The effect of sub-facet scale surface structure on wall brightness temperatures at multiple scales

Abstract

Wall surface temperatures are important components of urban climates but are under-sampled by satellite and airborne remote sensing and at the microscale are under-sampled in observational studies. In urban canopy models, they are represented with simplistic geometries. This study examines the effect of microscale (sub-facet) surface structure geometries on wall surface brightness temperature distributions at micro-to neighbourhood scales using mobile sampling traverses of two suburban neighbourhoods with different sub-facet geometries. Visible and thermal imagery were recorded simultaneously and combined and classified to create a database of temperatures with associated geographic and thermal attributes. This study investigates (1) if sub-facet scale geometries affect temperature distributions, (2) if these cause canyon scale biases, and (3) if there are therefore inter-neighbourhood biases. It is shown that sub-facet geometries modify wall surface temperatures predominantly by cooling due to self-shading. Surface-sun geometry thus leads to intra-and inter-neighbourhood temperature differences of several degrees Celsius. The observed effects have important implications for modelling of urban surface temperatures, where simplified geometries may overestimate wall surface temperatures.

Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability

“…Further classification requires more detailed visible imagery from e.g. Google Street view, (Li et al, 2018;Gong et al, 2018), study-specific vehicle traverses (Hilland and Voogt, 2020) or manual inspection (Christen et al, 2012).…”

“…Lee et al, 2018) or with vehicle traverses to sample more walls and ground (e.g. Voogt and Oke, 1997;Hilland and Voogt, 2020). Other thermography observations have increased spatial coverage using Asano and Hoyano's (1998) spherical sampling technique (e.g.…”

“…Classification methods have used time consuming and subjective techniques such as manual digitisation (Hartz et al, 2006;Lee et al, 2018;Antoniou et al, 2019) or supervised clustering (Voogt and Oke, 1997;Hénon et al, 2012). To expand classification possibilities, Hilland and Voogt (2020)…”

Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures

“…Considering the buildings in Hong Kong are very tall and narrow, the total wall area may even be higher than the urban horizontal surface area. Wall surface temperatures are important components of the urban climate but are under-sampled by satellite and airborne remote sensing (Hilland and Voogt, 2020). SUHIIr based on radiometric surface temperature may cause a large bias in assessments of SUHI in Hong Kong.…”

A Quantitative Study of a Directional Heat Island in Hefei, China Based on Multi-Source Data

“…However, the current urban heat island index calculation, using surface temperature without considering directionality, cannot truly reflect the actual situation of SUHI intensity. Moreover, there are various factors affecting the urban TRD, including observation time [ 44 , 47 ], surface geometric structure [ 48 , 49 , 50 , 51 , 52 ], physical properties of materials [ 49 , 53 , 54 ], and spatial locations between the sun-surface-sensor [ 55 , 56 , 57 ]; thus, most TRD studies selected more delineated partial surfaces of one or multiple cities for observation or model simulation, and rarely integrated the TRD with SUHI intensity.…”