Urban surface energy partitioning drives the urban thermal environment and the urban heat island (UHI) effect. Accurately estimating the Bowen ratio (β), a key indicator of sensible and latent heat flux balance, remains challenging in complex urban environments due to heterogeneous land cover and intricate 3D morphology. This study proposes an improved Simplified Surface Energy Balance Index (S-SEBI) method that integrates the Sky View Factor (SVF) into the dry-wet edge fitting process to enhance the accuracy of Bowen ratio estimation using remote sensing data. Taking Hong Kong's Kowloon Peninsula as the study area, the estimated β values were validated against ground-based meteorological observations and ENVI-met simulations. Results demonstrate that the improved S-SEBI model achieved an RMSE of 0.120 and MAE of 0.080. Spatial and temporal analysis reveals a clear relationship between β and urban morphological parameters, including building height (BH), density (BD), and fractional vegetation cover (FVC). Notably, a U-shaped relationship between β and BD is observed, with FVC consistently reducing β. Moreover, β increases with BH but levels off beyond a threshold, primarily due to building shadow effects: as shadow coverage expands and then stabilizes, sensible and latent heat fluxes also stabilize, leading to a steady surface Bowen ratio. This study highlights the effectiveness of incorporating urban geometric indicators into remote sensing models and provides a physically sound framework for assessing urban energy balance and climate mitigation strategies.

The urban complex material and geometry characteristics result in a 3-D thermal heterogeneity and that limits the urban surface temperature (UST) retrieval. In this study, we improved the temperature and emissivity separation (TES) algorithm by incorporating thermal heterogeneity within mixed pixel (MP). The improvement was based on the discrete anisotropic radiative transfer (DART) model and applied to retrieve land surface temperature (LST) from Sustainable Development Goals Science Satellite 1 (SDGSAT-1). The TES algorithm retrieval approach for MP (TES-MP) algorithm was validated with ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the data simulated by the DART model, and the results show that it can reach good accuracy under complex urban conditions. Based on the simulated scenes from the Sheung Wan building in Hong Kong, the root mean squared error (RMSE) of the TES-MP algorithm is 0.85 K under thermal homogeneous conditions and 1.13 K under thermal heterogeneous conditions. Additionally, new high-reflectivity construction materials are common in urban areas, i.e., metal materials. It shows that the relationship between maximum-minimum difference (MMD) and minimum emissivity (ε_min) is not applicable to these materials. Thus, the impacts of such materials on the UST retrieval were evaluated. The results show that the higher the reflectivity and the fractional abundance of such materials, the larger the LST underestimation. Under nadir observation conditions, the proportion of high-reflectivity walls does not cause significant LST retrieval errors. The geometry and adjacency effects on retrieved LST were evaluated, and the results show that the TES-MP algorithm has some resistance to geometry and adjacency effects, thereby reducing errors in LST retrieval. This study provides a new view on retrieving LST of urban MPs and also suggests that three or more bands should be considered when setting up thermal infrared (TIR) sensors.

Accurate estimation of urban land surface temperature (ULST) is critical for studying urban heat islands, but complex three-dimensional (3D) structures and materials in urban areas introduce significant adjacency effects into remote sensing retrievals. To investigate the influence of different factors on the adjacency effects, this study employed the DART model to quantify brightness temperature differences (ΔTb) of urban pixels by comparing their simulated radiance in two scenarios: (1) an isolated state (no adjacent buildings) and (2) an adjacent state (with surrounding buildings). ΔTb, representing the adjacency effect, was systematically analyzed across spatial resolutions (1–120 m), building geometry (building height BH, roof area index (Formula presented.), adjacent obstruction degree SVF_Obs.), and material reflectance (reflectance R = 0.05, 0.1, 0.15) to determine key influencing factors. The results demonstrate that (1) adjacency effects intensify significantly with higher spatial resolution (mean ΔTb ≈ 5 K at 1 m vs. ≈2 K at 30 m), with 60–90 m identified as the critical resolution range where the adjacency-induced error is attenuated to a level (ΔTb < 1 K) that is commensurate with the intrinsic uncertainty of current mainstream ULST algorithms; (2) increased building height, reduced density ((Formula presented.)), and greater adjacent obstruction (SVF_Obs.) exacerbate adjacency effects; (3) material emissivity (ε = 1 − R) is the dominant factor, where low-ε materials (high R) exhibit markedly stronger adjacency effects than geometric influences (e.g., ΔTb at R = 0.15 is approximately three times higher than at R = 0.05); and (4) temperature differences among surface components exert minimal influence on adjacency effects (ΔTb < 0.5 K). This study clarifies key factors driving adjacency effects in high-resolution ULST retrieval and defines the critical spatial resolution for simplifying inversions, providing essential insights for accurate urban temperature estimation.

Trees are the most important natural factor to alleviate the urban heat island effect and the latent heat flux (LE) they release contributes significantly to urban cooling. In this study, the model ENVI-met was used to study the influence of building geometry on the LE exchanged by trees at the block scale in compact urban areas. The building density (BD), building height (BH) and sky view factor (SVF) were used to characterize building geometry. The sensitivity of LE to building geometry was estimated by multi-linear regression analysis. The following conclusions were drawn:(1) the LE of trees is sensitive to building geometry, higher during daytime than night-time in winter and the higher during night-time than daytime in summer; (2) LE changes as expected across the seasons, with LE larger in summer; (3) The shade affects the LE of a tree by influencing the solar irradiance; (4) In winter the average LE of a single tree is larger with 0.06 than 0.12 fractional vegetation cover (FVC). This study can provide useful leads towards further research to explore latent heat exchanges by trees at the street scale.