investigate the value of the EUMETSAT LSASAF product derived from the SEVIRI radiometer aboard the MSG geostationary satellite, whose 15-minute frequency makes it particularly interesting for fine temporal monitoring, despite its limited horizontal resolution of 3-5 km (which should be refined with the new MTG-I mission).

We first compare the SEVIRI LST data with those provided by the AVHRR and MODIS radiometers aboard polar-orbiting satellites, with finer 1-km resolution but only one day-time and one night-time passes. We question the relevance of pass times and investigate how satellite LST and ground-station air temperature compare depending on time of the day, types of environment, or weather conditions. This study highlights the relevance of SEVIRI for monitoring LST diurnal regimes and patterns on a regional/local scale, with promising future applications. We note that the agreement between nighttime SUHI and UHI depends on the meteorological conditions of turbulence, which govern surface-atmosphere exchanges. For very steady conditions with little turbulence, SUHIs and UHIs are particularly comparable and of greater intensity. In more mixed conditions, the urban-rural differences are less marked in LST but a good correspondence between air temperature and LST is observed in the city center.

How to cite: Lemonsu, A., Delmotte, A., Ceamanos, X., Havu, M., Masson, V., and Nagel, T.: Assessment of LSASAF SEVIRI land surface temperature for monitoring regional-local conditions and urban climate in the Paris region, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-389, https://doi.org/10.5194/icuc12-389, 2025.

The HERITAGE program develops a sensing and design system aiming at detection, reduction and prevention (by monitoring and design) of urban heat-stress occurring due to ageing built environment, through socio-technical solutions. The system will detect and forecast spatiotemporal patterns of heat stress at unprecedented resolutions (1m scale), aiming at technological solutions to reduce indoor and outdoor heat stress through developing urban design guidelines and connecting the energy transition, housing demands, repurposing areas, climate adaptation and digitalization.

This necessitates a multi-disciplinary approach involving earth observation, urban hydro-meteorology and climatology, urban design and sustainable infrastructural energy systems. Therefore, parallel to the sensing, research lines are rolled out on robust hydro-meteorological, design and energy solutions, at multiple spatiotemporal scales and forms. Concretely, these research lines fill knowledge gaps through innovative techniques for analysis, simulation, development and experimental testing of newly designed multiscale urban heritage canopy layer schemes for climate models, multiscale form-microclimatic relationships and sustainable energy systems, suited for application in aged neighborhoods and buildings.

Reflected and emitted solar and thermal radiation can be considered the main drivers for turbulent and radiative heat exchange and thus for urban heat. However, their use from remote sensing observations in urban areas is still in its infancy and rather simplistic in its modelling approach. The above-mentioned multiscale schemes and relationships will be mainly developed in the city of Enschede (and tested in the cities of Amsterdam, Rotterdam, Eindhoven and Delft) for which we collect ground-based, air- and space-borne radiative observations and heat-exchanges at matching scales. We cover space-time resolutions from submeter to kilometer and from 100 Hz to hours, monitoring the exchange processes at the relevant scales. The observations will be employed to develop scale-dependent heat-exchange parameterizations, suitable for 3D city models at building-, street-, and neighborhood-level. Here, first observation and modelling results are presented.

How to cite: Timmermans, W., Gadde, S., Joshi, H., Oude Elberink, S., Büyükdemircioğlu, M., Steeneveld, G.-J., Milošević, D., Sandvik, B., Uijlenhoet, R., Droste, A., Chen, X., van Esch, M., Maiullari, D., Walder, C., Reinders, A., Loonen, R., Hu, G., and Otoo, E.: HEat Robustness In relation To AGEing cities (HERITAGE) Program: First results, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-401, https://doi.org/10.5194/icuc12-401, 2025.

The ERC urbisphere project aims to forecast dynamic feedbacks between weather/climate and cities. The urbisphere - Berlin field campaign (Autumn 2021 to Autumn 2022) provides new information on urban- and regional-scale boundary layer interactions across a wide range of scales. During an intensive observation period (IOP) surface temperatures were observed with of spatial resolutions from <1 m to 1 km, coincident in location and time on many occasions. The August 2022 IOP, included multiple sensors: five ground-based thermal infrared (TIR) cameras (Optris 640 Pi and Optris400 Pi), aircraft mounted SatelliteVu MIR (Mid-Infrared) sensor and drone mounted Anafi Parrot Thermal. Satellite observations during this period include Sentinel-3 SLSTR, MODIS, ASTER, ECOSTRESS and Landsat. As the sensors differ in field of view, wavelength, and accuracies, here we harmonise the surface temperature from different TIR and MIR data, and evaluate its spatial and temporal variability during the IOP.

Acknowledgement

This work is part of the urbisphere project (www.urbisphere.eu), a synergy project funded by the European Research Council (ERC-SyG) within the European Union’s Horizon 2020 research and innovation program under grant agreement no. 855005. Special thanks to the Chair of Climatology at Technische Universität Berlin for providing equipment, ensuring access to observation sites and to all those who contributed to the field work: Fred Meier, Kai König, Josefine Brückmann, Martina Frid, Beth Saunders.

How to cite: Mitraka, Z., Lantzanakis, G., Gkolemi, M., Tsirantonakis, D., Chrysoulakis, N., Morrison, W., Fenner, D., Christen, A., Reinicke, T., Grimmond, S., Birkmann, J., and Abrams, M.: Analysis of urban surface temperatures from ground-based and airborne sensors: Berlin, Germany, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-450, https://doi.org/10.5194/icuc12-450, 2025.

The goal of this project is to detect and analyze local and large-scale drivers of urban fog dissipation using satellite data. 

In urban areas the heat island effect is often associated with a suppression or an accelerated dissipation of fog. This can be explained by night-time urban warming that lifts the cloud base, while day-time solar radiation may contribute to fog ”burnoff” in the morning hours due to enhanced heating of the surface and the air above. However, the processes determining the urban fog life cycle are not yet fully understood. To better understand determinants of fog life cycle in urban areas, meteorological conditions associated with urban fog dissipation are analyzed in Milan using a variety of data sources. 

SEVIRI (Spinning Enhanced Visible Infra-Red Imager) satellite data is used to determine urban fog persistence and dissipation, while meteorological data from METAR observations (Meteorological Aerodrome Report), urban weather stations and ERA-5 reanalysis is used to investigate its local and large-scale characteristics.. Distinct meteorological patterns are identified that favor fog dissipation in Milan on particular days and the preceding nights. This study has implications for the urban climate in many European cities where morning fog variability is determining the urban radiation budget and, consequently, heat stress in urban environments.

How to cite: Fuchs, J. and Deny, M.: Diurnal patterns and drivers of urban fog dissipation in Milan, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-476, https://doi.org/10.5194/icuc12-476, 2025.

Surface temperature, a key factor in the urban surface energy balance, influencing human thermal comfort, heat fluxes, and building energy use. Three-dimensional (3D) surface temperature is crucial for these applications, but satellite-derived land surface temperature (LST) has a directional view bias. 3D building energy balance models show potential in evaluating satellite view biases. Here, the VTUF-3D model (Nice et al., 2018, based on Krayenhoff and Voogt, 2007) is compared to ground-based thermal camera observations and satellite LST in Berlin, Germany.

During the urbisphere-Berlin campaign (Fenner et al., 2024), four Optris PI160 thermal cameras were mounted 80 m above ground level on a residential tower block to capture diverse urban surfaces. Emissivity correction was applied on the images. ASTER LST was retrieved in multiple locations in Berlin, covering a total area of 2 km².

VTUF-3D simulations used a 5 m grid resolution and material properties based on the Hertwig et al. (2025) approach. Forcing meteorological data came from the same site as the cameras.

For comparison with the cameras, sample facets (building facades and ground) were selected and the average temperature from the cameras was compared to the modelled temperature. Mean absolute error (MAE) is 3-8 K, with greater errors for surfaces such as glass or metal due to parametrization challenges.

To compare the model results against LST, near-nadir view was assumed allowing the consideration of horizontal facets only. VTUF-3D pixels were aggregated to satellite resolution (90 m) to allow comparison. MAE is 2-7 K (MBE > 0 K), with greater errors in vegetated areas.

Overall, VTUF-3D performs reasonably well, but its limitations must be considered during simulations for reliable results. Our simulations can help inform assessment of neighborhood-scale temperature variability, and support urban heat monitoring, climate resilience, and urban planning initiatives for city-wide heat management and sustainable development.

How to cite: Gkolemi, M., Mitraka, Z., Hertwig, D., Morrison, W., Fenner, D., Chrysoulakis, N., Grimmond, S., Christen, A., and Birkmann, J.: Comparison of urban surface temperatures derived from in-situ and satellite TIR data with modeled 3D urban surface temperatures, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-602, https://doi.org/10.5194/icuc12-602, 2025.

Land function types exhibit varying heat performances, influenced by their external neighborhood environments. Jointly allocating internal and external land functions may offer a sustainable urban management strategy to mitigate urban heat islands (UHI), though the related research is limited. To address this gap, we focused on Hong Kong, using land surface temperature (LST) as a climate indicator to explore the impact of internal and neighboring land function zones on surface heat conditions. First, we quantified the internal and external environments, internal-external interaction degree, and the internal land function mixing level using a reality-based spatial quantification method. Second, we employed a Lasso regression model to investigate how land function features influence LST. Third, we used ANOVA and variance partitioning to identify the contributions of internal land functions and the largest neighboring land functions to LST. The results indicate that (1) Internal land function types significantly affect LST. Industrial areas have the highest LST, followed by residential/office and transportation areas, while vegetation and water bodies exhibit lower LST. Proximity to industrial or transportation areas raises LST, whereas surrounding vegetation cools it. (2) ANOVA and variance partitioning reveal that internal land functions account for 49.8% of LST variation, main surrounding functions for 2.8%, and their interactions for 3.5%. (3) The interaction impacts vary across different internal-external function combinations. Oceans particularly cool residential and office areas, while vegetation is more effective for industrial and transportation zones. Conversely, artificial land uses, especially industrial and transportation areas, significantly increase internal area temperatures. (4) Areas with complex shapes and higher internal mixed levels show lower LST, suggesting that increased interaction with external surroundings and greater diversity of land functions can reduce LST. These findings are crucial for urban planners aiming to mitigate UHI through sustainable design, particularly in mixed-function zones and urban-rural transition areas.

How to cite: wang, M.: Optimizing Internal-External Land Function Allocation to Mitigate Urban Heat Islands: Insights from Hong Kong, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-929, https://doi.org/10.5194/icuc12-929, 2025.