A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse)

Wang, Dandan; Chen, Yunhao; Zhan, Wenfeng

doi:10.1016/j.rse.2018.02.051

Cited by 29 publications

(6 citation statements)

References 48 publications

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“…Ts distributions give a unique temporally continuous dataset. Observations and the underlying methods for their retrieval and classification could be used as input and to evaluate unstably stratified large eddy simulation modelling (Gronemeier et al, 2017) and building energy balance models (Bueno et al, 2012), or input to radiative transfer models such as DART (Gastellu-Etchegorry et al, 2015) for evaluation of effective thermal anisotropy (Krayenhoff and Voogt, 2016;Morrison et al, 2018;Wang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

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

2021

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Section: Discussionmentioning

confidence: 99%

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

2021

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“…To further understand uncertainties with emissivity correction from changes in surface temperature distributions, the surface temperature distribution can be pre-processed by DART (e.g. Wang, Chen and Zhan, 2018) or by user-defined approaches (Morrison et al, 2018) to resolve its variability from shadow patterns.…”

Section: Discussion 61 Flexibility In the Correction Frameworkmentioning

confidence: 99%

“…Wu et al, 2018) and longwave (e.g. Wang et al, 2018) IR spectrum. DART has been evaluated for simple terrain (Sobrino et al, 2011).…”

Section: Dart Radiative Transfer and Sensor View Modelmentioning

confidence: 99%

Atmospheric and emissivity corrections for ground-based thermography using 3D radiative transfer modelling

Morrison

Yin

Lauret

et al. 2020

Remote Sensing of Environment

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“…Studies from Toulouse city centre show the thermal anisotropy can reach 10 K on clear summer days (Lagouarde et al ., 2010; Lagouarde et al ., 2012). This severely affects mapping of radiometric surface temperature and then the interpretation of SUHI (Voogt, 2011; Zhan et al ., 2012; Hu and Brunsell, 2013; Huang et al ., 2016; Wang et al ., 2018; Wang and Chen, 2019; Li and Li, 2020). Current studies on SUHI are based on the radiometric surface temperature observed by thermal imaging radiometers above the urban canopy without considering the impact of anisotropy (Weng et al ., 2004; Peng et al ., 2012; Li et al ., 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Menenti

et al. 2020

Intl Journal of Climatology

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The surface urban heat island intensity (SUHII) when using nadir-viewing radiometric and complete surface temperature (T r and T c) was evaluated. The urban areas of the Kowloon peninsula and Hong Kong Island were selected and four daytime Landsat TM images and two nighttime ASTER images were collected to retrieve T r and then model T c based on a semi-empirical model. SUHIIs were estimated using both the retrieved T r and modelled T c. High spatial resolution (HR) airborne thermal images (0.2 m) observed at 12:10 noon on Oct 24, 2017 were used to retrieve T c directly. Results based on HR data and satellite data were consistent and indicated that the geometry of the built-up space had a larger impact on SUHII when using T c (SUHIIc) than T r (SUHIIr). During daytime SUHIIc decreased while SUHIIr showed a very slight increase with building density. Both SUHIIc and SUHIIr decreased with higher building height but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr decreased with increasing building height variance and increased with increasing sky view factor (SVF). The rate of decrease with building height variance for SUHIIc was larger than SUHIIr. The rate of increase of SUHIIc with SVF was higher than SUHIIr. During nighttime, geometry effects on SUHIIc and SUHIIr were different from daytime. Both SUHIIc and SUHIIr increased with building density, while the rate of increase of SUHIIc with building density, as well as with building height, was much higher than SUHIIr. Both SUHIIc and SUHIIr decreased with SVF, but the rate of decrease of SUHIIc was higher than SUHIIr. Both SUHIIc and SUHIIr initially increased with building height variance and then remained Abbreviations: SUHIIc, The difference between the complete surface temperature of reference rural areas and the complete surface temperature of urban areas; SUHIIr, The difference between the radiometric surface temperature of reference rural areas and the radiometric surface temperature of urban areas; UHII, The difference between air temperature of reference rural station and the air temperature of urban stations.

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A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse)

Cited by 29 publications

References 48 publications

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

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

Atmospheric and emissivity corrections for ground-based thermography using 3D radiative transfer modelling

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

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