Remotely sensed urban surface temperature： from directional temperature， complete surface temperature to 3-dimensional surface temperature
- Pages: 1-18(2023)
DOI： 10.11834/jrs.20233064
扫 描 看 全 文
- About Journal
- Journal Online
- Special Source
摘要
城市地表温度作为城市下垫面能量收支和局地气候变化的重要参数，一般通过遥感技术手段获取较大范围城市地表温度。由于城市地表呈显著的3维结构，使其呈现出较强的热辐射方向性。而热红外遥感通常提供传感器在特定方向视场中所有组分表面的平均温度（即方向温度），方向温度随传感器观测角度变化，且与城市表面温度的真实分布状况存在差异。为更好地表征城市下垫面与大气的能量交换，常采用“全表面温度”代表城市表面温度特征。目前，“全表面温度”仅在描述城市表面温度的平均状况方面有所突破，但仍然无法体现城市表面温度的高时空分辨率特征，无法满足精细城市热环境评估的需求。本文梳理了城市地表遥感温度从“方向温度”（2维）到“全表面温度”（2.5维）再到“3维表面温度”（3维）的发展脉络，以及目前利用遥感方向观测获取城市地表不同维度温度的进展；厘清了不同维度城市地表遥感温度的差异及其相互关系；阐述了不同维度城市地表遥感温度的应用。在总结存在的问题的基础上，指出了城市地表遥感温度的未来发展趋势：（1）面向应用的城市3维表面温度定义；（2）面向城市3维表面温度重建的立体化观测和（3）3维表面温度产品与城市气候模型的耦合。
Abstract
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. To better characterize the energy exchange between the urban underlying surface and the atmosphere, the term "complete surface temperature" was proposed to represent the characteristics of urban surface temperature. 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 summarizes 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), as well as the current progress in using remote sensing directional observations to obtain urban surface temperature in different dimensions; Clarifies the differences and interrelationships of in different dimensions; The application of remotely-sensed urban surface temperature in different dimensions was also elaborated. On the basis of summarizing the existing problems, the future development trend of remotely-sensed urban surface temperature is pointed out: (1) definition of 3-dimensional urban surface temperature for different application purposes; (2) Stereoscopic observation for the reconstruction of 3-dimensional urban surface temperature and (3) coupling of 3-dimensional surface temperature products and urban climate models.
关键词
城市遥感地表温度方向温度全表面温度3维表面温度
Keywords
urban remote sensingland surface temperaturedirectional temperaturecomplete surface temperature3-dimensional surface temperature
references
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. [DOI: 10.5194/amt-8-2699-2015http://dx.doi.org/10.5194/amt-8-2699-2015]
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), 6-8 March 2017.
Allen M A, Voogt J A, and Christen A. 2018. Time-Continuous Hemispherical Urban Surface Temperatures. remote sensing 10(1):3. [DOI: 10.3390/rs10010003http://dx.doi.org/10.3390/rs10010003]
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. [DOI: 10.1002/joc.859http://dx.doi.org/10.1002/joc.859]
Bruse M, and Fleer H. 1998. Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling & Software 13(3-4):373-384. [DOI: 10.1016/S1364-8152(98)00042-5http://dx.doi.org/10.1016/S1364-8152(98)00042-5]
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(08): 1710-1721.
曹彪, 杜永明, 卞尊健, 董亚冬, 赵峰, 胡添, 冉雪婷, 秦伯雄, 历华, 肖青, 柳钦火. 2021. 热红外核驱动模型在垄行植被热辐射方向性特征拟合中的精度分析. 遥感学报 25(08): 1710-1721 [DOI: 10.11834/jrs.20211300http://dx.doi.org/10.11834/jrs.20211300]
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. [DOI: 10.1016/j.rse.2019.111304http://dx.doi.org/10.1016/j.rse.2019.111304]
Cao B, Roujean J L, Gastellu-Etchegorry J P, Liu Q H, Du Y M, Lagouarde J P, Huange 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. [DOI: 10.1016/j.rse.2020.112157http://dx.doi.org/10.1016/j.rse.2020.112157]
Chatzidimitriou A, and Yannas S. 2015. Microclimate development in open urban spaces: The influence of form and materials. Energy and Buildings 108:156-174. [DOI: 10.1016/j.enbuild.2015.08.048http://dx.doi.org/10.1016/j.enbuild.2015.08.048]
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 033(006): 555-567
陈云浩, 吴佳桐, 王丹丹. 2018. 广义地表热辐射方向性计算机模拟研究进展. 地球科学进展 033(006): 555-567 [ DOI:CNKI:SUN:DXJZ.0.2018-06-004]
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. Sci Rep 8(1):11498. [DOI: 10.1038/s41598-018-29873-xhttp://dx.doi.org/10.1038/s41598-018-29873-x]
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. [DOI: 10.1016/j.rse.2013.09.003http://dx.doi.org/10.1016/j.rse.2013.09.003]
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. [DOI: 10.1016/j.rse.2012.04.016http://dx.doi.org/10.1016/j.rse.2012.04.016]
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. [DOI: 10.1016/j.rse.2016.08.012http://dx.doi.org/10.1016/j.rse.2016.08.012]
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. [DOI: 10.1016/j.rse.2016.12.008http://dx.doi.org/10.1016/j.rse.2016.12.008]
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. [DOI: 10.1016/j.rse.2018.02.066http://dx.doi.org/10.1016/j.rse.2018.02.066]
Fontanilles G, Briottet X, Fabre S, and Tremas T. 2008. Thermal infrared radiance simulation with aggregation modeling (TITAN): an infrared radiative transfer model for heterogeneous three-dimensional surface--application over urban areas. Appl Opt 47(31):5799-5810. [DOI: 10.1364/ao.47.005799http://dx.doi.org/10.1364/ao.47.005799]
Gastellu-Etchegorry J P, Yin T G, Lauret N, Cajgfinger T, Gregoire T, Grau E, Feret J B, Lopes M, Guilleux J, Dedieu G, Malenovsky 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. [DOI: 10.3390/rs70201667http://dx.doi.org/10.3390/rs70201667]
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. [DOI: 10.1109/Lgrs.2013.2260319http://dx.doi.org/10.1109/Lgrs.2013.2260319]
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 (12): 123-131
郭冠华, 陈颖彪, 魏建兵, 吴志峰, 容晓臻. 2012. 粒度变化对城市热岛空间格局分析的影响. 生态学报(12): 123-131. [ DOI:10.5846/stxb201107181068]
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. [DOI: 10.1016/j.buildenv.2011.02.001http://dx.doi.org/10.1016/j.buildenv.2011.02.001]
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. [DOI: 10.1007/s00704-012-0615-0http://dx.doi.org/10.1007/s00704-012-0615-0]
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. [DOI: 10.1007/s00704-020-03094-7http://dx.doi.org/10.1007/s00704-020-03094-7]
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. [DOI: 10.1016/j.rse.2016.03.043http://dx.doi.org/10.1016/j.rse.2016.03.043]
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. [DOI: 10.1016/j.isprsjprs.2018.12.004http://dx.doi.org/10.1016/j.isprsjprs.2018.12.004]
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. [DOI: 10.1016/j.isprsjprs.2019.04.010http://dx.doi.org/10.1016/j.isprsjprs.2019.04.010]
Jiang L, Zhan W, Hu L, Huang F, Hong F, Liu Z, Lai J, and Wang C. 2021. Assessment of different kernel-driven models for daytime urban thermal radiation directionality simulation. Remote Sensing of Environment 263:112562. [DOI: 10.1016/j.rse.2021.112562http://dx.doi.org/10.1016/j.rse.2021.112562]
Jiang L, Zhan W, Tu L, Dong P, Wang S, Li L, Wang C, and Wang C. 2022. Diurnal variations in directional brightness temperature over urban areas through a multi-angle UAV experiment. Building and Environment 222:109408. [DOI: 10.1016/j.buildenv.2022.109408http://dx.doi.org/10.1016/j.buildenv.2022.109408]
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. [DOI: 10.1016/j.buildenv.2018.03.005http://dx.doi.org/10.1016/j.buildenv.2018.03.005]
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. [DOI: 10.1175/Jam2500.1http://dx.doi.org/10.1175/Jam2500.1]
Kanda M, Kawai T, Kanega M, Moriwaki R, Narita K, and Hagishima A. 2005. A simple energy balance model for regular building arrays. Boundary-Layer Meteorology 116(3):423-443. [DOI: 10.1007/s10546-004-7956-xhttp://dx.doi.org/10.1007/s10546-004-7956-x]
Kanda M, Kawai T, and Nakagawa K. 2005. A Simple Theoretical Radiation Scheme for Regular Building Arrays. Boundary-Layer Meteorology 114(1):71-90. [DOI: 10.1007/s10546-004-8662-4http://dx.doi.org/10.1007/s10546-004-8662-4]
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. [DOI: 10.1007/s10546-013-9883-1http://dx.doi.org/10.1007/s10546-013-9883-1]
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. [DOI: 10.1007/s10546-006-9153-6http://dx.doi.org/10.1007/s10546-006-9153-6]
Krayenhoff E S, and Voogt J A. 2016. Daytime Thermal Anisotropy of Urban Neighbourhoods: Morphological Causation. remote sensing 8(2):108. [DOI: 10.3390/rs8020108http://dx.doi.org/10.3390/rs8020108]
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
匡文慧. 2018. 城市土地利用/覆盖变化与热环境生态调控研究进展与展望. 地理科学 38(10): 1643-1652 [ DOI:10.13249/j.cnki.sgs.2018.10.008]
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). Int J Biometeorol 66(4):833-848. [DOI: 10.1007/s00484-022-02241-1http://dx.doi.org/10.1007/s00484-022-02241-1]
Lagouarde J P, Henon 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. [DOI: 10.1016/j.rse.2011.06.022http://dx.doi.org/10.1016/j.rse.2011.06.022]
Lagouarde J P, Henon 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. [DOI: 10.1016/j.rse.2009.08.012http://dx.doi.org/10.1016/j.rse.2009.08.012]
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. [DOI: 10.1007/s00703-008-0325-4http://dx.doi.org/10.1007/s00703-008-0325-4]
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. [DOI: 10.1016/j.rse.2003.12.011http://dx.doi.org/10.1016/j.rse.2003.12.011]
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. [DOI: 10.1504/IJEP.2010.030887http://dx.doi.org/10.1504/IJEP.2010.030887]
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. [DOI: 10.1109/LGRS.2019.2962383http://dx.doi.org/10.1109/LGRS.2019.2962383]
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. [DOI: 10.1016/j.isprsjprs.2019.03.013http://dx.doi.org/10.1016/j.isprsjprs.2019.03.013]
Ma W, Chen Y H, Zhan W F, and J Z. 2013. Thermal anisotropy model for simulated three dimensional urban targets. Journal of Remote Sensing 17(1):62-76. [DOI: CNKI:SUN:YGXB.0.2013-01-006http://dx.doi.org/CNKI:SUN:YGXB.0.2013-01-006]
Masson V. 2000. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology 94(3):357-97. [DOI: 10.1023/A:1002463829265http://dx.doi.org/10.1023/A:1002463829265]
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, Lacherade 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. [DOI: 10.1007/s00703-008-0289-4http://dx.doi.org/10.1007/s00703-008-0289-4]
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. [DOI: 10.1016/j.rse.2009.08.002http://dx.doi.org/10.1016/j.rse.2009.08.002]
Meng Q Y. 2020. Remote Sensing of Urban Greenness Spatial. Beijing: Science Press
孟庆岩. 2020. 城市绿度空间遥感, 北京：科学出版社.
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. [DOI: 10.1016/j.rse.2017.09.019http://dx.doi.org/10.1016/j.rse.2017.09.019]
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. [DOI: 10.1016/j.rse.2018.05.004http://dx.doi.org/10.1016/j.rse.2018.05.004]
Nichol J E. 1998. Visualisation of urban surface temperatures derived from satellite images. International Journal of Remote Sensing 19(9):1639-1649. [DOI: 10.1080/014311698215153http://dx.doi.org/10.1080/014311698215153]
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. [DOI: 10.1016/0168-1923(95)02259-Zhttp://dx.doi.org/10.1016/0168-1923(95)02259-Z]
Oke T R. 1973. City size and the urban heat island. Atmospheric Environment (1967) 7(8):769-779. [DOI: 10.1016/0004-6981(73)90140-6http://dx.doi.org/10.1016/0004-6981(73)90140-6]
Peng Z X, Zhou J and Li M. 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
彭志兴, 周纪, 李明松. 2016. 基于地面观测的异质性下垫面像元尺度地表温度模拟研究进展. 地球科学进展 31(5): 471-480 [ DOI:10.11867/j.issn.1001-8166.2016.05.0471]
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. [DOI: 10.1007/s00703-008-0320-9http://dx.doi.org/10.1007/s00703-008-0320-9]
Poglio T, Mathieu-Marni S, Ranchin T, Savania 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. [DOI: 10.1016/j.rse.2006.03.017http://dx.doi.org/10.1016/j.rse.2006.03.017]
Roberts S M, Voogt J, and Oke T. 2009. Outdoor scale model experiment to evaluate the complete urban surface temperature. In Eighth Symposium on the Urban Environment. [2023-03-02]. https://ams.confex.com/ams/89annual/webprogram/Paper144424.htmlhttps://ams.confex.com/ams/89annual/webprogram/Paper144424.html.
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. [DOI: 10.1080/01431168908904002http://dx.doi.org/10.1080/01431168908904002]
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 97(D18):20455-20468. [DOI: 10.1029/92jd01411http://dx.doi.org/10.1029/92jd01411]
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. [DOI: 10.1023/B:BOUN.0000027978.21230.b7http://dx.doi.org/10.1023/B:BOUN.0000027978.21230.b7]
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 (IGARSS): 6709-6712. [DOI: 10.1109/Igarss.2012.6352059http://dx.doi.org/10.1109/Igarss.2012.6352059]
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
孙灏, 陈云浩, 占文凤, 王萌杰, 马伟. 2015. 一种近似用于高发射率城市地表热红外等效发射率的方向性变异核驱动模型及其不确定性分析. 红外与毫米波学报 34(1): 66-73 [ DOI:10.3724/SP.J.1010.2015.00066]
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). [DOI: 10.1029/2008jd010035http://dx.doi.org/10.1029/2008jd010035]
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. [DOI: 10.1016/j.scs.2018.08.009http://dx.doi.org/10.1016/j.scs.2018.08.009]
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). [DOI: 10.1029/2012gl054059http://dx.doi.org/10.1029/2012gl054059]
Voogt J. 2021. Urban heatwaves and thermal remote sensing. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 11-16 July 2021.
Voogt J A. 2000. Image representations of complete urban surface temperatures. Geocarto International 15(3):21-32. [DOI: 10.1080/10106040008542160http://dx.doi.org/10.1080/10106040008542160]
Voogt J A. 2008. Assessment of an Urban Sensor View Model for thermal anisotropy. Remote Sensing of Environment 112(2):482-495. [DOI: 10.1016/j.rse.2007.05.013http://dx.doi.org/10.1016/j.rse.2007.05.013]
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. [DOI: 10.1175/1520-0450-39.10.1679http://dx.doi.org/10.1175/1520-0450-39.10.1679]
Voogt J A, and Oke T R. 1997. Complete urban surface temperatures. Journal of Applied Meteorology 36(9):1117-1132. [DOI: 10.1175/1520-0450(1997)036<1117:Cust>2.0.Co;2http://dx.doi.org/10.1175/1520-0450(1997)036<1117:Cust>2.0.Co;2]
Voogt J A, and Oke T R. 1998. Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing 19(5):895-920. [DOI: 10.1080/014311698215784http://dx.doi.org/10.1080/014311698215784]
Voogt J A and Oke T R. 1998. Radiometric temperatures of urban canyon walls obtained from vehicle traverses. Theoretical and Applied Climatology 60(1-4): 199-217. [DOI: 10.1007/s007040050044http://dx.doi.org/10.1007/s007040050044]
Voogt J A, and Oke T R. 2003. Thermal remote sensing of urban climates. Remote Sensing of Environment 86(3):370-84. [DOI: 10.1016/s0034-4257(03)00079-8http://dx.doi.org/10.1016/s0034-4257(03)00079-8]
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. [DOI: 10.1109/tgrs.2019.2904871http://dx.doi.org/10.1109/tgrs.2019.2904871]
Wang D D, Chen Y H, Cui Y H, and Sun H. 2018. A Geometric Model to Simulate Urban Thermal Anisotropy for Simplified Neighborhoods. IEEE Transactions on Geoscience and Remote Sensing 56(8):4930-4944. [DOI: 10.1109/Tgrs.2018.2842794http://dx.doi.org/10.1109/Tgrs.2018.2842794]
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. [DOI: 10.1016/j.rse.2021.112361http://dx.doi.org/10.1016/j.rse.2021.112361]
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. [DOI: 10.1016/j.rse.2022.113312http://dx.doi.org/10.1016/j.rse.2022.113312]
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. [DOI: 10.1016/j.rse.2019.111547http://dx.doi.org/10.1016/j.rse.2019.111547]
Wang D D, Chen Y H, and Zhan W F. 2018. A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse). Remote Sensing of Environment 209:263-274. [DOI: 10.1016/j.rse.2018.02.051http://dx.doi.org/10.1016/j.rse.2018.02.051]
Wang X, Li Y, Wang K, Yang X, 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. [DOI: 10.1175/JAMC-D-17-0095.1http://dx.doi.org/10.1175/JAMC-D-17-0095.1]
Wei L T, Jiang X G, Wu H and Ru C. 2022. Review of Urban Thermal Radiation Anisotropy. Geo-InformationScience24(04): 617-630
韦乐田, 姜小光, 吴骅, 茹晨. 2022. 城市地表热辐射方向性研究进展综述. 地球信息科学学报 24(04): 617-630 [ DOI:10.12082/dqxxkx.2022.210422]
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. [DOI: 10.1016/j.rse.2017.09.033http://dx.doi.org/10.1016/j.rse.2017.09.033]
Xu H Q. 2011. Analysis on urban heat island effect based on the dynamics of urban surface biophysical descriptors. Acta Ecologica Sinica 031(014): 3890-3901
徐涵秋. 2011. 基于城市地表参数变化的城市热岛效应分析. 生态学报 031(014): 3890-3901 [ DOI:CNKI:SUN:STXB.0.2011-14-004]
Yang J, Menenti M, Wu Z, Wong M S, Abbas S, Xu Y, and Shi Q. 2020. Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures. International Journal of Climatology 41(S1). [DOI: 10.1002/joc.6919http://dx.doi.org/10.1002/joc.6919]
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. [DOI: 10.1016/j.rse.2019.111540http://dx.doi.org/10.1016/j.rse.2019.111540]
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. [DOI: 10.1177/0309133321988850http://dx.doi.org/10.1177/0309133321988850]
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. National Remote Sensing Bulletin 10(005): 661-669
余涛, 田启燕, 顾行发, 王进, 刘强, 阎广建. 2006. 城市简单目标方向亮温研究.遥感学报 10(005): 661-669 [DOI: 10.3321/j.issn:1007-4619.2006.05.009http://dx.doi.org/10.3321/j.issn:1007-4619.2006.05.009]
Zhan W F, Chen Y H, Ma W, Zhou J and Li J. 2012. FOV effect analysis in directional brightness temperature observations for urban targets. National Remote Sensing Bulletin 14(02):372-395
占文凤, 陈云浩, 马伟, 周纪, 李京. 2010. 城市目标方向亮温观测的视场效应分析. 遥感学报14(02):372-395 [DOI: 10.1016/j.buildenv.2012.05.005http://dx.doi.org/10.1016/j.buildenv.2012.05.005]
Zhan W F, Chen Y H, Voogt J, Zhou J, Wang J F, Liu W Y, and Ma W. 2012. Interpolating diurnal surface temperatures of an urban facet using sporadic thermal observations. Building and Environment 57:239-252. [DOI: 10.1016/j.buildenv.2012.05.005http://dx.doi.org/10.1016/j.buildenv.2012.05.005]
Zhan W F, Chen Y H, Voogt J A, Zhou J, Wang J F, Ma W, and Liu W Y. 2012. Assessment of thermal anisotropy on remote estimation of urban thermal inertia. Remote Sensing of Environment 123:12-24. [DOI: 10.1016/j.rse.2012.03.001http://dx.doi.org/10.1016/j.rse.2012.03.001]
Zhou J, Chen Y H, Li J, Ma W and Zhan W F. 2009. Progress in Thermal Anisotropy of Urban Areas: A Review. Progress in Geography 24(5): 497
周纪, 陈云浩, 李京, 马伟, 占文凤. 2009. 城市区域热辐射方向性研究进展. 地球科学进展 24(5): 497 [ DOI:10.3321/j.issn:1001-8166.2009.05.005]
Zhou W Q and Tian Y Y. 2020. Effects of urban three-dimensional morphology on thermal environment: a review. Acta Ecologica Sinica 040(002): 416-427
周伟奇, 田韫钰. 2020. 城市3维空间形态的热环境效应研究进展. 生态学报 040(002): 416-427. [ DOI:10.5846/stxb201902250353]
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
School of Geography and Information Engineering,China University of Geosciences
Artificial Intelligence School, Wuchang University of Technology
Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belvaux
Anhui Key Laboratory of Wetland Ecological Protection and Restoration, Anhui University
Anhui Provincial Laboratory of Information Materials and Intelligent Perception
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP