Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts.

Accurate characterization of surface air temperature at the urban scale is relevant for developing effective climate change adaptation and mitigation strategies in the context of global warming. However, reanalysis products such as ERA5-Land provide 2 m air temperature (T2m) at relatively coarse spatial resolutions, limiting their applicability for detailed urban-scale analyses. To address this limitation, this study focuses on the spatial downscaling of ERA5-Land T2m from 0.1° to 0.05° resolution using a deep learning–based approach. A specific type of Convolutional Neural Network (CNN), known as Super Resolution Deep Residual Network (SRDRN), is implemented to enhance the spatial detail of surface air temperature fields. The proposed framework integrates auxiliary variables derived from satellite observations and meteorological reanalysis data to better capture surface–atmosphere interactions and improve model performance. These auxiliary features include the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), albedo, Normalized Difference Built-up Index (NDBI), as well as meteorological variables such as precipitation, solar radiation, and wind components. Model training and evaluation are performed following a supervised learning approach, with the fine-resolution MERIDA-HRES dataset used as reference data and split into training, validation, and testing subsets. The SRDRN configuration incorporating these multisource auxiliary features outperforms both a previous downscaling experiment based on T2m and baseline methods, including the classical statistical downscaling approach LOcalized Constructed Analog (LOCA) and bilinear interpolation (previous SRDRN: RMSE = 1.4 °C, R² = 0.74). In addition, an evaluation employing the SPHERA dataset at 0.02° spatial resolution further confirms the robustness and spatial consistency of the proposed approach. These results demonstrate that the inclusion of satellite-derived surface data and specific meteorological variables substantially improves the accuracy of downscaled T2m at spatial resolutions closer to the urban scale. By enhancing the spatial resolution of surface air temperature data, this work confirms the potential of deep learning approaches for temperature downscaling and subsequent urban climate analysis. Future work will focus on increasing the spatial resolution to 0.01° and validating the enhanced products against in-situ weather observations to further assess accuracy, robustness, and applicability for urban climate services.