基于真实结构的地表热辐射方向性计算机模拟研究进展

doi:10.11867/j.issn.1001-8166.2009.12.1309

地球科学进展 ›› 2009, Vol. 24 ›› Issue (12): 1309 -1317. doi: 10.11867/j.issn.1001-8166.2009.12.1309

所属专题： IODP研究；

基于真实结构的地表热辐射方向性计算机模拟研究进展

占文凤 ¹, 周纪 ¹, 马伟 ^1,2

1.北京师范大学资源学院，地表过程与资源生态国家重点实验室，北京100875；2.中国冶金地质总局矿产资源研究院遥感中心，北京100029

收稿日期:2009-06-15 修回日期:2009-12-17 出版日期:2009-12-10
通讯作者: 占文凤(1986)，男，江西新余人，博士研究生，主要从事资源环境遥感研究. E-mail:zhanwenfeng@ires.cn
基金资助:
国家自然科学基金项目“城市地表热辐射方向性模型及能量通量的角度订正方法”(编号：40771136)；“大城市建筑结构及材料与城市热岛效应耦合机制的遥感研究——以北京为例”(编号：40701114)；北京市优秀人才培养资助项目“北京市地表能量收支的遥感研究”(编号：20081D0503100254)资助.

Computer Simulation of Land Surface Thermal Anisotropy Based on Realistic Structure Model: A Review

ZHAN Wenfeng ¹, ZHOU Ji ¹, MA Wei ^1,2

1.State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science & Technology, Beijing Normal University, Beijing100875,China;2.Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing100029, China

Received:2009-06-15 Revised:2009-12-17 Online:2009-12-10 Published:2009-12-17
Contact: Zhan Wenfeng E-mail:zhanwenfeng@ires.cn

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地表温度是地质学、水文学和陆面过程研究中的重要参数。由于地表的三维结构和异质性，大部分不同类型陆地表面均存在不同程度热辐射方向性现象。总结了基于真实结构的地表热辐射方向性计算机模型研究进展，归纳了真实结构模型中蒙特卡罗光线追踪法和辐射度方法的理论基础，阐明了两种方法的区别和联系，并从理论和物理意义上分别推导和阐释了其与几何光学模型及辐射传输模型的关系，指出了真实结构模型在算法效率、非同温系统蒙特卡罗模拟、参数获取、模型反演和应用等方面存在的主要问题，并展望了其在未来的发展趋势。

关键词: 地表温度, 热辐射方向性, 真实结构模型, 蒙特卡罗光线追踪, 辐射度

Land surface temperature is one of the most important parameters in geology, hydrology and land surface process model. However, thermal anisotropy lies in almost all types of land surfaces due to its three dimensionality and heterogeneity. Based on realistic structure model, the progresses of computer simulation about land surface thermal anisotropy have been summarized. The theoretical basis of Monte Carlo Ray Tracing (MCRT) and radiostiy has been generalized and various types of approaches have been reviewed at the same time. These two methods have been widely used in many fields, including the calculation of directional emissivity and directional brightness temperature (DBT), validating other traditional models and combining energy balance model with computer simulation models. The similarities and differences between MCRT and radiosity method were clarified. Time complexities of these two algorithms are both high. The backward-MCRT and radiosity are independent on view direction; the forward-MCRT is on the opposite side. MCRT is applicable to specular-specular and specular-diffuse surface reflection, while diffuse surfaces need radiosity methods. Statistical geometrical data of the objects is optional for MCRT, while realistic structure is necessary for radiosity. The realistic model was then compared with geometrical optics model and transfer radiation model. Theoretical derivation indicates that the computer simulation model is equal to geometrical model without considering multi-scattering between objects. On the other hand, thermal radiation transfer equation is applied to mixed dispersion media, while the radiosity integral equation is based on radiation balance of ‘micro-plane’. Some inherent flaws of computer simulation model, including time efficiency, MCRT in non-isothermal surfaces，parameters acquisition, inversion and application have been pointed out. Moreover, carrying out field observation actively, enhancing the application research of realistic structure model, integrating the advantages of existing models and some other key points which need further investigation, have also been emphasized in the end.

Key words: land surface temperature, thermal anisotropy, realistic structure model, Monte Carlo Ray Tracing, Radiosity

中图分类号:

TP701

[1]Sobrino J A, Jiménez-Muñoz J C, Verhoef W. Canopy directional emissivity: Comparison between models[J].Remote Sensing of Environment,2005, 99: 304-314.
[2]Masuda K, Takashima T, Takayama Y. Emissivity of pure sea waters for the model sea surface in the infrared window regions[J].Remote Sensing of Environment,1988, 24: 313-329.
[3]Paw U, K T. Development of models for thermal infrared radiation above and within plant canopies[J].ISPRS Journal of Photogrammetry and Remote Sensing,1992, 47: 189-203.
[4]Voogt J A. Assessment of an urban sensor view model for thermal anisotropy[J].Remote Sensing of Environment,2008, 112: 482-495.
[5]Zhou Ji, Chen Yunhao, Li Jing, et al. Progress in thermal anisotropy of urban areas: A review[J].Advances in Earth Science,2009, 24(5): 497-505.[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.]
[6]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: 443-462.
[7]Caselles V, Sobrino J A, Coll C. A physical model for interpreting the land surface temperature obtained by remote sensors over incomplete canopies[J].Remote Sensing of Environment,1992, 39: 203-211.
[8]Kimes D S, Kirchner J A. Directional radiometric measurements of row-crop temperatures[J].International Journal of Remote Sensing,1983, 4(2): 299-311.
[9]Kimes D S, Smith J A, Link L E. Thermal IR exitance model of a plant canopy[J].Applied Optics,1981, 20(4): 623-632.
[10]Sobrino J A, Caselles V. Thermal infrared model for interpreting the directional radiometric temperature of a vegetative surface[J].Remote Sensing of Environment, 1990, 33: 193-199.
[11]Yu Tao, Tian Qiyan, Gu Xingfa, et al. Modelling directional brightness temperature over a simple typical structure of urban areas[J].Journal of Remote Sensing,2006, 10(5): 661-669.[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.]
[12]Su H B, Zhang R H, Tang X Z, et al. An alternative method to compute the component fractions in the geometrical optical model: Visual computing method[J].IEEE Transactions on Geoscience and Remote Sensing,2003, 41(3): 719-724.
[13]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,2003, 111: 109-132.
[14]Kimes D S. Remote sensing of temperature profiles in vegetation canopies using multiple view angles and inversion techniques[J].IEEE Transactions on Geoscience and Remote Sensing,1981, 19(2): 85-90.
[15]Otterman J, Susskind J, Brakke T, et al. Inferring the thermal-infrared hemispheric emission from a sparsely-vegetation surface by directional measurement[J].Boundary Layer Meteorology,1995, 74: 163-180.
[16]Otterman J, Brakke T W, Fuchs M, et al. Longwave emission from a plant/soil surface as a function of the view direction: Dependence on the canopy architecture[J].International Journal of Remote Sensing,1999, 20(11): 2 195-2 201.
[17]Liu Q H, Huang H G, Qin W H, et al. An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy[J].IEEE Transactions on Geoscience and Remote Sensing,2007, 45(9): 2 900-2 918.
[18]Menenti M, Li J, Li Z L. Multi-angular thermal infrared observations of terrestrial vegetation[C]∥Liang S L, ed. Advances in Land Remote Sensing System, Modeling, Inversion and Application.Berlin: Springer, 2008:51-93.
[19]Yu T, Gu X F, Tian G L, et al. Modeling directional brightness temperature over a maize canopy in row structure[J].IEEE Transactions on Geoscience and Remote Sensing,2004, 42(10): 2 290-2 304.
[20]Wang W M, Li Z L. Simulation of directional emission from vegetative canopy using Monte-Carlo method[J].IGARSS'06,2006: 1 370-1 373.
[21]Chen Liangfu, Liu Qinhuo. The thermal radiant directionality of continuous vegetation[J].Journal of Remote Sensing,2001, 5(6): 407-415.[陈良富, 柳钦火. 连续植被的热辐射方向性[J]. 遥感学报, 2001, 5(6): 407-415.]
[22]Chen Liangfu, Zhuang Jiali, Liu Qinhuo, et al. Study the thermal anisotropy of row crops[J].Science in China (Series E),2000, 30(suppl.): 77-88.[陈良富, 庄家礼, 柳钦火, 等. 行播作物热辐射方向性规律探讨[J]. 中国科学(E辑), 2000, 30(增刊): 77-88.]
[23]Chen Liangfu, Zhuang Jiali, Xu Xiru, et al. Simulation and test of radiant directionality of non-isothermal discontinuous target[J].Journal of Remote Sensing,2000, 4(suppl.): 48-52.[陈良富, 庄家礼, 徐希孺, 等. 非同温离散体热辐射方向性模拟与验证[J]. 遥感学报, 2000, 4(增刊): 48-52.]
[24]Su Lihong, Li Xiaowen, Wang Jindi. Simulation of thermal exitance from homogeneous canopy[J].Journal of Remote Sensing,2000, 4(suppl.): 53-58.[苏理宏, 李小文, 王锦地. 水平均匀冠层热辐射的计算机模拟和模型验证[J]. 遥感学报, 2000, 4(增刊): 53-58.]
[25]Qin W H, Gerstl S A W. 3-D scene modeling of semi-desert vegetation cover and its radiation regime[J].Remote Sensing of Environment,2000, 74: 145-162.
[26]Voogt J A, Krayenhoff. Modelling urban thermal anisotropy[C]∥The 5th International Symposium on Remote Sensing of Urban Areas (URS 2005). Arizona: Phoenix, 2005.
[27]Tan Heping, Xia Xinlin, Liu Linhua, et al. Numerical Calculation of Thermal Radiation Properties and Transmission-computional heat Radiation Transfer[M]. Haerbin: Harbin Institute of Technology Press, 2006.[谈和平, 夏新林, 刘林华, 等. 红外辐射特性与传输的数值计算—计算热辐射学[M]. 哈尔滨: 哈尔滨工业大学出版社, 2006.]
[28]Chelle M, Andrieu B. Modelling the light environment of virtual crop canopies[C]∥Vos J, ed. Functional-Structural Plant Modelling in Crop Production. Netherlands:Springer, 2007:75-89.
[29]Liang S L. Quantitative Remote Sensing of Land Surfaces[M]. New Jersey: John Wiley, 2004.
[30]Li Xiaowen, Wang Jindi. Vegetation Optical Remote Sensing Model and Its Structure Parameterization[M]. Beijing: Science Press, 1995.[李小文, 王锦地. 植被光学遥感模型与植被结构参数化[M]. 北京: 科学出版社, 1995.]
[31]Disney M I, Lewis P, North P R J. Monte Carlo ray tracing in optical canopy reflectance modeling[J].Remote Sensing Reviews,2000,18(2): 163-196.
[32]Hansen J E, Travis L D. Light scattering in planetary atmospheres[J].Space Science Reviews,1974, 16: 527-610.
[33]Antyufeev V S, Marshak A L. Monte Carlo method and transport equation in plant canopies[J].Remote Sensing of Environment,1990, 31: 183-191.
[34]North P R J. Three-dimensional forest light interaction model using a Monte Carlo method[J].IEEE Transactions on Geoscience and Remote Sensing,1996, 34(4): 946-956.
[35]Chelle M, Saint-Jean S. Taking into account the 3D canopy structure to study the physical environment of plants: The Monte Carlo solution[C]∥Godin C, ed. 4th International Workshop on Functional-Structural Plant Models, FSPM04. France,2004: 176-180.
[36]Govaerts Y M, Verstraete M M. Raytran: A Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media[J].IEEE Transactions on Geoscience and Remote Sensing,1998, 36(2): 493-505.
[37]Goel N S, Thompson R L. A snapshot of canopy reflectance models and a universal model for the radiation regime[J].Remote Sensing Reviews,2000, 18: 197-225.
[38]Widlowski J L, Lavergne T, Pinty B, et al. Rayspread: A virtual laboratory for rapid BRF simulations over 3-D plant canopies[C]∥Graziani F, ed. Computational Methods in Transport. Berlin:Springer, 2006:211-231.
[39]Pinty B, Gobron N, Widlowski J, et al. Radiation transfer model intercomparison (RAMI) exercise[J].Journal of Geophysical Research,2001, 106(11): 11 937-11 956.
[40]Pinty B, Widlowski J, Taberner M, et al. Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase[J].Journal of Geophysical Research,2004, 106(D06210): 1-19.
[41]Widlowski J L, Taberner M, Pinty B, et al. Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models[J].Journal of Geophysical Research,2007, 112(D09111): 1-28.
[42]Howell J R, M P. Monte Carlo solution of thermal transfer though radiant media between gray walls[J].Journal of Heat Transfer-Transactions of ASME,1964, 86(1): 116-122.
[43]Sapritsky V I, Prokhorov A V. Spectral effective emissivities of nonisothermal cavities calculated by the Monte Carlo method[J].Applied Optics,1995, 35(25): 5645-5652.
[44]Su H B, Zhang R H, Tang X Z, et al. Determination of the effective emissivity for the regular and irregular cavities using Monte-carlo method[J].International Journal of Remote Sensing,2000, 21(11): 2 313-2 319.
[45]Su L H, Li X W, Liang S H, et al. Simulation of scaling effects of thermal emission from non-isothermal pixels with the typical three dimension structure[J].International Journal of Remote Sensing,2003, 24(19): 3 743-3 753.
[46]Wang Jiangen, Yu Tao, Li Hu, et al. Simulating row crop directional brightness temperature based on POV-ray[J].Journal of Computer Applications,2009, 29(4): 1 003-1 007.[王剑庚, 余涛, 李虎,等.基于Pov-Ray的行播作物方向亮温仿真研究[J]. 计算机应用, 2009, 29(4): 1 003-1 007.]
[47]Espaa M L, Baret F, Aries F, et al. Modeling maize canopy 3D architecture application to relectance simulation[J].Ecological Modelling,1999, 122: 25-43.
[48]Lewis P. Three-dimensional plant modelling for remote sensing simulation studies using the botanical plant modelling system[J].Agronomie,1999, 19: 185-210.
[49]Guillevic P, Gastellu-Etchegorry J P, Demarty J, et al. Thermal infrared radiative transfer within three-dimensional vegetation covers[J].Journal of Geophysical Research,2003, 108: 4 248-4 261.
[50]Peng Qunsheng, Bao Hujun, Jin Xiaogang. Principles of Realistic Computer Graphics[M]. Beijng: Science Press, 1999.[彭群生, 鲍虎军, 金小刚. 计算机真实感图形的算法基础[M]. 北京: 科学出版社,1999.]
[51]Goral C M, Torrance K E, Greenberg D P, et al. Modeling the interaction of light between diffuse surfaces[J].ACM SIGGRAPH Computer Graphics,1984, 18(3): 213-222.
[52]Nishita T, Nakamae E. Continuous tone representation of three-dimensional objects taking account of shadows and interreflection[J].ACM SIGGRAPH Computer Graphics,1985, 19(3): 23-30.
[53]Goel N S, Rozehnal I, Thompson R I. A computer graphics based model for scattering from objects of arbitrary shapes in the optical region[J].Remote Sensing of Environment,1991, 36: 73-104.
[54]Borel C C, Gerstl S A, Powers B J. The radiosity method in optical remote sensing of structured 3-D surfaces[J].Remote Sensing of Environment,1991, 36: 13-44.
[55]Gerstl S A, Borel C C. Principles of the radiosity method versus radiative transfer for canopy reflectance modeling[J].IEEE Transactions on Geoscience and Remote Sensing,1992, 30(2): 271-275.
[56]Chelle M, Andrieu B. The nested radiosity model for the distribution of light within plant canopies[J].Ecological Modelling,1998, 111: 75-91.
[57]Xie Donghui. Study on Computer Simulation Model and Its Applications[D]. Beijing: Beijing Normal University, 2005.[谢东辉. 计算机模拟模型的研究与应用[D]. 北京: 北京师范大学, 2005.]
[58]Song Jinling. Computer Simulation of Vegetation Canopy at Multi-scale and Sensitivity Analysis of Parameters[D]. Beijng: Beijing Normal University, 2006.[宋金玲. 植被冠层的多尺度计算机模拟及参数敏感性分析[D]. 北京: 北京师范大学, 2006.]
[59]Huang Huaguo, Liu Qinhuo, Liu Qiang, et al. Simulation of time effect on thermal emission directionality measurement[J].Journal of System Simulation,2007, 19(15): 3 586-3 590.[黄华国, 柳钦火, 刘强, 等. 热辐射方向性测量中的时间效应模拟[J]. 系统仿真学报, 2007, 19(15): 3 586-3 590.]
[60]Zhang R H, Li Z L, Sun X M, et al. On the applicability of Kirchoff's law and the principle of heat balance in thermal infrared remote sensing: A non-isothermal system[J].Science in China (Series D),2005, 48(1): 53-64.

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