Conference Agenda

Urban surface structure and component temperatures induce significant thermal anisotropy (TA), resulting in substantial differences in observed surface temperatures across varying viewing angles. Although previous studies have investigated the temporal dynamics of TA through observations and modeling, its spatial differentiation over heterogeneous surfaces remains poorly constrained. Resolving how surface heterogeneity influences TA is hindered by the coarse spatial resolution and limited angular sampling of current multi-angle satellite observations. Consequently, most mainstream thermal-anisotropy models were developed for simplified scenes and lack systematic evaluation of their applicability to complex urban environments. To address these challenges, we coupled the microscale 3D urban energy balance model (TUF-3D) with the state-of-the-art Discrete Anisotropic Radiative Transfer (DART) model. This approach allows for rapid and accurate TA modeling of hypothetical urban scenes with varying geometric structures and component temperatures, thereby quantifying the impact of surface heterogeneity on TA. Building height variability was used to represent geometric heterogeneity, while differences in building material properties were used to characterize component temperature heterogeneity. To evaluate , The results of a series of sensitivity experiments have validated the individual effects of geometric and component temperature heterogeneity on TA. From the perspective of component temperature, changes in average component temperatures result in a maximum TA difference of 7.29 K, while temperature variability alone contributes only 0.54 K. These findings suggest that assuming simplified scenes with uniform building heights or homogeneous component temperatures can introduce biases in TA simulations, potentially compromising the accuracy of models correcting for the angular effects of land surface temperature.

Understanding the thermal behavior of soil and vegetation separately is essential for interpreting land–atmosphere energy exchange, diagnosing ecosystem stress, and improving land surface modelling. However, conventional satellite LST products only provide a mixed radiometric signal, masking the distinct thermal responses of soil and canopy. This study introduces GloSVeT, the first global dataset that provides monthly surface soil and vegetation component temperatures at 0.05° resolution for 2003–2023. The dataset is generated using an enhanced multisource fusion framework that integrates multi-temporal MODIS observations with ERA5-Land skin temperature and vegetation structural information to retrieve physically consistent component temperatures. We summarize the data sources, modelling framework, and global implementation strategy, and present an independent evaluation using flux-tower networks with screened spatial representativeness. Validation results show strong agreement with in-situ measurements, with correlations typically above 0.9 and RMSE around 2 K for both soil and vegetation temperatures. Seasonal variations in performance reflect expected hydrothermal conditions, and a small cool bias is attributable to the temporal sampling of satellite observations. GloSVeT provides a new basis for studying surface energy partitioning, monitoring hydrothermal dynamics, and supporting ecosystem and climate model applications.

This study presents the development of a ground-based observation system designed for vicarious calibration of satellite sensors operating in the mid-wave infrared (MWIR) region. Conventional natural targets used in LWIR calibration lack spectrally stable emissivity in MWIR, motivating the need for dedicated reference targets and high-sensitivity measurement instruments. We introduce a thermally controlled ground reference target whose effective emissivity can be tuned by adjusting the ratio of water and metal surfaces using perforated plates of varying hole diameters. In parallel, an MWIR radiation thermometer employing lock-in detection was developed to enable accurate measurement of low-signal MWIR radiance from room-temperature targets. The system achieved measurement uncertainties down to 20–70 mK. A field campaign was conducted at the Goheung Aerospace Center using the integrated reference targets and radiation thermometer to validate performance under real environmental conditions. The results demonstrate the feasibility of applying controlled emissivity targets and lock-in-based MWIR radiometry to improve the accuracy of MWIR vicarious calibration frameworks.

A Method for Identifying Industrial Heat Sources

This study addresses the limitations of conventional urban shortwave radiation simulations in representing complex three-dimensional morphology. A parameterization approach for large-scale urban sky view factor was proposed, significantly improving computational efficiency and spatial adaptability. Based on this, an urban solar shortwave radiation transfer model was developed to quantitatively characterize the shading and reflection effects of building clusters. Furthermore, a novel remote sensing inversion method for urban surface reflectance and solar radiation parameters was introduced, enabling high-accuracy estimation of surface radiative properties and offering a new technical pathway for urban thermal environment and energy balance research.

Urban Heat Islands (UHIs) emerge from reduced vegetation, impervious surfaces, and anthropogenic heat emissions, leading to elevated surface temperatures in urban areas. Monitoring UHIs at fine spatial and temporal scales requires thermal data capable of capturing both urban heterogeneity and daily variability—conditions not satisfied by the native 1 km resolution of MODIS Land Surface Temperature (LST).

This study presents a regime-aware machine learning workflow to downscale daily MODIS LST to the native spatial scale of MODIS NDVI (231 m) over Bologna (Italy), using only MODIS-internal predictors and meteorological forcing. The approach adopts a two-stage architecture: a Ridge regression model estimates a day-level atmospheric bias, while a Random Forest reconstructs pixel-level residuals to recover fine-scale thermal variability from vegetation, land-cover, topographic, and atmospheric predictors. To account for atmospheric control, the dataset is partitioned into three thermal regimes (COLD, MILD, HOT), with independent models trained for each regime.

Pre-processing and data integration were performed in Google Earth Engine using MODIS LST (MOD11A1/MYD11A1), NDVI, SRTM-derived terrain variables, and built-up fraction from ESA WorldCover. Experiments show strong predictive performance (RMSE < 1 K; R² ≈ 0.90) and spatial patterns consistent with Local Climate Zones.

The MILD and HOT regimes provide the largest enhancement in spatial detail compared to the original MODIS product, while the COLD regime shows reduced performance, likely due to weaker surface–atmosphere coupling. Results highlight that atmospheric conditions play a dominant role in downscaling accuracy, exceeding the impact of model architecture. The framework enables scalable, daily UHI monitoring and supports heatwave analysis and climate-resilient urban planning.

Nighttime optical remote sensing provides valuable insights into natural and, in particular, human activities. This study evaluates the nighttime imaging capabilities of the Sentinel-2 mission using the only available nighttime acquisition not limited to ocean observations for dark signal calibration, covering the United Arab Emirates with Dubai in 2015. We checked the detection limit using granules over the Persian Gulf, extracted radiance spectra for different regions of interest, and analysed lighting types and temperatures. Results suggest a conservative nighttime detection limit of approx. 0.37 W/m²/um/sr for visible/near infrared bands, and 0.08 W/m²/um/sr for short-wave infrared bands. Sentinel-2’s high spatial resolution and multispectral bands, although designed for daytime observations, were capable of detecting and classifying bright visible/near and short-wave infrared emitters. Comparisons with hyperspectral EnMAP imagery acquired in 2025 validated the classifications and revealed changes in urban lighting over a decade. While limitations apply, this study highlights S2’s potential for nighttime remote sensing and supports considerations of nighttime capabilities for future satellite missions.