Früher|Nächster

Remotely sensed urban surface temperature： From directional temperature， complete surface temperature to three-dimensional surface temperature

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：cyh@bnu.edu.cn
  • Introduction：陈云浩，研究方向为资源环境与城市遥感、高光谱与热红外遥感。E-mail： cyh@bnu.edu.cn
CHEN Yunhao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

  • Email：ddwang@cugb.edu.cn
  • Introduction：王丹丹，研究方向为城市热辐射方向性理论与建模。E-mail： ddwang@cugb.edu.cn
WANG Dandan2*,  
  • Affiliation：

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China

    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

ZHAN Wenfeng34,  
  • Affiliation：

    School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

ZHOU Ji5,  
  • Affiliation：

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

HU Deyong6,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

QUAN Jinling7,  
  • Affiliation：

    College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

SUN Hao8,  
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China

    Innovation Center for FengYun Meteorological Satellite, Beijing 100081, China

GUO Zheng910,  
  • Affiliation：

    Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China

XIA Haiping11,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

DAI Xiujuan1,  
  • Affiliation：

    School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

JIANG Lu12

Resümee

Urban land surface temperature is an important indicator of the energy budget of urban underlying surface and local climate change. Remote sensing is an important tool to obtain urban land surface temperature at a large spatial scale. Remarkable urban three-dimensional structure and complex urban surface materials substantially influence the directional variation in upwelling thermal radiance. Thermal infrared remote sensing typically provides an average temperature (i.e., directional temperature) of all component surfaces in a sensor’s field of view at a specific viewing direction. The directional temperature varies with the sensor’s observation angle and differs from the true distribution of urban surface temperature. The term “complete surface temperature” was proposed to represent the characteristics of urban surface temperature to characterize the energy exchange between the urban underlying surface and the atmosphere. Currently, “complete surface temperature” has only made a breakthrough in describing the average state of urban surface temperature, but it still cannot reflect the high-resolution spatiotemporal characteristics of urban surface temperature and cannot meet the needs of fine-scale assessments of urban thermal environment.In this review, we summarize the development of urban surface remote sensing temperature from “directional temperature” (2-dimensional) to “complete surface temperature” (2.5-dimensional) and then to “3-dimensional surface temperature” (3-dimensional) and the current progress in using remote sensing directional observations to obtain urban surface temperature in different dimensions. We also clarify the differences and interrelationships of different dimensions. The application of remotely sensed urban surface temperature in different dimensions is also elaborated. On the basis of the existing problems, the future development trend of remotely sensed urban surface temperature is determined as follows: (1) definition of three-dimensional urban surface temperature for different application purposes, (2) stereoscopic observation for the reconstruction of three-dimensional urban surface temperature, and (3) coupling of three-dimensional surface temperature products and urban climate models.

Schlüsselwort

urban remote sensing;land surface temperature;directional temperature;complete surface temperature;three-dimensional surface temperature

References

  1. 1.
    Adderley C, Christen A and Voogt J A. 2015. The effect of radiometer placement and view on inferred directional and hemispheric radiometric temperatures of an urban canopy. Atmospheric Measurement Techniques, 8(7): 2699-2714
  2. 2.
    Allen M A, Voogt J A and Christen A. 2017. Towards a continuous climatological assessment of urban surface heat islands//2017 Joint Urban Remote Sensing Event (JURSE). Dubai: IEEE: 1-4
  3. 3.
    Allen M A, Voogt J A and Christen A. 2018. Time-continuous hemispherical urban surface temperatures. Remote Sensing, 10(1): 3
  4. 4.
    Arnfield A J. 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1): 1-26
  5. 5.
    Bruse M and Fleer H. 1998. Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling and Software, 13(3/4): 373-384
  6. 6.
    Cao B, Du Y M, Bian Z J, Dong Y D, Zhao F, Hu T, Ran X T, Qin B X, Li H, Xiao Q and Liu Q H. 2021. Assessment of thermal infrared kernel-driven models over row-planted canopies. National Remote Sensing Bulletin, 25(8): 1710-1721
  7. 7.
    Cao B, Liu Q H, Du Y M, Roujean J L, Gastellu-Etchegorry J P, Trigo I F, Zhan W F, Yu Y Y, Cheng J, Jacob F, Lagouarde J P, Bian Z J, Li H, Hu T and Xiao Q. 2019. A review of earth surface thermal radiation directionality observing and modeling: historical development, current status and perspectives. Remote Sensing of Environment, 232: 111304
  8. 8.
    Cao B, Roujean J L, Gastellu-Etchegorry J P, Liu Q H, Du Y M, Lagouarde J P, Huang H G, Li H, Bian Z J, Hu T, Qin B X, Ran X T and Xiao Q. 2021. A general framework of kernel-driven modeling in the thermal infrared domain. Remote Sensing of Environment, 252: 112157
  9. 9.
    Chatzidimitriou A and Yannas S. 2015. Microclimate development in open urban spaces: the influence of form and materials. Energy and Buildings, 108: 156-174
  10. 10.
    Chen Y H, Wu J T and Wang D D. 2018. Review of the study on generalized computer simulation of land surface thermal anisotropy. Advances in Earth Science, 33(6): 555-567
  11. 11.
    Chrysoulakis N, Grimmond S, Feigenwinter C, Lindberg F, Gastellu-Etchegorry J P, Marconcini M, Mitraka Z, Stagakis S, Crawford B, Olofson F, Landier L, Morrison W and Parlow E. 2018. Urban energy exchanges monitoring from space. Scientific Reports, 8(1): 11498
  12. 12.
    Duan S B, Li Z L, Tang B H, Wu H and Tang R L. 2014. Generation of a time-consistent land surface temperature product from MODIS data. Remote Sensing of Environment, 140: 339-349
  13. 13.
    Duan S B, Li Z L, Wang N, Wu H and Tang B H. 2012. Evaluation of six land-surface diurnal temperature cycle models using clear-sky in situ and satellite data. Remote Sensing of Environment, 124: 15-25
  14. 14.
    Duffour C, Lagouarde J P and Roujean J L. 2016. A two parameter model to simulate thermal infrared directional effects for remote sensing applications. Remote Sensing of Environment, 186: 250-261
  15. 15.
    Ermida S L, DaCamara C C, Trigo I F, Pires A C, Ghent D and Remedios J. 2017. Modelling directional effects on remotely sensed land surface temperature. Remote Sensing of Environment, 190: 56-69
  16. 16.
    Ermida S L, Trigo I F, DaCamara C C and Roujean J L. 2018. Assessing the potential of parametric models to correct directional effects on local to global remotely sensed LST. Remote Sensing of Environment, 209: 410-422
  17. 17.
    Fontanilles G, Briottet X, Fabre S and Trémas T. 2008. Thermal infrared radiance simulation with aggregation modeling (TITAN): an infrared radiative transfer model for heterogeneous three-dimensional surface--application over urban areas. Applied Optics, 47(31): 5799-5810
  18. 18.
    Gastellu-Etchegorry J P, Yin T G, Lauret N, Cajgfinger T, Gregoire T, Grau E, Feret J B, Lopes M, Guilleux J, Dedieu G, Malenovský Z, Cook B D, Morton D, Rubio J, Durrieu S, Cazanave G, Martin E and Ristorcelli T. 2015. Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes. Remote Sensing, 7(2): 1667-1701
  19. 19.
    Guillevic P C, Bork-Unkelbach A, Gottsche F M, Hulley G, Gastellu-Etchegorry J P, Olesen F S and Privette J L. 2013. Directional viewing effects on satellite land surface temperature products over sparse vegetation canopies-a multisensor analysis. IEEE Geoscience and Remote Sensing Letters, 10(6): 1464-1468
  20. 20.
    Guo G H, Chen Y B, Wei J B, Wu Z F and Rong X Z. 2012. Impacts of grid sizes on urban heat island pattern analysis. Acta Ecologica Sinica, 32(12): 3764-3772
  21. 21.
    Hénon A, Mestayer P G, Groleau D and Voogt J. 2011. High resolution thermo-radiative modeling of an urban fragment in Marseilles city center during the UBL-ESCOMPTE campaign. Building and Environment, 46(9): 1747-1764
  22. 22.
    Hénon A, Mestayer P G, Lagouarde J P and Voogt J A. 2012. An urban neighborhood temperature and energy study from the CAPITOUL experiment with the SOLENE model. Theoretical and Applied Climatology, 110(1/2): 177-196
  23. 23.
    Hilland R V J and Voogt J A. 2020. The effect of sub-facet scale surface structure on wall brightness temperatures at multiple scales. Theoretical and Applied Climatology, 140(1): 767-785
  24. 24.
    Hu L Q, Monaghan A, Voogt J A and Barlage M. 2016. A first satellite-based observational assessment of urban thermal anisotropy. Remote Sensing of Environment, 181: 111-121
  25. 25.
    Hu L Q and Wendel J. 2019. Analysis of urban surface morphologic effects on diurnal thermal directional anisotropy. ISPRS Journal of Photogrammetry and Remote Sensing, 148: 1-12
  26. 26.
    Huang X and Wang Y. 2019. Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: a case study of Wuhan, Central China. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 119-131
  27. 27.
    Jiang L, Zhan W F, Hu L Q, Huang F, Hong F L, Liu Z H, Lai J M and Wang C G. 2021. Assessment of different kernel-driven models for daytime urban thermal radiation directionality simulation. Remote Sensing of Environment, 263: 112562
  28. 28.
    Jiang L, Zhan W F, Tu L L, Dong P, Wang S S, Li L, Wang C L and Wang C G. 2022. Diurnal variations in directional brightness temperature over urban areas through a multi-angle UAV experiment. Building and Environment, 222: 109408
  29. 29.
    Jiang L, Zhan W F, Voogt J, Zhao L M, Gao L, Huang F, Cai Z and Ju W M. 2018. Remote estimation of complete urban surface temperature using only directional radiometric temperatures. Building and Environment, 135: 224-236
  30. 30.
    Kanda M, Kanega M, Kawai T, Moriwaki R and Sugawara H. 2007. Roughness lengths for momentum and heat derived from outdoor urban scale models. Journal of Applied Meteorology and Climatology, 46(7): 1067-1079
  31. 31.
    Kanda M, Kawai T, Kanega M, Moriwaki R, Narita K and Hagishima A. 2005a. A simple energy balance model for regular building arrays. Boundary-Layer Meteorology, 116(3): 423-443
  32. 32.
    Kanda M, Kawai T and Nakagawa K. 2005b. A simple theoretical radiation scheme for regular building arrays. Boundary-Layer Meteorology, 114(1): 71-90
  33. 33.
    Krayenhoff E S, Christen A, Martilli A and Oke T R. 2014. A multi-layer radiation model for urban neighbourhoods with trees. Boundary-Layer Meteorology, 151(1): 139-178
  34. 34.
    Krayenhoff E S and Voogt J A. 2007. A microscale three-dimensional urban energy balance model for studying surface temperatures. Boundary-Layer Meteorology, 123(3): 433-461
  35. 35.
    Krayenhoff E S and Voogt J A. 2016. Daytime thermal anisotropy of urban neighbourhoods: morphological causation. Remote Sensing, 8(2): 108
  36. 36.
    Kuang W H. 2018. Advance and future prospects of urban land use/cover change and ecological regulation of thermal environment. Scientia Geographica Sinica, 38(10): 1643-1652
  37. 37.
    Lachapelle J A, Krayenhoff E S, Middel A, Meltzer S, Broadbent A M and Georgescu M. 2022. A microscale three-dimensional model of urban outdoor thermal exposure (TUF-Pedestrian). International Journal of Biometeorology, 66(4): 833-848
  38. 38.
    Lagouarde J P, Hénon A, Irvine M, Voogt J, Pigeon G, Moreau P, Masson V and Mestayer P. 2012. Experimental characterization and modelling of the nighttime directional anisotropy of thermal infrared measurements over an urban area: case study of Toulouse (France). Remote Sensing of Environment, 117: 19-33
  39. 39.
    Lagouarde J P, Hénon A, Kurz B, Moreau P, Irvine M, Voogt J and Mestayer P. 2010. Modelling daytime thermal infrared directional anisotropy over Toulouse city centre. Remote Sensing of Environment, 114(1): 87-105
  40. 40.
    Lagouarde J P and Irvine M. 2008. Directional anisotropy in thermal infrared measurements over Toulouse city centre during the CAPITOUL measurement campaigns: first results. Meteorology and Atmospheric Physics, 102(3/4): 173-185
  41. 41.
    Lagouarde J P, Moreau P, Irvine M, Bonnefond J M, Voogt J A and Solliec F. 2004. Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France). Remote Sensing of Environment, 93(4): 443-462
  42. 42.
    Leroyer S, Calmet I and Mestayer P G. 2010. Urban boundary layer simulations of sea-breeze over Marseille during the ESCOMPTE experiment. International Journal of Environment and Pollution, 40(1/3): 109-122
  43. 43.
    Li N N and Li X M. 2020. The impact of building thermal anisotropy on surface urban heat island intensity estimation: an observational case study in Beijing. IEEE Geoscience and Remote Sensing Letters, 17(12): 2030-2034
  44. 44.
    Liu Z H, Zhan W F, Lai J M, Hong F L, Quan J L, Bechtel B, Huang F and Zou Z X. 2019. Balancing prediction accuracy and generalization ability: a hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 189-206
  45. 45.
    Ma W, Chen Y H, Zhan W F and Zhou J. 2013. Thermal anisotropy model for simulated three dimensional urban targets. Journal of Remote Sensing (in Chinese), 17(1): 62-76
  46. 46.
    Masson V. 2000. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology, 94(3): 357-397
  47. 47.
    Masson V, Gomes L, Pigeon G, Liousse C, Pont V, Lagouarde J P, Voogt J, Salmond J, Oke T R, Hidalgo J, Legain D, Garrouste O, Lac C, Connan O, Briottet X, Lachérade S and Tulet P. 2008. The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment. Meteorology and Atmospheric Physics, 102(3/4): 135-157
  48. 48.
    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
  49. 49.
    Meng Q Y. 2020. Remote Sensing of Urban Green Space. Beijing: Science Press
  50. 50.
    Meng Q Y, Zhang L L, Sun Z H, Meng F, Wang L and Sun Y X. 2018. Characterizing spatial and temporal trends of surface urban heat island effect in an urban main built-up area: a 12-year case study in Beijing, China. Remote Sensing of Environment, 204: 826-837
  51. 51.
    Morrison W, Kotthaus S, Grimmond C S B, Inagaki A, Yin T G, Gastellu-Etchegorry J P, Kanda M and Merchant C J. 2018. A novel method to obtain three-dimensional urban surface temperature from ground-based thermography. Remote Sensing of Environment, 215: 268-283
  52. 52.
    Nichol J E. 1998. Visualisation of urban surface temperatures derived from satellite images. International Journal of Remote Sensing, 19(9): 1639-1649
  53. 53.
    Norman J M and Becker F. 1995. Terminology in thermal infrared remote sensing of natural surfaces. Agricultural and Forest Meteorology, 77(3/4): 153-166
  54. 54.
    Oke T R. 1973. City size and the urban heat island. Atmospheric Environment (1967), 7(8): 769-779
  55. 55.
    Peng Z X, Zhou J and Li M S. 2016. Review of methods for simulating land surface temperature at the pixel scale based on ground measurements over heterogeneous surface. Advances in Earth Science, 31(5): 471-480
  56. 56.
    Pigeon G, Moscicki M A, Voogt J A and Masson V. 2008. Simulation of fall and winter surface energy balance over a dense urban area using the TEB scheme. Meteorology and Atmospheric Physics, 102(3/4): 159-171
  57. 57.
    Poglio T, Mathieu-Marni S, Ranchin T, Savaria E and Wald L. 2006. OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment, 104(2): 238-246
  58. 58.
    Roberts S M, Voogt J A and Oke T R. 2009. Outdoor scale model experiment to evaluate the complete urban surface temperature//Eighth Symposium on the Urban Environment. Phoenix. Available from: .
  59. 59.
    Roth M, Oke T R and Emery W J. 1989. Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10(11): 1699-1720
  60. 60.
    Roujean J L, Leroy M and Deschamps P Y. 1992. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data. Journal of Geophysical Research: Atmospheres, 97(D18): 20455-20468
  61. 61.
    Soux A, Voogt J A and Oke T R. 2004. A model to calculate what a remote sensor ‘sees’ of an urban surface. Boundary-Layer Meteorology, 112(2): 401-424
  62. 62.
    Sun H, Chen Y H, Zhan W F and Ma W. 2012. Temperature diurnal change of walls and the effect on modeling urban thermal anisotropy//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 6709-6712
  63. 63.
    Sun H, Chen Y H, Zhan W F, Wang M J and Ma W. 2015. A kernel model for urban surface thermal emissivity anisotropy and its uncertainties. Journal of Infrared and Millimeter Waves, 34(1): 66-73
  64. 64.
    Trigo I F, Monteiro I T, Olesen F and Kabsch E. 2008. An assessment of remotely sensed land surface temperature. Journal of Geophysical Research: Atmospheres, 113(D17): D17108
  65. 65.
    Tsoka S, Tsikaloudaki A and Theodosiou T. 2018. Analyzing the ENVI-met microclimate model’s performance and assessing cool materials and urban vegetation applications-a review. Sustainable Cities and Society, 43: 55-76
  66. 66.
    Vinnikov K Y, Yu Y Y, Goldberg M D, Tarpley D, Romanov P, Laszlo I and Chen M. 2012. Angular anisotropy of satellite observations of land surface temperature. Geophysical Research Letters, 39(23): L23802
  67. 67.
    Voogt J. 2021. Urban heatwaves and thermal remote sensing//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE: 938-941
  68. 68.
    Voogt J A. 2000. Image representations of complete urban surface temperatures. Geocarto International, 15(3): 21-32
  69. 69.
    Voogt J A. 2008. Assessment of an Urban Sensor View Model for thermal anisotropy. Remote Sensing of Environment, 112(2): 482-495
  70. 70.
    Voogt J A and Grimmond C S B. 2000. Modeling surface sensible heat flux using surface radiative temperatures in a simple urban area. Journal of Applied Meteorology, 39(10): 1679-1699
  71. 71.
    Voogt J A and Oke T R. 1997. Complete urban surface temperatures. Journal of Applied Meteorology, 36(9): 1117-1132
  72. 72.
    Voogt J A and Oke T R. 1998a. Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing, 19(5): 895-920
  73. 73.
    Voogt J A and Oke T R. 1998b. Radiometric temperatures of urban canyon walls obtained from vehicle traverses. Theoretical and Applied Climatology, 60(1/4): 199-217
  74. 74.
    Voogt J A and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370-384
  75. 75.
    Wan Z M and Dozier J. 1996. A generalized split-window algorithmfor retrieving land-surface temperature from space. IEEE Transac-tions on Geoscience and Remote Sensing, 34(4): 892-905
  76. 76.
    Wang D D and Chen Y H. 2019. A geometric model to simulate urban thermal anisotropy in simplified dense neighborhoods (GUTA-Dense). IEEE Transactions on Geoscience and Remote Sensing, 57(8): 6226-6239
  77. 77.
    Wang D D, Chen Y H, Cui Y H and Sun H. 2018a. A geometric model to simulate urban thermal anisotropy for simplified neighborhoods. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4930-4944
  78. 78.
    Wang D D, Chen Y H, Hu L Q, Voogt J A, Gastellu-Etchegorry J P and Krayenhoff E S. 2021. Modeling the angular effect of MODIS LST in urban areas: a case study of Toulouse, France. Remote Sensing of Environment, 257: 112361
  79. 79.
    Wang D D, Chen Y H, Hu L Q, Voogt J A and He X Y. 2022. Satellite-based daytime urban thermal anisotropy: a comparison of 25 global cities. Remote Sensing of Environment, 283: 113312
  80. 80.
    Wang D D, Chen Y H, Voogt J A, Krayenhoff E S, Wang J F and Wang L B. 2020. An advanced geometric model to simulate thermal anisotropy time-series for simplified urban neighborhoods (GUTA-T). Remote Sensing of Environment, 237: 111547
  81. 81.
    Wang D D, Chen Y H and Zhan W F. 2018b. A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse). Remote Sensing of Environment, 209: 263-274
  82. 82.
    Wang X X, Li Y G, Wang K, Yang X Y and Chan P W. 2017. A simple daily cycle temperature boundary condition for ground surfaces in CFD predictions of urban wind flows. Journal of Applied Meteorology and Climatology, 56(11): 2963-2980
  83. 83.
    Webster C, Westoby M, Rutter N and Jonas T. 2018. Three-dimensional thermal characterization of forest canopies using UAV photogrammetry. Remote Sensing of Environment, 209: 835-847
  84. 84.
    Wei L T, Jiang X G, Wu H and Ru C. 2022. Review of urban thermal radiation anisotropy. Journal of Geo-Information Science, 24(4): 617-630
  85. 85.
    Xu H Q. 2011. Analysis on urban heat island effect based on the dynamics of urban surface biophysical descriptors. Acta Ecologica Sinica, 31(14): 3890-3901
  86. 86.
    Yang J X, Menenti M, Wu Z F, Wong M S, Abbas S, Xu Y and Shi Q. 2021. Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures. International Journal of Climatology, 41(S1): E3219-E3238
  87. 87.
    Yang J X, Wong M S, Ho H C, Krayenhoff E S, Chan P W, Abbas S and Menenti M. 2020. A semi-empirical method for estimating complete surface temperature from radiometric surface temperature, a study in Hong Kong city. Remote Sensing of Environment, 237: 111540
  88. 88.
    Yao R, Wang L C, Huang X, Wu X J, Yang L and Niu Z G. 2021. Assessing urbanization's contribution to warming in mainland China using satellite-estimated air temperature data. Progress in Physical Geography: Earth and Environment, 45(5): 687-705
  89. 89.
    Yu T, Tian Q Y, Gu X F, Wang J, Liu Q and Yan G J. 2006. Modelling directional brightness temperature over a simple typical structure of urban areas. Journal of Remote Sensing (in Chinese), 10(5): 661-669
  90. 90.
    Zhan W F, Chen Y H, Ma W, Zhou J and Li J. 2010. FOV effect analysis in directional brightness temperature observations for urban targets. Journal of Remote Sensing (in Chinese), 14(2): 372-386
  91. 91.
    Zhan W F, Chen Y H, Voogt J, Zhou J, Wang J F, Liu W Y and Ma W. 2012a. Interpolating diurnal surface temperatures of an urban facet using sporadic thermal observations. Building and Environment, 57: 239-252
  92. 92.
    Zhan W F, Chen Y H, Voogt J A, Zhou J, Wang J F, Ma W and Liu W Y. 2012b. Assessment of thermal anisotropy on remote estimation of urban thermal inertia. Remote Sensing of Environment, 123: 12-24
  93. 93.
    Zhou J, Chen Y H, Li J, Ma W and Zhan W F. 2009. Progress in thermal anisotropy of urban areas: a review. Advances in Earth Science, 24(5): 497-505
  94. 94.
    Zhou W Q and Tian Y Y. 2020. Effects of urban three-dimensional morphology on thermal environment: a review. Acta Ecologica Sinica, 40(2): 416-427

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website