A novel method to obtain three-dimensional urban surface temperature from ground-based thermography

Abstract

Urban geometry and materials combine to create complex spatial, temporal and directional patterns of longwave infrared (LWIR) radiation. Effective anisotropy (or directional variability) of thermal radiance causes remote sensing (RS) derived urban surface temperatures to vary with RS view angles. Here a new and novel method to resolve effective thermal anisotropy processes from LWIR camera observations is demonstrated at the Comprehensive Outdoor Scale MOdel (COSMO) test site. Pixel-level differences of brightness temperatures reach 18.4 K within one hour of a 24-h study period. To understand this variability, the orientation and shadowing of surfaces is explored using the Discrete Anisotropic Radiative Transfer (DART) model and Blender three-dimensional (3D) rendering software. Observed pixels and the entire canopy surface are classified in terms of surface orientation and illumination. To assess the variability of exitant longwave radiation () from the 3D COSMO surface (3), the observations are prescribed based on class. The parameterisation is tested by simulating thermal images using a camera view model to determine camera perspectives of 3 fluxes. The mean brightness temperature differences per image (simulated and observed) are within 0.65 K throughout a 24-h period. Pixel-level comparisons are possible with the high spatial resolution of 3 and DART camera view simulations. At this spatial scale (< 0.10 m), shadow hysteresis, surface sky view factor and building edge effects are not completely resolved by 3. By simulating apparent brightness temperatures from multiple view directions, effective thermal anisotropy of 3 is shown to be up to 6.18 K. The developed methods can be extended to resolve some of the identified sources of sub-facet variability in realistic urban settings. The extension of DART to the interpretation of ground-based RS is shown to be promising. List of symbols and acronyms [units] β, ϕ, ω Euler angles describing a sequence of rotations within the (, ,) coordinate frame BOA Bottom of atmosphere BRF Bidirectional reflectance factor C Non-specific camera COSMO COmprehensive urban Scale MOdel Camera focal plane array size [mm] DART Discrete Anisotropic Radiative Transfer model (Gastellu-Etchegorry et al., 2012) DSM Digital surface model ε Emissivity ↓ Broadband longwave radiation flux (irradiance) downward from sky [W m-2 ] ↓ Broadband shortwave radiation flux (irradiance) downward from sky [W m-2 ] , ↓ 3 Broadband longwave radiation flux (exitance) from discrete points of an urban surface, resolved in 3D [W m-2 ] Camera derived broadband longwave radiation flux (exitance) [W m-2 ] Non-specific broadband longwave radiation flux (exitance) from urban canopy elements [W m-2 ]

Variability of urban surface temperatures and implications for aerodynamic energy exchange in unstable conditions

“…However, this requires more detailed surface temperature measurements than can currently be obtained by EO and is the driver behind other ongoing work (e.g. Morrison et al ., ).…”

“…Kanda et al ., ). Practically, T 0 is difficult to measure and is often replaced in Equation with some other surface temperature, such as the satellite‐observed land surface temperature ( LST ), the radiative surface temperature ( T R ) calculated from measurements of upwelling long wave radiation, or area‐weighted facet‐based complete surface temperatures from some combination of thermal camera measurements (Voogt and Grimmond, ) and 3D surface models (Morrison et al ., ).…”

Urban storage heat flux variability explored using satellite, meteorological and geodata

“…Thus, as T a controls the change in both (T a and T s ), this may cause ΔQ S discrepancies, especially if T a variability is not accounted for. Improvements in surface temperature for different facets and their relation to different cooling/heating rates are being explored (Morrison et al 2018(Morrison et al , 2020.…”

“…what surfaces are seen from the sensor used to derive T LST . This is a well-known issue (Voogt 2008;Voogt and Oke 1997;Morrison et al 2018Morrison et al , 2020 and not considered in this study. Furthermore, the downscaling procedure can introduce biases in T LST (Mitraka et al 2015).…”

Variability of physical meteorology in urban areas at different scales: implications for air quality

“…11,12 To enhance our theoretical understanding of processes and/or to undertake numerical predictions we typically simplify the urban form to capture what are thought to be the most relevant features. 4,13,14 For example, buildings may be treated as having smooth walls, 15 rather than accounting for variations caused by the presence of windows, doors, balconies (etc.) or even non-cuboid shapes.…”