The effect of radiometer placement and view on inferred directional and hemispheric radiometric temperatures of a urban canopy

Abstract

Abstract. Any radiometer at a fixed location has a biased view when observing a convoluted, three dimensional surface such as an urban canopy. The goal of this contribution is to determine the bias of various sensors views observing a simple urban residential neighbourhood (nadir, oblique, hemispherical) over a 24 h cycle under clear weather conditions. The error in measuring longwave radiance (L) and/or inferring surface temperatures (T0) is quantified for different times over a diurnal cycle. Panoramic time-sequential thermography (PTST) data was recorded by a thermal camera on a hydraulic mast above a residential canyon in Vancouver, BC. The dataset resolved sub-facet temperature variability of all representative urban facets in a 360° swath repetitively over a 24 h cycle. This dataset is used along with computer graphics and vision techniques to project measured fields of L for a given time and pixel onto texture sheets of a three-dimensional urban surface model at a resolution of centimetres. The resulting dataset attributes L of each pixel on the texture sheets to different urban facets and associates facet location, azimuth, slope, material, and sky view factor. The texture sheets of L are used to calculate the complete surface temperature (T0,C) and to simulate the instantaneous field of view (IFOV) of narrow and hemispheric radiometers observing the same urban surface (in absence of emissivity and atmospheric effects). The simulated directional (T0,d) and hemispheric (T0,h) radiometric temperatures inferred from various biased views are compared to T0,C. For a range of simulated off-nadir (ϕ) and azimuth (Ω) angles, T0,d (ϕ, Ω) and T0,C differ between −2.7 and +2.9 K over the course of the day. The effects of effective anisotropy are highest in the daytime, particularly around sunrise and sunset when different views can lead to differences in T0,d (ϕ, Ω) that are as high as 3.5 K. For a sensor with a narrow IFOV in the nadir of the urban surface, T0,d (ϕ = 0°) differs from T0,C by −2.2 K (day) and by +1.6 K (night). Simulations of the IFOV of hemispherical, downward-facing pyrgeometers at 270 positions show considerable variations in the measured L and inferred hemispherical radiometeric temperature T0,h as a function of both horizontal placement and height. The root mean squared error (RMSE) between different horizontal positions in retrieving outgoing longwave emittance L↑ decreased exponentially with height, and was 11.2, 6.3 and 2.0 W m−2 at 2, 3, and 5 times the mean building height zb. Generally, above 3.5 zb the horizontal positional error is less than the typical accuracy of common pyrgeometers. The average T0,h over 24 h determined from the hemispherical radiometer sufficiently above an urban surface is in close agreement with the average T0,C. However, over the course of the day, the difference between T0,h and T0,C shows an RMSE of 1.8 K (9.9 W m−2) because the relative contributions of facets within the projected IFOV of a pyrgeometer do not correspond to their fractions of the complete urban surface.

Time-Continuous Hemispherical Urban Surface Temperatures

“…Both T hem, r and T plan overestimate T comp by day and underestimate T comp by night. These results are consistent with findings in [10,34] observed for clear sky days. However, when analyzed over a time series, over/underestimations from geometric effects are highly dependent on synoptic conditions, particularly for a nadir view of the urban surface.…”

“…• T hem, r samples the surface hemispherically (i.e., samples vertical, horizontal, and sloped features), providing a temperature that is more geometrically representative than one from a narrow field-of-view remote sensor in the nadir [10].…”

Integration of Aerial Thermal Imagery, LiDAR Data and Ground Surveys for Surface Temperature Mapping in Urban Environments

“…Furthermore, if the effects of multiple reflections among the complex surfaces of an urban environment are considered, the effective emissivity may differ from the emissivity of the material, and a correlation between SVF and effective emissivity can be found [34]. Thus, emissivity is likely to be the major source of uncertainty in surface temperature mapping, especially considering the large variability of materials occurring in an urban environment and the variability of emissivity values even for the same material, due to aging and weathering [23,35]. For the present work, emissivity values are directly measured for the calibration and control points, and they can be considered valid for those locations.…”

“…A proper modeling of the reflections from surrounding objects requires a detailed 3D model and the evaluation of both surface temperatures and emissivities for all of the facets of the urban texture, but is often neglected due to data unavailability. Although some experiences in the literature tried to solve this complexity in the case of infrared imagery acquired with oblique views over street canyons or single blocks [23,24], these approaches seem hardly feasible when analyzing a whole urban area with aerial imagery. The second assumption is a constant emissivity over the band interval, according to the hypothesis of grey-body behavior.…”

“…To obtain Equation (3), two main simplifications are adopted. Firstly, the radiance emitted by the surrounding objects is assumed to be equal to the one emitted by the target surface (l sr = l B ); actually, temperatures of various urban surfaces (roofs, walls, ground) can vary considerably due to differences in the facet-specific energy-balance [23]. A proper modeling of the reflections from surrounding objects requires a detailed 3D model and the evaluation of both surface temperatures and emissivities for all of the facets of the urban texture, but is often neglected due to data unavailability.…”