Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top–down and bottom–up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.

Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information; thus, the downscaling of LST is recognized as being an important and inevitable intermediate process. In this paper, improvement in the downscaled LST accuracy is investigated, employing the statistical downscaling methodology in an urban setting. A new approach is proposed, where thermal radiances are disaggregated using multiple regression analysis and are then combined with emissivity values derived from a high-resolution image classification. Predictors include reflectance values, built-up and vegetation indices, and topographic data. Surface classification is performed utilizing machine learning techniques and fusing Sentinel-2 imagery with ancillary data. Thermal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor are downscaled from their original resolution to 100 m in the city of Athens, Greece. Validation of sharpened temperatures is performed using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface temperature product and in-situ measurements. It is demonstrated that the proposed downscaling framework using ridge regression has the potential to produce reliable, high temporal LST estimates with an average error of fewer than 2 K, while consistently having a better accuracy than the reference, single-predictor downscaling of the MODIS LST product.

Variations in urban form lead to the development of distinctive intra-urban surface thermal patterns. Previous assessment of the relation between urban structure and satellite-based Land Surface Temperature (LST) has generally been limited to single-city cases. Here, examining 25 European cities (June–August 2017), we estimated the statistical association between surface parameters—the impervious fraction (λ_imp), the building fraction (λ_b), and the building height (H)—and the neighborhood scale (1000 × 1000 m) LST variations, as captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Correlation analysis, multiple linear regression, and spatial regression were used. As expected, λ_imp had a consistent positive influence on LSTs. In contrast, the relation of LST with λ_b and H was generally weaker or negative in the daytime, whereas at night it shifted to a robust positive effect. In particular, daytime LSTs of densely built, high-rise European districts tended to have lower values. This was especially the case for the city of Athens, Greece, where a more focused analysis was conducted, using further surface parameters and the Local Climate Zone (LCZ) scheme. For the urban core of the city, the canyon aspect ratio H/W had a statistically significant (p <0.01) negative relationship with LST by day (Spearman’s rho = −0.68) and positive during nighttime (rho = 0.45). The prevailing intra-urban surface thermal variability in Athens was well reproduced by a 5-day numerical experiment using the meteorological Weather Research and Forecasting Model (WRF) model and a modified urban parameterization scheme. Although the simulation resulted in some systematic errors, the overall accuracy of the model was adequate, regarding the surface temperature (RMSE = 2.4 K) and the near-surface air temperature (RMSE = 1.7 K) estimations.

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.

In recent years, a considerable body of research has demonstrated the suitability of global and regional reanalysis data for human-biometeorological applications. These applications include the assessment of the outdoor thermal environment and the investigation of its relation to human health, especially in areas where the spatial coverage of surface observational networks is sparse. Here, we present the first comprehensive evaluation of the most recent pan-European regional reanalysis, namely the Copernicus European Regional Reanalysis (CERRA) dataset at 5.5 km spatial resolution, in terms of simulating the observed human bioclimate, as expressed by the modified physiologically equivalent temperature (mPET) that is computed through the RayMan Pro model, and its meteorological drivers. The validation was performed over Greece using up to 11 years of records of 2 m air temperature and relative humidity, 10 m wind speed and global solar radiation derived from 35 sites of the nationwide network of surface weather stations operated by the METEO Unit at the National Observatory of Athens. The ERA5-Land dataset at ~9 km spatial resolution, which represents the current state-of-the-art reanalysis, was also compared against the same observations. Our findings show that the CERRA dataset performs significantly better compared to the ERA5-Land reanalysis with respect to the replication of the examined meteorological variables and mPET. The added value of the CERRA data is particularly evident during the warm period of the year and in regions that are characterized by complex topography and/or proximity to the coastline. Combining the CERRA dataset with population and mortality data, we further showcase its applicability for human-biometeorological and heat–health studies at a local scale, using the regional unit of Rethymno (Crete) as a pilot area for the analysis.

The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with heatwaves. Additionally, this study examines the role of different land cover types in modulating heatwave impacts, employing turbulent flux observations from micrometeorological towers. The interaction between heatwaves and droughts is further explored using the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture data, highlighting the amplifying role of water stress through land–atmosphere feedbacks. The results reveal a statistically significant upward trend in LST-derived thermal anomalies, with the 2022 heatwave identified as the most extreme event, when approximately 75% of Europe experienced strong positive anomalies. On average, 91% of heatwave episodes identified in reanalysis-based air temperature records coincided with LST-defined anomaly events, confirming LST as a robust proxy for heatwave detection. Flux tower observations show that, during heatwaves, evergreen coniferous and mixed forests predominantly enhance sensible heat fluxes (mean anomalies during midday of 74 W/m² and 62 W/m², respectively), while grasslands exhibit increased latent heat flux (89 W/m²). Notably, under extreme compound heat–drought conditions, this pattern reverses for grassed sites due to rapid soil moisture depletion. Overall, the findings underscore the combined influence of surface temperature and drought in driving extreme heat events and introduce a novel, multi-source approach that integrates satellite, reanalysis, and ground-based data to assess heatwave dynamics across scales.