城市地表热辐射方向性研究进展综述

doi:10.12082/dqxxkx.2022.210422

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 617-630.doi: 10.12082/dqxxkx.2022.210422

城市地表热辐射方向性研究进展综述

韦乐田¹(), 姜小光¹^,²^,^*(), 吴骅³, 茹晨¹

1.中国科学院大学资源与环境学院,北京 100049
2.中国科学院空天信息创新研究院定量遥感信息技术院重点实验室,北京 100094
3.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101

收稿日期:2021-07-23 修回日期:2021-09-28 出版日期:2022-04-25 发布日期:2022-06-25
通讯作者: ^*姜小光（1960— ）,男,北京人,教授,博士生导师,主要从事定量遥感、地表真实性检验等研究。 E-mail: xgjiang@ucas.ac.cn
作者简介:韦乐田（1996— ）,女,浙江杭州人,硕士生,主要从事热红外定量遥感研究。E-mail: weiletian19@mails.ucas.ac.cn
基金资助:
国家自然科学基金项目(41971319)

Review of Urban Thermal Radiation Anisotropy

WEI Letian¹(), JIANG Xiaoguang¹^,²^,^*(), WU Hua³, RU Chen¹

1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2. Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received:2021-07-23 Revised:2021-09-28 Online:2022-04-25 Published:2022-06-25
Contact: JIANG Xiaoguang
Supported by:
National Natural Science Foundation of China(41971319)

摘要/Abstract

摘要：

热辐射方向性是指从不同方向观测同一目标地物时,测得的热辐射值各异的现象,通常体现为方向性辐亮度或亮温各异。随着高空间分辨率遥感数据的出现,面对高精度地表温度产品的需求,热辐射方向性效应不可忽视,如今已成为热红外遥感重点关注的问题之一。对于物质组成不均一、几何结构复杂的城市地表来说,热辐射方向性尤为显著。本文整理、分析了在城市地区开展的一系列热辐射方向性观测试验和正演模型,其中也包括一些对地表温度真值的有益探索;并对城市热辐射方向性强度的影响因素进行了归纳,包括观测季节与时间、地表几何结构、材料自身的物理属性、观测角度、视场角等,这些因素会使热辐射方向性的强度呈现出一定的时空规律性。最后,针对提高城市地表温度的反演精度、如何更好地开展城市热辐射方向性研究提出了5点展望。

关键词: 城市地表温度, 热辐射方向性, 完全表面温度, 方向亮温, 多角度观测, 几何三维模型, 辐射传输模型, 参数模型

Abstract:

Thermal radiation directionality refers to the phenomenon that thermal radiation values measured from different observation directions are different for a certain surface object, which is usually reflected in different directional radiance or different brightness temperature. With the emergence of high spatial resolution remotely sensed data and the demand for high-precision surface temperature products, the effect of thermal radiation anisotropy cannot be ignored. Now it has become one of the hottest issues concerned widely in the thermal infrared field. The thermal anisotropy is more obvious for the urban surface with diverse surface features and complex geometric structure. This article describes three observational experiments, including ground observation experiment, airborne observation experiment, and space observation experiment. These three methods have their own advantages and disadvantages and can be used in different situations. The observation data that represents the reality of urban radiation directionality often shows obvious thermal radiation anisotropy in urban areas during the daytime. In addition, a series of forward models of thermal radiation anisotropy carried out in urban areas are categorized and analyzed. These models can be divided into three categories: geometric three-dimensional model, radiative transfer model, and parameter model. According to existing academic papers, in-situ observation data are usually used to estimate the coefficients and verify the simulation accuracy of forward models. By combining these two approaches, observations and models, some scholars have made some achievements in this field. The purpose of studying thermal radiation anisotropy in urban areas is to obtain land surface parameters with higher accuracy. So, the exploration of true values of urban surface temperature are also included in this study. Furthermore, the impact factors of thermal radiation anisotropy are summarized, such as observation season and time, surface geometry, physical properties of surface materials, observation angle, FOV of sensor, etc. which influence the spatial and temporal patterns of intensity of thermal radiation anisotropy. At last, for the ultimate goal of improving the retrieval accuracy of urban surface temperature, five prospects are put forward: using high-resolution thermal infrared sensors to get the data of urban thermal background field, carrying out more thermal infrared multi-angle remote sensing experiments from different platforms, improving understanding of the mechanism of thermal radiation of non-isothermal heterogeneous pixels, performing validation of urban surface temperature, applying the research results into practice such as angle correction of satellite temperature products.

Key words: urban land surface temperature, thermal radiation anisotropy, complete surface temperature, directional brightness temperature, multi-angle observation, geometric three-dimensional model, radiative transfer model, parameter model

韦乐田, 姜小光, 吴骅, 茹晨. 城市地表热辐射方向性研究进展综述[J]. 地球信息科学学报, 2022, 24(4): 617-630.DOI:10.12082/dqxxkx.2022.210422

WEI Letian, JIANG Xiaoguang, WU Hua, RU Chen. Review of Urban Thermal Radiation Anisotropy[J]. Journal of Geo-information Science, 2022, 24(4): 617-630.DOI:10.12082/dqxxkx.2022.210422

图/表 6

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表1

常见的热辐射方向性模型

模型		假设	优点	不足	适用场景
几何三维模型	GUTA-sparse^[39]	建筑等高、方向随机分布	将核驱动形式引入模型,线性结构使之更为灵活	至少需要4个方向的观测数据	建筑高距比小于1的稀疏城市表面
	GUTA-osg^[40]	建筑等高、方向随机分布	运用布尔模型解决建筑物阴影重叠的情况	没有考虑多重散射和垂直墙面上的阴影	建筑高距比小于2的稀疏城市表面
	GUTA-dense^[41]	建筑等高、方向随机分布	运用布尔模型解决建筑物阴影重叠的情况	没有考虑多重散射和发射率的方向性	建筑高距比大于2时,需结合GUTA-osg和GUTA-dense模拟
	SUM^[23]	建筑等高且屋顶均水平	可以计算任一表面在观测视场角中所占的面积比例和光照情况	需要输入地表组分的温度和观测角度等信息,使其应用大大受限	简化的城市地表
	SUM_VEG^[42]	阴影叶片具有相同程度的阴影,接收的辐射值相同	使用光线追踪法和间隙概率法来估计叶片在视场中的辐射贡献	需要输入地表组分的温度和植被的生物物理属性等大量信息	植被冠层和简化的城市地表
辐射传输模型	SOLENE^[43,44]	对流热传递系数是恒定的;建筑内部温度是恒定的	可用于计算几何形态较复杂的城市热辐射	计算时间长,验证难度大	晴朗无云的大气环境,适合微小尺度的热辐射分析
	TUF-3D^[47]	表面由平行的平面组成;能被细分为相同的方形斑块	相较于其它辐射传输模型,模型输入较少	对建筑的几何形态过于理想化;对流模拟过程较粗糙	可模拟复杂的城市地表
	TITAN^[49]	建筑垂直于地面且等高;表面是朗伯体	可以得到总信号的所有辐射贡献	需要输入几何场景和大气等大量信息	简化的城市地表
	DART^[50,51]	将体素作为辐射相互作用的存储单元	可以直接模拟大气辐射传输;用户界面操作方便;维护更新及时	输入参数多,计算时间长	可模拟复杂的城市地表
	ATIMOU^[52]	建筑垂直于地面且等高;表面是朗伯体平面	能够给出LST的解析解,对于反演高精度的城市LST是一个有益的尝试	建筑对称等高、街道无限长等假设限制了其实际应用	简化的城市地表
参数模型	RL^[17]	同一水平冠层的散射体之间没有相互遮挡	关注热点效应	不考虑阴影效应;在夜间的拟合效果不佳;非线性形式限制其广泛运用
	Vinnikov 三核驱动模型^[36]	模型由各向同性核、发射率核和太阳核构成	低LAI的植被冠层的模拟效果好	模拟热点效应能力弱
	TIR-BRDF模型^[54,55]	冠层由叶片、光照土壤和阴影土壤组成	观测天顶角小于45°时,TIR-BRDF的模拟效果好	至少需要4个方向的观测数据
	GUTA-T^[65]	建筑等高、方向随机分布	太阳天顶角约45°时,模拟效果好	仅适用于太阳天顶角30~50°的情况;没有考虑热滞后效应

表1

表2

参考文献 82

[1]	李召良, 段四波, 唐伯惠, 等. 热红外地表温度遥感反演方法研究进展[J]. 遥感学报, 2016,20(5):899-920.
	[ Li Z L, Duan S B, Tang B H, et al. Review of methods for land surface temperature derived from thermal infrared remotely sensed data[J]. Journal of Remote Sensing, 2016,20(5):899-920. ] DOI: 10.11834/jrs.20166192 doi: 10.11834/jrs.20166192
[2]	毛克彪, 杨军, 韩秀珍, 等. 基于深度动态学习神经网络和辐射传输模型地表温度反演算法研究[J]. 中国农业信息, 2018,30(5):47-57.
	[ Mao K B, Yang J, Han X Z, et al. Retrieving land surface temperature based on deep dynamic learning NN algorithm and radiation transmission model[J]. China Agriculture Information, 2018,30(5):47-57. ] DOI: 10.12105/j.issn.1672-0423.20180506 doi: 10.12105/j.issn.1672-0423.20180506
[3]	阎广建, 李小文, 王锦地, 等. 宽波段热红外方向性辐射建模[J]. 遥感学报, 2000,4(3):189-193.
	[ Yan G J, Li X W, Wang J D, et al. Modeling directional effects of thermal emission in wide band measurements[J]. Journal of Remote Sensing, 2000,4(3):189-193. ] DOI: 10.11834/jrs.20000305 doi: 10.11834/jrs.20000305
[4]	徐希孺, 范闻捷, 陈良富. 开放的复杂目标热辐射特性的矩阵表达式[J]. 中国科学(D辑:地球科学), 2001,31(12):1046-1051.
	[ Xu X R, Fan W J, Chen L F. Matrix expression of thermal radiation characteristics of open complex targets[J]. Science in China(Series D), 2001,31(12):1046-1051. ] DOI: 10.3969/j.issn.1674-7240.2001.12.011 doi: 10.3969/j.issn.1674-7240.2001.12.011
[5]	Lagouarde J P, Dayau S, Moreau P, et al. Directional anisotropy of brightness surface temperature over vineyards: Case study over the medoc region (SW France)[J]. IEEE Geoscience & Remote Sensing Letters, 2013,11(2):574-578. DOI: 10.1109/LGRS.2013.2282492 doi: 10.1109/LGRS.2013.2282492
[6]	马伟, 陈云浩, 占文凤, 等. 城市模拟目标的3维热辐射方向性模型[J]. 遥感学报, 2013,17(1):62-76.
	[ Ma W, Chen Y H, Zhan W F, et al. Thermal anisotropy model for simulated three dimensional urban targets[J]. Journal of Remote Sensing, 2013,17(1):62-76. ] DOI: 10.11834/jrs.20131361 doi: 10.11834/jrs.20131361
[7]	Monteith J L, Szeicz G. Radiative temperature in the heat balance of natural surfaces[J]. Quarterly Journal of the Royal Meteorological Society, 1962,88(378):496-507. DOI: 10.1002/qj.49708837811 doi: 10.1002/qj.49708837811
[8]	Voogt J A, Oke T R. Thermal remote sensing of urban climates[J]. Remote Sensing of Environment, 2003,86(3):370-384. DOI: 10.1016/S0034-4257(03)00079-8 doi: 10.1016/S0034-4257(03)00079-8
[9]	周纪, 陈云浩, 李京, 等. 城市区域热辐射方向性研究进展[J]. 地球科学进展, 2009,24(5):497-505.
	[ Zhou J, Chen Y H, Li J, et al. Progress in thermal anisotropy of urban areas: A Review[J]. Advances in Earth Science, 2009,24(5):497-505.] DOI: 10.3321/j.issn:1001-8166.2009.05.005 doi: 10.3321/j.issn:1001-8166.2009.05.005
[10]	Chen S S, Ren H Z, Ye X, et al. Geometry and adjacency effects in urban land surface temperature retrieval from high-spatial-resolution thermal infrared images[J]. Remote Sensing of Environment, 2021,262:112518. DOI: 10.1016/j.rse.2021.112518 doi: 10.1016/j.rse.2021.112518
[11]	Cao B, Liu Q H, Du Y M, et al. A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives[J]. Remote Sensing of Environment, 2019,232:111304. DOI: 10.1016/j.rse.2019.111304 doi: 10.1016/j.rse.2019.111304
[12]	Lagouarde J P, Hénon A, Kurz B, et al. Modelling daytime thermal infrared directional anisotropy over Toulouse city centre[J]. Remote Sensing of Environment, 2010,114(1):87-105. DOI: 10.1016/j.rse.2009.08.012 doi: 10.1016/j.rse.2009.08.012
[13]	Bian Z J, Roujean J L, Cao B, et al. Modeling the directional anisotropy of fine-scale TIR emissions over tree and crop canopies based on UAV measurements[J]. Remote Sensing of Environment, 2021,252:112150. DOI: 10.1016/j.rse.2020.112150 doi: 10.1016/j.rse.2020.112150
[14]	Voogt J A, Oke T R. Complete urban surface temperatures[J]. Journal of Applied Meteorology, 1997,36(9):1117-1132. DOI: 10.1175/1520-0450(1997)036<1117:CUST>2.0.CO;2 doi: 10.1175/1520-0450(1997)036<1117:CUST>2.0.CO
[15]	Hu L Q, Monaghan A, Voogt J A, et al. A first satellite-based observational assessment of urban thermal anisotropy[J]. Remote Sensing of Environment, 2016,181:111-121. DOI: 10.1016/j.rse.2016.03.043 doi: 10.1016/j.rse.2016.03.043
[16]	Adderley C, Christen A, Voogt J A. The effect of radiometer placement and view on inferred directional and hemispheric radiometric temperatures of an urban canopy[J]. Atmospheric Measurement Techniques Discussions, 2015,8(7):2699-2714. DOI: 10.5194/amt-8-2699-2015 doi: 10.5194/amt-8-2699-2015
[17]	Lagouarde J P, Irvine M. Directional anisotropy in thermal infrared measurements over Toulouse city centre during the CAPITOUL measurement campaigns: first results[J]. Meteorology & Atmospheric Physics, 2008,102(3-4):173-185. DOI: 10.1007/s00703-008-0325-4 doi: 10.1007/s00703-008-0325-4
[18]	Masson V, Gomes L, Pigeon G, et al. The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment[J]. Meteorology and Atmospheric Physics, 2008,102(3-4):135-157. DOI: 10.1007/s00703-008-0289-4 doi: 10.1007/s00703-008-0289-4
[19]	Duffour C, Lagouarde J P, Roujean J L. A two parameter model to simulate thermal infrared directional effects for remote sensing applications[J]. Remote Sensing of Environment, 2016,186:250-261. DOI: 10.1016/j.rse.2016.08.012 doi: 10.1016/j.rse.2016.08.012
[20]	Wang D D, Chen Y H, Hu L Q, et al. Modeling the angular effect of MODIS LST in urban areas: A case study of Toulouse, France[J]. Remote Sensing of Environment, 2021,257:112361. DOI: 10.1016/j.rse.2021.112361 doi: 10.1016/j.rse.2021.112361
[21]	Sugawara H, Takamura T. Longwave radiation flux from an urban canopy: evaluation via measurements of directional radiometric temperature[J]. Remote Sensing of Environment, 2006,104(2):226-237. DOI: 10.1016/j.rse.2006.01.024 doi: 10.1016/j.rse.2006.01.024
[22]	Morrison W, Kotthaus S, Grimmond C S B, et al. A novel method to obtain three-dimensional urban surface temperature from ground-based thermography[J]. Remote Sensing and Environment, 2018,215:268-283. DOI: 10.1016/j.rse.2018.05.004 doi: 10.1016/j.rse.2018.05.004
[23]	Soux A, Voogt J A, Oke T R. A model to calculate what a remote sensor 'sees' of an urban surface[J]. Boundary-Layer Meteorology, 2004,111(1):109-132. DOI: 10.1023/B:BOUN.0000010995.62115.46 doi: 10.1023/B:BOUN.0000010995.62115.46
[24]	占文凤, 陈云浩, 马伟, 等. 城市目标方向亮温观测的视场效应分析[J]. 遥感学报, 2010,14(2):372-386.
	[ Zhan W F, Chen Y H, Ma W, et al. FOV effect analysis in directional brightness temperature observations for urban targets[J]. Journal of Remote Sensing, 2010,14(2):372-386. ] DOI: 10.11834/jrs.20100213 doi: 10.11834/jrs.20100213
[25]	Voogt J A, Oke T R. Effects of urban surface geometry on remotely-sensed surface temperature[J]. International Journal of Remote Sensing, 1998,19(5):895-920. DOI: 10.1080/014311698215784 doi: 10.1080/014311698215784
[26]	Sugawara H, Narita K I, Mikami T. Estimation of effective thermal property parameter on a heterogeneous urban surface[J]. Journal of the Meteorological Society of Japan, 2002,79(6):1169-1181. DOI: 10.2151/jmsj.79.1169 doi: 10.2151/jmsj.79.1169
[27]	Lagouarde J P, Moreau P, Irvine M, et al. Airborne experimental measurements of the angular variations in surface temperature over urban areas: case study of Marseille (France)[J]. Remote Sensing of Environment, 2004,93(4):443-462. DOI: 10.1016/j.rse.2003.12.011 doi: 10.1016/j.rse.2003.12.011
[28]	Lagouarde J P, Hénon A, Irvine M, et al. Experimental characterization and modelling of the nighttime directional anisotropy of thermal infrared measurements over an urban area: Case study of Toulouse (France)[J]. Remote Sensing of Environment, 2012,117(3-4):19-33. DOI: 10.1016/j.rse.2011.06.022 doi: 10.1016/j.rse.2011.06.022
[29]	Lagouarde J P, Ommandoire D C, Irvine M, et al. Atmospheric boundary-layer turbulence induced surface temperature fluctuations. Implications for TIR remote sensing measurements[J]. Remote Sensing of Environment, 2013,138(2):189-198. DOI: 10.1016/j.rse.2013.06.011 doi: 10.1016/j.rse.2013.06.011
[30]	余涛, 田启燕, 顾行发, 等. 城市简单目标方向亮温研究[J]. 遥感学报, 2006,10(5):661-669.
	[ Yu T, Tian Q Y, Gu X F, et al. Modelling directional brightness temperature over a simple typical structure of urban areas[J]. Journal of Remote Sensing, 2006,10(5):661-669. ]
[31]	刘强, 肖青, 刘志刚, 等. 黑河综合遥感联合试验中机载WiDAS数据的预处理方法[J]. 遥感技术与应用, 2010,25(6):797-804.
	[ Liu Q, Xiao Q, Liu Z G, et al. Image processing method of airborne WiDAS sensor in WATER Campaign[J]. Remote Sensing Technology and Application, 2010,25(6):797-804. ] DOI: 10.11873/j.issn.1004-0323.2010.6.797 doi: 10.11873/j.issn.1004-0323.2010.6.797
[32]	Yang J X, Wong M S, Ho H C, et al. A semi-empirical method for estimating complete surface temperature from radiometric surface temperature, a study in Hong Kong city[J]. Remote Sensing of Environment, 2020,237:111540. DOI: 10.1016/j.rse.2019.111540 doi: 10.1016/j.rse.2019.111540
[33]	Yang J X, Menenti M, Wu Z F, et al. Assessing the impact of urban geometry on surface urban heat island using complete and nadir temperatures[J]. International Journal of Climatology, 2020,41(1):E3219-E3238. DOI: 10.1002/joc.6919 doi: 10.1002/joc.6919
[34]	李召良, Stoll M P, 张仁华. 利用ATSR数据分解土壤和植被温度的研究[J]. 中国科学E辑:技术科学, 2000,30(S1):27-38.
	[ Li Z L, Stoll M P, Zhang R H. Decomposition of soil and vegetation temperature using ATSR data[J]. Science in China(Series E), 2000,30(S1):27-38. ] DOI: 10.3321/j.issn:1006-9275.2000.z1.005 doi: 10.3321/j.issn:1006-9275.2000.z1.005
[35]	阎广建, 姜海兰, 闫凯, 等. 多角度光学定量遥感[J]. 遥感学报, 2021,25(1):83-108.
	[ Yan G J, Jiang H L, Yan K, et al. Review of optical multi-angle quantitative remote sensing[J]. Journal of Remote Sensing, 2021,25(1):83-108. ] DOI: 10.11834/jrs.20218355 doi: 10.11834/jrs.20218355
[36]	Vinnikov K Y, Yu Y, Goldberg M D, et al. Angular anisotropy of satellite observations of land surface temperature[J]. Geophysical Research Letters, 2012,39(23). DOI: 10.1029/2012GL054059 doi: 10.1029/2012GL054059
[37]	Jackson R D, Reginato R J, Pinter P J, et al. Plant canopy information extraction from composite scene reflectance of row crops[J]. Applied Optics, 1979,18(22):3775-3782. DOI: 10.1364/AO.18.003775 doi: 10.1364/AO.18.003775 pmid: 20216693
[38]	Kimes D S, Kirchner J A. Directional radiometric measurements of row-crop temperatures[J]. International Journal of Remote Sensing, 1983,4(2):299-311. DOI: 10.1080/01431168308948548 doi: 10.1080/01431168308948548
[39]	Wang D D, Chen Y H, Zhan W F. A geometric model to simulate thermal anisotropy over a sparse urban surface (GUTA-sparse)[J]. Remote Sensing of Environment, 2018,209:263-274. DOI: 10.1016/j.rse.2018.02.051 doi: 10.1016/j.rse.2018.02.051
[40]	Wang D D, Chen Y H, Cui Y H, et al. A geometric model to simulate urban thermal anisotropy for simplified neighborhoods[J]. IEEE Transactions on Geoscience & Remote Sensing, 2018,56(8):4930-4944. DOI: 10.1109/TGRS.2018.2842794 doi: 10.1109/TGRS.2018.2842794
[41]	Wang D D, Chen Y H. A geometric model to simulate urban thermal anisotropy in simplified dense neighborhoods (GUTA-dense)[J]. IEEE Transactions on Geoscience & Remote Sensing, 2019,57(8):6226-6239. DOI: 10.1109/TGRS.2019.2904871 doi: 10.1109/TGRS.2019.2904871
[42]	Dyce D R, Voogt J A. The influence of tree crowns on urban thermal effective anisotropy[J]. Urban Climate, 2018,23(SI):91-113. DOI: 10.1016/j.uclim.2017.02.006 doi: 10.1016/j.uclim.2017.02.006
[43]	Groleau D, Ragnaud F F, Rosant J M. Simulation of the radiative behaviour of an urban quarter of marseille with the solene model[C]. Fifth International Conference on Urban Climate, Lodz, Pologne, 2003.
[44]	Henon A, Mestayer P G, Groleau D, et al. High resolution thermo-radiative modeling of an urban fragment in Marseilles city center during the UBL-ESCOMPTE campaign[J]. Building & Environment, 2011,46(9):1747-1764. DOI: 10.1016/j.buildenv.2011.02.001 doi: 10.1016/j.buildenv.2011.02.001
[45]	Kanda M, Kawai T, Nakagawa K. A simple theoretical radiation scheme for regular building arrays[J]. Boundary-Layer Meteorology, 2005,114(1):71-90. DOI: 10.1007/s10546-004-8662-4 doi: 10.1007/s10546-004-8662-4
[46]	Kawai T, Kanda M. 3-Dimensional radiation model for urban canopy[J]. Proceedings of Hydraulic Engineering, 2003,47:55-60. DOI: 10.2208/prohe.47.55 doi: 10.2208/prohe.47.55
[47]	Krayenhoff E S, Voogt J A. A microscale three-dimensional urban energy balance model for studying surface temperatures[J]. Boundary-Layer Meteorology, 2007,123(3):433-461. DOI: 10.1007/s10546-006-9153-6 doi: 10.1007/s10546-006-9153-6
[48]	Krayenhoff E S, Voogt J A. Daytime thermal anisotropy of urban neighbourhoods: Morphological causation[J]. Remote Sensing, 2016,8(2):1-22. DOI: 10.3390/rs8020108 doi: 10.3390/rs8020108
[49]	Fontanilles G, Briottet X, Fabre S, et al. Thermal Infrared Radiance Simulation with Aggregation Modeling (TITAN): An Infrared Radiative Transfer Model for Heterogeneous Three-dimensional Surface--Application over Urban Areas[J]. Applied Optics, 2008,47(31):5799-810. DOI: 10.1364/AO.47.005799 doi: 10.1364/AO.47.005799
[50]	Gastellu-Etchegorry J P. 3D Modeling of satellite spectral images, radiation budget and energy budget of urban landscapes[J]. Meteorology & Atmospheric Physics, 2008,102(3-4SI):187-207. DOI: 10.1007/s00703-008-0344-1 doi: 10.1007/s00703-008-0344-1
[51]	Gastellu-Etchegorry J P, Yin T G, Nicolas L, et al. Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes[J]. Remote Sensing, 2015,7(2):1667-1701. DOI: 10.3390/rs70201667 doi: 10.3390/rs70201667
[52]	Zheng X, Gao M, Li Z, et al. Impact of 3-D Structures and Their Radiation on Thermal Infrared Measurements in Urban Areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020,58(12). DOI: 10.1109/TGRS.2020.2987880 doi: 10.1109/TGRS.2020.2987880
[53]	Cao B, Roujean J L, Gastellu-Etchegorry J P, et al. A general framework of kernel-driven modeling in the thermal infrared domain[J]. Remote Sensing of Environment, 2021,252:112157. DOI: 10.1016/j.rse.2020.112157 doi: 10.1016/j.rse.2020.112157
[54]	彭菁菁, 刘强, 柳钦火, 等. 多角度热红外亮温值的模型拟合与应用[J]. 红外与毫米波学报, 2011,30(4):361-365,371.
	[ Peng J J, Liu Q, Liu Q H, et al. Kernel-driven model fitting of multi-angle thermal infrared brightness temperature and its application[J]. Journal of Infrared and Millimeter Waves, 2011,30(4):361-365,371. ]
[55]	Ren H Z, Liu R Y, Yan G J, et al. Angular normalization of land surface temperature and emissivity using multiangular middle and thermal infrared data[J]. IEEE Transactions on Geoscience & Remote Sensing, 2014,52(8):4913-4931. DOI: 10.1109/TGRS.2013.2285924 doi: 10.1109/TGRS.2013.2285924
[56]	Ren H Z, Yan G J, Liu R Y, et al. Determination of optimum viewing angles for the angular normalization of land surface temperature over vegetated surface[J]. Sensors, 2015,15(4):7537-7570. DOI: 10.3390/s150407537 doi: 10.3390/s150407537
[57]	Jiang L, Zhan W F, Voogt J A, et al. Remote estimation of complete urban surface temperature using only directional radiometric temperatures[J]. Building & Environment, 2018,135(MAY):224-236. DOI: 10.1016/j.buildenv.2018.03.005 doi: 10.1016/j.buildenv.2018.03.005
[58]	Jiang L, Zhan W F, Zou Z X. Two methods for remote estimation of complete urban surface temperature[C]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Wuhan, China, 2017. DOI: 10.5194/isprs-archives-XLII-2-W7-479-2017 doi: 10.5194/isprs-archives-XLII-2-W7-479-2017
[59]	Jiang L, Zhan W F, Hu L Q, et al. Assessment of different kernel-driven models for daytime urban thermal radiation directionality simulation[J]. Remote Sensing of Environment, 2021,263:112562. DOI: 10.1016/j.rse.2021.112562 doi: 10.1016/j.rse.2021.112562
[60]	Liu X Y, Tang B H, Li Z L. Evaluation of three parametric models for estimating directional thermal radiation from simulation, airborne, and satellite data[J]. Remote Sensing, 2018,10(3):420. DOI: 10.3390/rs10030420 doi: 10.3390/rs10030420
[61]	Yang J X, Wong M S, Menenti M, et al. Study of the geometry effect on land surface temperature retrieval in urban environment[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,109:77-87. DOI: 10.1016/j.isprsjprs.2015.09.001 doi: 10.1016/j.isprsjprs.2015.09.001
[62]	Yang J X, Wong M S, Menenti M, et al. Modeling the effective emissivity of the urban canopy using sky view factor[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,105:211-219. DOI: 10.1016/j.isprsjprs.2015.04.006 doi: 10.1016/j.isprsjprs.2015.04.006
[63]	Yang J X, Wong M S, Menenti M, et al. Development of an improved urban emissivity model based on sky view factor for retrieving effective emissivity and surface temperature over urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,122:30-40. DOI: 10.1016/j.isprsjprs.2016.09.007 doi: 10.1016/j.isprsjprs.2016.09.007
[64]	Yang J X, Wong M S, Menenti M. Effects of urban geometry on turbulent fluxes: A remote sensing perspective[J]. IEEE Geoscience and Remote Sensing Letters, 2016,13(12):1767-1771. DOI: 10.1109/LGRS.2016.2607759 doi: 10.1109/LGRS.2016.2607759
[65]	Wang D D, Chen Y H, Voogt J A, et al. An advanced geometric model to simulate thermal anisotropy time-series for simplified urban neighborhoods (GUTA-T)[J]. Remote Sensing of Environment, 2020,237:111547. DOI: 10.1016/j.rse.2019.111547 doi: 10.1016/j.rse.2019.111547
[66]	符锴华. 基于3-D模拟的典型植被热辐射方向性研究[D]. 北京:中国科学院研究生院(遥感应用研究所), 2005.
	[ Fu K H. The study on thermal infrared emission directionality of typical soil-vegetation canopy based on 3-D structure simulation model[D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2005. ]
[67]	Coll C, Galve J M, Niclòs R, et al. Angular variations of brightness surface temperatures derived from dual-view measurements of the Advanced Along-Track Scanning Radiometer using a new single band atmospheric correction method[J]. Remote Sensing of Environment, 2019,223:274-290. DOI: 10.1016/j.rse.2019.01.021 doi: 10.1016/j.rse.2019.01.021
[68]	Voogt J A. Assessment of an urban sensor view model for thermal anisotropy[J]. Remote Sensing of Environment, 2008,112(2):482-495. DOI: 10.1016/j.rse.2007.05.013 doi: 10.1016/j.rse.2007.05.013
[69]	Hu L Q, Wendel J. Analysis of urban surface morphologic effects on diurnal thermal directional anisotropy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019,148:1-12. DOI: 10.1016/j.isprsjprs.2018.12.004 doi: 10.1016/j.isprsjprs.2018.12.004
[70]	Pasha Y S, Fshari A A. Assessment and improvement of the accuracy of radiation heat transfer estimation in simplified urban canopy models[J]. Energy Procedia, 2017,143:532-539. DOI: 10.1016/j.egypro.2017.12.722 doi: 10.1016/j.egypro.2017.12.722
[71]	Zheng X P, Li Z L, Chen K S, et al. Assessment of the urban three-dimensional structural influence on the satellite thermal infrared measurement[C]. 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring), Rome, Italy, 2019. DOI: 10.1109/PIERS-Spring46901.2019.9017315 doi: 10.1109/PIERS-Spring46901.2019.9017315
[72]	Forouzandeh A, Richter T. Accurate prediction of heating energy demand of courtyard's surrounding envelopes using temperature correction factor[J]. Energy and Buildings, 2019,193:49-68. DOI: 10.1016/j.enbuild.2019.03.030 doi: 10.1016/j.enbuild.2019.03.030
[73]	周伟奇, 田韫钰. 城市三维空间形态的热环境效应研究进展[J]. 生态学报, 2020,40(2):416-427.
	[ Zhou W Q, Tian Y Y. Effects of urban three-dimensional morphology on thermal environment: a review[J]. Acta Ecologica Sinica, 2020,40(2):416-427. ] DOI: 10.5846/stxb201902250353 doi: 10.5846/stxb201902250353
[74]	Hilland R, Voogt J A. The effect of sub-facet scale surface structure on wall brightness temperatures at multiple scales[J]. Theoretical and Applied Climatology, 2020,140(14):767-785. DOI: 10.1007/s00704-020-03094-7 doi: 10.1007/s00704-020-03094-7
[75]	Minnis P, Khaiyer M M. Anisotropy of land surface skin temperature derived from satellite data[J]. Journal of Applied Meteorology, 2000,39(7):1117-1129. DOI: 10.1175/1520-0450(2000)039<1117:AOLSST>2.0.CO;2 doi: 10.1175/1520-0450(2000)039<1117:AOLSST>2.0.CO
[76]	Pinheiro A C T, Privette J L, Mahoney R, et al. Directional effects in a daily AVHRR land surface temperature dataset over africa[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(9):1941-1954. DOI: 10.1109/TGRS.2004.831886 doi: 10.1109/TGRS.2004.831886
[77]	杨英宝, 苏伟忠, 江南. 基于遥感的城市热岛效应研究[J]. 地理与地理信息科学, 2006,22(5):36-40.
	[ Yang Y B, Su W Z, Jiang N. Application of remote sensing to study urban heat island effect[J]. Geography and Geo-Information Science, 2006,22(5):36-40. ] DOI: 10.3969/j.issn.1672-0504.2006.05.007 doi: 10.3969/j.issn.1672-0504.2006.05.007
[78]	Li N N, Li X M. The impact of building thermal anisotropy on surface urban heat island intensity estimation: An observational case study in Beijing[J]. IEEE Geoscience and Remote Sensing Letters, 2020,17(12):2030-2034. DOI: 10.1109/LGRS.2019.2962383 doi: 10.1109/LGRS.2019.2962383
[79]	赵艳华, 赵利民. 红外多角度观测的优势分析[J]. 航天返回与遥感, 2017,38(1):30-37.
	[ Zhao Y H, Zhao L M. Analysis of advantages of multi-angle infrared observation[J]. Spacecraft Recovery & Remote Sensing, 2017,38(1):30-37. ] DOI: 10.3969/j.issn.1009-8518.2017.01.005 doi: 10.3969/j.issn.1009-8518.2017.01.005
[80]	Ermida S L, DaCamara C C, Trigo I F, et al. Modelling directional effects on remotely sensed land surface temperature[J]. Remote Sensing and Environment, 2017,190:56-69. DOI: 10.1016/j.rse.2016.12.008 doi: 10.1016/j.rse.2016.12.008
[81]	Ermida S L, Trigo I F, DaCamara C C, et al. Assessing the potential of parametric models to correct directional effects on local to global remotely sensed LST[J]. Remote Sensing and Environment, 2018,209:410-422. DOI: 10.1016/j.rse.2018.02.066 doi: 10.1016/j.rse.2018.02.066
[82]	Ghent D J, Corlett G K, G Ttsche F M, et al. Global land surface temperature from the along-track scanning radiometers[J]. Journal of Geophysical Research Atmospheres, 2017,122(22):12167-12193. DOI: 10.1002/2017JD027161 doi: 10.1002/2017JD027161

影响因素	主要参数
观测时间	季节、日尺度
地表几何结构	建筑高度、密度
	高宽比（H/W）
	观测表面方向、倾角
	天空可视因子
材料物理属性	反射率
	发射率
	热导率
空间位置	观测距离
	观测天顶角、方位角
	太阳天顶角、方位角
传感器参数	视场角
传感器参数	空间分辨率

城市地表热辐射方向性研究进展综述

Review of Urban Thermal Radiation Anisotropy

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