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Morphological characteristics and remote sensing application

  • role： First author第一作者
  • Affiliation：

    School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing 100048, China

  • Email：deyonghu@cnu.edu.cn
  • Introduction：胡德勇，1974 年生，男，教授，研究方向为遥感和地理信息系统在自然灾害、资源环境等领域的应用。E-mail： deyonghu@cnu.edu.cn
HU Deyong12,  
  • Affiliation：

    School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing 100048, China

ZHANG Yani12,  
  • Affiliation：

    School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing 100048, China

LIU Manqing12,  
  • Affiliation：

    School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing 100048, China

YU Chen12,  
  • Affiliation：

    School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

CAO Shisong3,  
  • Affiliation：

    School of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resource Environment and Geographic Information System, Beijing 100048, China

DI Yufei12

Resümee

Accurate quantification of surface solar radiation is the basis of remote sensing inversion of reflectivity, and a research on urban surface radiation is important. The sky view factor is selected to characterize the morphological characteristics of the underlying surface of the urban area, and the Urban Surface Solar Radiation Model (USSR) is constructed. This model has distinguished the different effects of direct solar radiation, diffuse sky radiation, and environmental radiation on ground objects. The remote sensing data of Landsat 8 visible and near-infrared bands are considered the examples, and the application prospect of the USSR model for the quantification of urban surface solar radiation is analyzed. The research conclusions are as follows: (1) The USSR clearly quantifies the radiation components of urban surface based on the sky view factor (V), which can effectively solve the simulation of solar radiation transfer process of urban surface and better express the influence of the morphological structure of urban underlying surface on the incident radiation. (2) When USSR is applied to the estimates of solar radiation in the visible and near-infrared bands of Landsat 8 remote sensing data, compared with the estimated values without considering the influence of the morphological characteristics of the underlying surface, the urban surface solar radiation values estimated based on the USSR model can better express the “interception” effect of urban underlying surfaces on the incident radiation. (3) Compared with the TEB model, the USSR model estimates have high correlation, which indirectly verifies the availability of the USSR model. (4) According to the sensitivity analysis of V and reflectivity of the underneath surface, the results show an increasing trend as the V value of the underlying surface increased. In general, the parameter setting value is weak and insensitive compared with the parameter V. The proposed USSR model can amend the estimation results of urban surface solar radiation and improve the reliability of estimation results, thereby expanding the application depth and breadth of urban remote sensing.

Schlüsselwort

solar radiation;urban surface;morphological characteristics;sky view factor;underlying surface reflectance

References

  1. 1.
    Achour-Younsi S and Kharrat F. 2016. Outdoor thermal comfort: impact of the geometry of an urban street canyon in a Mediterranean subtropical climate–case study Tunis, Tunisia. Procedia-Social and Behavioral Sciences, 216: 689-700
  2. 2.
    Barlow J F. 2014. Progress in observing and modelling the urban boundary layer. Urban Climate, 10: 216-240
  3. 3.
    Cantelli A, Monti P and Leuzzi G. 2015. Numerical study of the urban geometrical representation impact in a surface energy budget model. Environmental Fluid Mechanics, 15(2): 251-273
  4. 4.
    Chen L, Ng E, An X P, Ren C, Lee M, Wang U and He Z J. 2012. Sky view factor analysis of street canyons and its implications for daytime intra-urban air temperature differentials in high-rise, high-density urban areas of Hong Kong: a GIS-based simulation approach. International Journal of Climatology, 32(1): 121-136
  5. 5.
    Chen L, Yu B L, Yang F and Mayer H. 2016. Intra-urban differences of mean radiant temperature in different urban settings in Shanghai and implications for heat stress under heat waves: a GIS-based approach. Energy and Buildings, 130: 829-842
  6. 6.
    Gál T and Unger J. 2014. A new software tool for SVF calculations using building and tree-crown databases. Urban Climate, 10: 594-606
  7. 7.
    Groleau D and Mestayer P G. 2013. Urban morphology influence on urban albedo: a revisit with the SOLENE model. Boundary-Layer Meteorology, 147(2): 301-327
  8. 8.
    Guo G H, Zhou X Q, Wu Z F, Xiao R B and Chen Y B. 2016. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Environmental Modelling and Software, 84: 427-439.
  9. 9.
    Kondo A, Ueno M, Kaga A and Yamaguchi K. 2001. The influence of urban canopy configuration on urban albedo. Boundary-Layer Meteorology, 100(2): 225-242
  10. 10.
    Liu Q H, Cao B, Zeng Y L, Li J, Du Y M, Wen J G, Fan W L, Zhao J and Yang L. 2016. Recent progresses on the remote sensing radiative transfer modeling over heterogeneous vegetation canopy. Journal of Remote Sensing, 20(5): 933-945
  11. 11.
    Loridan T and Grimmond C S B. 2012. Multi‐site evaluation of an urban land‐surface model: intra‐urban heterogeneity, seasonality and parameter complexity requirements. Quarterly Journal of the Royal Meteorological Society, 138(665): 1094-1113.
  12. 12.
    Machete R, Falcão A P, Gomes M G and Rodrigues A M. 2018. The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential. Energy and Buildings, 177: 290-302
  13. 13.
    Martinelli L and Matzarakis A. 2017. Influence of height/width proportions on the thermal comfort of courtyard typology for Italian climate zones. Sustainable Cities and Society, 29: 97-106
  14. 14.
    Masson V. 2000. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology, 94(3): 357-397
  15. 15.
    Masson V. 2006. Urban surface modeling and the meso-scale impact of cities. Theoretical and Applied Climatology, 84(1/3): 35-45
  16. 16.
    Meier F, Scherer D and Richters J. 2010. Determination of persistence effects in spatio-temporal patterns of upward long-wave radiation flux density from an urban courtyard by means of Time-Sequential Thermography. Remote Sensing of Environment, 114(1): 21-34
  17. 17.
    Mousivand A, Verhoef W, Menenti M and Gorte B. 2015. Modeling top of atmosphere radiance over heterogeneous non-Lambertian rugged terrain. Remote Sensing, 7(6): 8019-8044
  18. 18.
    Overby M, Willemsen P, Bailey B N, Halverson S and Pardyjak E R. 2016. A rapid and scalable radiation transfer model for complex urban domains. Urban Climate, 15: 25-44
  19. 19.
    Park C, Schade G W, Werner N D, Sailor D J and Kim C H. 2016. Comparative estimates of anthropogenic heat emission in relation to surface energy balance of a subtropical urban neighborhood. Atmospheric Environment, 126: 182-191
  20. 20.
    Pigeon G, Zibouche K, Bueno B, Le Bras J and Masson V. 2014. Improving the capabilities of the Town Energy Balance model with up-to-date building energy simulation algorithms: an application to a set of representative buildings in Paris. Energy and Buildings, 76: 1-14
  21. 21.
    Richter R and Schläpfer D. 2002. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric/topographic correction. International Journal of Remote Sensing, 23(13): 2631-2649
  22. 22.
    Sugawara H and Takamura T. 2006. Longwave radiation flux from an urban canopy: evaluation via measurements of directional radiometric temperature. Remote Sensing of Environment, 104(2): 226-237
  23. 23.
    Wang J D, Zhang L X, Liu Q H, Zhang B and Yin Q. 2009. Knowledge Base of Typical Ground Objects Spectrum in China. Beijing: Science Press
  24. 24.
    Wang K C and Dickinson R E. 2013. Contribution of solar radiation to decadal temperature variability over land. Proceedings of the National Academy of Sciences of the United States of America, 110(37): 14877-14882
  25. 25.
    Wang K C and Liang S L. 2009. Estimation of daytime net radiation from shortwave radiation measurements and meteorological observations. Journal of Applied Meteorology and Climatology, 48(3): 634-643
  26. 26.
    Wang Q and Li P H. 2013. Canopy vertical heterogeneity plays a critical role in reflectance simulation. Agricultural and Forest Meteorology, 169: 111-121.
  27. 27.
    Wang Y P and Akbari H. 2014. Effect of sky view factor on outdoor temperature and comfort in Montreal. Environmental Engineering Science, 31(6): 272-287
  28. 28.
    Wen J G, Liu Q H, Xiao Q, Liu Q and Li X W. 2008. Modeling the land surface reflectance for optical remote sensing data in rugged terrain. Science in China Series D: Earth Sciences, 51(8): 1169-1178
  29. 29.
    Wen J G, Liu Q, Tang Y, Dou B C, You D Q, Xiao Q, Liu Q H and Li X W. 2015. Modeling land surface reflectance coupled BRDF for HJ-1/CCD data of rugged terrain in Heihe river basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4): 1506-1518
  30. 30.
    Wen J G, Liu Q, Xiao Q, Liu Q H, You D Q, Hao D L, Wu S B and Lin X W. 2018. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sensing, 10(3): 370
  31. 31.
    Wu S B, Wen J G, Gastellu-Etchegorry J, Liu Q H, You D Q, Xiao Q, Hao D L, Lin X W and Yin T G. 2019. The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225: 403-415
  32. 32.
    Yang F, Qian F and Lau S S Y. 2013. Urban form and density as indicators for summertime outdoor ventilation potential: a case study on high-rise housing in Shanghai. Building and Environment, 70: 122-137
  33. 33.
    Zakšek K, Oštir K and Kokalj Ž. 2011. Sky-view factor as a relief visualization technique. Remote Sensing, 3(2): 398-415
  34. 34.
    Zeng L Y, Lu J, Li W Y and Li Y C. 2018. A fast approach for large-scale sky view factor estimation using street view images. Building and Environment, 135: 74-84
  35. 35.
    Zhang H L, Zhu S Y, Gao Y and Zhang G X. 2016. The relationship between urban spatial morphology parameters and urban heat island intensity under fine weather condition. Journal of Applied Meteorological Science, 27(2): 249-256
  36. 36.
    Zhang X T, Liang S L, Wild M and Jiang B. 2015. Analysis of surface incident shortwave radiation from four satellite products. Remote Sensing of Environment, 165: 186-202
  37. 37.
    Zhu S Y, Guan H D, Bennett J, Clay R, Ewenz C, Benger S, Maghrabi A and Millington A C. 2013. Influence of sky temperature distribution on sky view factor and its applications in urban heat island. International Journal of Climatology, 33(7): 1837-1843

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