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A review on the development and application of three dimensional computer simulation mode of optical remote sensing

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

  • Email：bianzj@aircas.ac.cn
  • Introduction：卞尊健，1991生，男，博士后，研究方向为热红外三维辐射传输建模。E-mail: bianzj@aircas.ac.cn
BIAN Zunjian1,  
  • Affiliation：

    Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China

QI Jianbo2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

WU Shengbiao1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

    College of Geography and Environment, Shandong Normal University, Jinan 250358, China

WANG Yusheng13,  
  • Affiliation：

    INRA, CAPTE, Avignon, 84914, France

LIU Shouyang4,  
  • Affiliation：

    Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University,Wuhan 430070, China

XU Baodong5,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

  • Email：duym@aircas.ac.cn
  • Introduction：杜永明，1978生，男，副研究员，研究方向为植被热红外辐射传输建模。E-mail: duym@aircas.ac.cn
DU Yongming1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

CAO Biao1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

LI Hua1,  
  • Affiliation：

    Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China

HUANG Huaguo2,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

XIAO Qing1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100101, China

LIU Qinhuo1

Resümee

The Three-Dimensional (3D) computer simulation model is an important part of remote sensing studies, especially for complex surfaces including mixed vegetated scenes, urban area, and mountain area. After 20 years of development, the 3D computer simulation has made remarkable progress, and has been widely used in the analysis of surface radiative transfer process, the validation of simplified models/algorithms and retrieved remote sensing products. Recently, there has been a surge of interest in the high-resolution remote sensing data obtained from both satellite- and Unmanned Aerial Vehicle (UAV)-board sensors, which have heightened the need of 3D models in remote sensing simulation and inversion researches at fine scales. However, few studies have focused on the difference among models and further modifications. In order to fully understand the development of 3D remote sensing models and to explore how to better serve applications in different fields using these models, this paper reviews the research of 3D remote sensing models in optical remote sensing, which wavelength range encompasses the visible, near infrared and thermal infrared bands. In this paper, the principle, application, and development trend of models are discussed. Firstly, the modelling strategies based on ray tracing /flux tracing and radiosity theories are introduced and then some existing models are briefly compared. Then, the typical applications of 3D computer simulation models in optical remote sensing are summarized. A lot of literature has published on Bidirectional Reflectance Distribution Function (BRDF), Directional Brightness Temperatures (DBT), Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC), in which the 3D model serves as not only a static tool as data generator in validation and analysis processes but also a dynamic tool for remote sensing inversion directly. Finally, this paper provides some insights for the future development trend of the model. Three perspectives can be performed in the future: (1) all 3D models suffer from slow operation speed, thereby models should be improved associated with operation efficiency such as using new Graphics Processing Unit (GPU) devices; (2) The main advantage of 3D models lies in its accurate simulation. Then, refine the simulation and replacing the empirical or semi-empirical processes by those physical-based must be performed. At the same time, the evaluation of 3D models should be further promoted based on multiple types of measurements from field and UAV-based experiments. In addition, (3) since multiple-source remote sensing data can be obtained, a comprehensive model based on not only radiative transfer but also interdisciplinary theories associated with evapotranspiration and fluorescence would promote its ability to explain the surface phenomena. With the in-depth study of remote sensing modeling for complex surfaces, the 3D computer model will play a more vital role in the research and application of remote sensing in the future.

Schlüsselwort

3D computer model;optics remote sensing;ray tracing;flux tracing;radiosity

References

  1. 1.
    Banskota A, Wynne R H, Thomas V A, Serbin S P, Kayastha N, Gastellu-Etchegorry J P and Townsend P A. 2013. Investigating the utility of wavelet transforms for inverting a 3-D radiative transfer model using hyperspectral data to retrieve forest LAI. Remote Sensing, 5(6): 2639-2659
  2. 2.
    Baret F and Buis S. 2008. Estimating canopy characteristics from remote sensing observations: review of methods and associated problems//Liang S L, ed. Advances in land remote Sensing: System, Modeling, Inversion and Application. Dordrecht: Springer: 173-201
  3. 3.
    Bayat B, van der Tol C and Verhoef W. 2018. Integrating satellite optical and thermal infrared observations for improving daily ecosystem functioning estimations during a drought episode. Remote Sensing of Environment, 209: 375-394
  4. 4.
    Bian Z J, Cao B, Li H, Du Y M, Huang H G, Xiao Q and Liu Q H. 2018a. Modeling the distributions of brightness temperatures of a cropland study area using a model that combines fast radiosity and energy budget methods. Remote Sensing, 10(5): 736
  5. 5.
    Bian Z J, Cao B, Li H, Du Y M, Lagouarde J P, Xiao Q and Liu Q H. 2018b. An analytical four-component directional brightness temperature model for crop and forest canopies. Remote Sensing of Environment, 209: 731-746
  6. 6.
    Bian Z J, Du Y M, Li H, Cao B, Huang H G, Xiao Q and Liu Q H. 2017. Modeling the temporal variability of thermal emissions from row-planted scenes using a radiosity and energy budget method. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 6010-6026
  7. 7.
    Borel C C, Gerstl S A W and Powers B J. 1991. The radiosity method in optical remote sensing of structured 3-D surfaces. Remote Sensing of Environment, 36(1): 13-44
  8. 8.
    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
  9. 9.
    Cao B, Gastellu-Etchegorry J P, Du Y M, Li H, Bian Z J, Hu T, Fan W J, Xiao Q and Liu Q H. 2019. Evaluation of Four Kernel-Driven Models in the Thermal Infrared Band. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 5456-5475
  10. 10.
    Chelle M and Andrieu B. 1998. The nested radiosity model for the distribution of light within plant canopies. Ecological Modelling, 111(1): 75-91
  11. 11.
    Chen Y and Liou K N. 2006. A Monte Carlo method for 3D thermal infrared radiative transfer. Journal of Quantitative Spectroscopy and Radiative Transfer, 101(1): 166-178
  12. 12.
    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
  13. 13.
    Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y and Wang L. 2003. Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the ill-posed inverse problem. Remote Sensing of Environment, 84(1): 1-15
  14. 14.
    Côté J F, Widlowski J L, Fournier R A and Verstraete M M. 2009. The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sensing of Environment, 113(5): 1067-1081
  15. 15.
    Disney M, Lewis P and Saich P. 2006. 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains. Remote Sensing of Environment, 100(1): 114-132
  16. 16.
    Disney M I, Lewis P and North P R J. 2000. Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18(2/4): 163-196
  17. 17.
    Disney M I, Lewis P, Gomez-Dans J, Roy D, Wooster M J and Lajas D. 2011. 3D radiative transfer modelling of fire impacts on a two-layer savanna system. Remote Sensing of Environment, 115(8): 1866-1881
  18. 18.
    Duffour C, Olioso A, Demarty J, Van der Tol C and Lagouarde J P. 2015. An evaluation of SCOPE: a tool to simulate the directional anisotropy of satellite-measured surface temperatures. Remote Sensing of Environment, 158: 362-375
  19. 19.
    Duthoit S, Demarez V, Gastellu-Etchegorry J P, Martin E and Roujean J L. 2008. Assessing the effects of the clumping phenomenon on BRDF of a maize crop based on 3D numerical scenes using DART model. Agricultural and Forest Meteorology, 148(8/9): 1341-1352
  20. 20.
    España M L, Baret F, Aries F, Chelle M, Andrieu B and Prévot L. 1999. Modeling maize canopy 3D architecture: application to reflectance simulation. Ecological Modelling, 122(1/2): 25-43
  21. 21.
    Ferreira M P, Féret J B, Grau E, Gastellu-Etchegorry J P, do Amaral C H, Shimabukuro Y E and de Souza Filho C R. 2018. Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy. Remote Sensing of Environment, 211: 276-291
  22. 22.
    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
  23. 23.
    García-Santos V, Cuxart J, Jiménez M A, Martínez-Villagrasa D, Simó G, Picos R and Caselles V. 2019. Study of temperature heterogeneities at sub-kilometric scales and influence on surface-atmosphere energy interactions. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 640-654
  24. 24.
    Gastellu-Etchegorry J P. 2008. 3D modeling of satellite spectral images, radiation budget and energy budget of urban landscapes. Meteorology and Atmospheric Physics, 102(3/4): 187
  25. 25.
    Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156
  26. 26.
    Gastellu-Etchegorry J P, Lauret N, Yin T G, Landier L, Kallel A, Malenovský Z, Al Bitar A, Aval J, Benhmida S, Qi J B, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N and Mitraka Z. 2017. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 2640-2649
  27. 27.
    Gastellu-Etchegorry J P, Martin E and Gascon F. 2004. DART: a 3D model for simulating satellite images and studying surface radiation budget. International Journal of Remote Sensing, 25(1): 73-96
  28. 28.
    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
  29. 29.
    Goel N S and Qin W H. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: a computer simulation. Remote Sensing Reviews, 10(4): 309-347
  30. 30.
    Goel N S, Rozehnal I and Thompson R L. 1991. A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sensing of Environment, 36(2): 73-104
  31. 31.
    Goodenough A A and Brown S D. 2017. DIRSIG5: next-generation remote sensing data and image simulation framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11): 4818-4833
  32. 32.
    Govaerts Y M and Verstraete M M. 1998. Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Transactions on Geoscience and Remote Sensing, 36(2): 493-505
  33. 33.
    Gu Y, Liou K N, Lee W L and Leung L R. 2012. Simulating 3-D radiative transfer effects over the Sierra Nevada Mountains using WRF. Atmospheric Chemistry and Physics, 12(20): 9965-9976
  34. 34.
    Guillen-Climent M L, Zarco-Tejada P J, Berni J A J, North P R J and Villalobos F J. 2012. Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precision Agriculture, 13(4): 473-500
  35. 35.
    Guillevic P, Gastellu-Etchegorry J P, Demarty J and Prévot L. 2003. Thermal infrared radiative transfer within three-dimensional vegetation covers. Journal of Geophysical Research: Atmospheres, 108(D8): 4248
  36. 36.
    Guillevic P C, Bork-Unkelbach A, Göttsche F M, Hulley G, Gastellu-Etchegorry J, 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
  37. 37.
    Guo J and Niu Z. 2009. Progress in 3D modeling and visualization of vegetation. Computer Engineering and Applications, 45(10): 26-29, 33 (郭俊, 牛铮. 2009. 植被三维建模及应用进展. 计算机工程与应用, 45(10): 26-29, 33)
  38. 38.
    He Y J. 2016. Computer Graphics. 3rd ed. Beijing: Machinery Industry Press
  39. 39.
    Hénon A. 2008. Températures Mesurées, Modélisées, et Observées par Télédétection Infrarouge Dans la Canopée Urbaine: Modélisation Aero-Thermo-Radiative des Flux de Chaleur Urbains. Nantes: Ecole Centrale de Nantes
  40. 40.
    Huang H G, Liu Q H and Qin W H. 2010. Thermal emission hot-spot effect of crop canopies-part I: simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 313-322
  41. 41.
    Huang H G, Liu Q H, Qin W H, Du Y M and Li X W. 2011. Temporal patterns of thermal emission directionality of crop canopies. Journal of Geophysical Research: Atmospheres, 116(D6): D06114
  42. 42.
    Huang H G, Qin W H and Liu Q H. 2013. RAPID: a radiosity applicable to porous IndiviDual objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132: 221-237
  43. 43.
    Huang H G, Zhang Z Y, Ni W J, Chai L N, Qin W H, Liu G, Xie D H, Jiang L M and Liu Q H. 2018. Extending RAPID model to simulate forest microwave backscattering. Remote Sensing of Environment, 217: 272-291
  44. 44.
    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
  45. 45.
    Kimes D, Gastellu-Etchegorry J and Esteve P. 2002. Recovery of forest canopy characteristics through inversion of a complex 3D model. Remote Sensing of Environment, 79(2/3): 320-328
  46. 46.
    Kimes D S and Kirchner J A. 1982. Radiative transfer model for heterogeneous 3-D scenes. Applied Optics, 21(22): 4119-4129
  47. 47.
    Knyazikhin Y, Martonchik J V, Myneni R B, Diner D J and Running S W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research: Atmospheres, 103(D24): 32257-32275
  48. 48.
    Kobayashi H and Iwabuchi H. 2008. A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape. Remote Sensing of Environment, 112(1): 173-185
  49. 49.
    Kötz B, Schaepman M, Morsdorf F, Bowyer P, Itten K and Allgöwer B. 2004. Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sensing of Environment, 92(3): 332-344
  50. 50.
    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
  51. 51.
    Lagouarde J P, Dayau S, Moreau P and Guyon D. 2014. Directional anisotropy of brightness surface temperature over vineyards: case study over the Medoc region (SW France). IEEE Geoscience and Remote Sensing Letters, 11(2): 574-578
  52. 52.
    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
  53. 53.
    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
  54. 54.
    Le Roux X, Gauthier H, Bégué A and Sinoquet H. 1997. Radiation absorption and use by humid savanna grassland: assessment using remote sensing and modelling. Agricultural and Forest Meteorology, 85(1/2): 117-132
  55. 55.
    Lee W L, Liou K N and Wang C C. 2013. Impact of 3-D topography on surface radiation budget over the Tibetan Plateau. Theoretical and Applied Climatology, 113(1): 95-103
  56. 56.
    Lewis P. 1999. Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie, 19(3/4): 185-210
  57. 57.
    Li W K, Guo Q H, Tao S L and Su Y J. 2018. VBRT: a novel voxel-based radiative transfer model for heterogeneous three-dimensional forest scenes. Remote Sensing of Environment, 206: 318-335
  58. 58.
    Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F and Sobrino J A. 2013. Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131: 14-37
  59. 59.
    Liou K N, Lee W L and Hall A. 2007. Radiative transfer in mountains: application to the Tibetan Plateau. Geophysical Research Letters, 34(23): L23809
  60. 60.
    Liu Q H, Huang H G, Qin W H, Fu K H and Li X W. 2007. An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy. IEEE Transactions on Geoscience and Remote Sensing, 45(9): 2900-2918
  61. 61.
    Liu S Y, Baret F, Abichou M, Boudon F, Thomas S, Zhao K G, Fournier C, Andrieu B, Irfan K, Hemmerlé M and de Solan B. 2017. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agricultural and Forest Meteorology, 247: 12-20
  62. 62.
    Malbéteau Y, Merlin O, Gascoin S, Gastellu J P, Mattar C, Olivera-Guerra L, Khabba S and Jarlan L. 2017. Normalizing land surface temperature data for elevation and illumination effects in mountainous areas: a case study using ASTER data over a steep-sided valley in Morocco. Remote Sensing of Environment, 189: 25-39
  63. 63.
    Malenovský Z, Homolová L, Zurita-Milla R, Lukeš P, Kaplan V, Hanuš J, Gastellu-Etchegorry J P and Schaepman M E. 2013. Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer. Remote Sensing of Environment, 131: 85-102
  64. 64.
    Malenovský Z, Martin E, Homolová L, Gastellu-Etchegorry J P, Zurita-Milla R, Schaepman M E, Pokorný R, Clevers J G P W and Cudlín P. 2008. Influence of woody elements of a Norway spruce canopy on nadir reflectance simulated by the DART model at very high spatial resolution. Remote Sensing of Environment, 112(1): 1-18
  65. 65.
    Miesch C, Briottet X and Kerr Y H. 2004. Phenomenological analysis of simulated signals observed over shaded areas in an urban scene. IEEE Transactions on Geoscience and Remote Sensing, 42(2): 434-442
  66. 66.
    Mo X, Liu S, Lin Z, Xu Y, Xiang Y and McVicar T R. 2005. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling, 183(2/3): 301-322
  67. 67.
    Myneni R B. 1991. Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies. Agricultural and Forest Meteorology, 55(3/4): 323-344
  68. 68.
    Norman J M, Kustas W P and Humes K S. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3/4): 263-293
  69. 69.
    North P R J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 946-956
  70. 70.
    Pharr M, Jakob W and Humphreys G. 2016. Physically Based Rendering: from Theory to Implementation. 3rd ed. Boston: Morgan Kaufmann
  71. 71.
    Pinheiro A C T, Privette J L and Guillevic P. 2006. Modeling the observed angular anisotropy of land surface temperature in a Savanna. IEEE Transactions on Geoscience and Remote Sensing, 44(4): 1036-1047
  72. 72.
    Qi J B, Xie D H, Yin T G, Yan G J, Gastellu-Etchegorry J P, Li L Y, Zhang W M, Mu X H and Norford L K. 2019. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221: 695-706
  73. 73.
    Qin W H and Gerstl S A W. 2000. 3-D scene modeling of semidesert vegetation cover and its radiation regime. Remote Sensing of Environment, 74(1): 145-162
  74. 74.
    Qin W H and Goel N S. 1995. An evaluation of hotspot models for vegetation canopies. Remote Sensing Reviews, 13(1/2): 121-159
  75. 75.
    Qin W H, Goel N S and Wang B Q. 1996. The hotspot effect in heterogeneous vegetation canopies and performances of various hotspot models. Remote Sensing Reviews, 14(4): 283-332
  76. 76.
    Roberts G, Wooster M J, Lauret N, Gastellu-Etchegorry J P, Lynham T and McRae D. 2018. Investigating the impact of overlying vegetation canopy structures on fire radiative power (FRP) retrieval through simulation and measurement. Remote Sensing of Environment, 217: 158-171
  77. 77.
    Schneider F D, Leiterer R, Morsdorf F, Gastellu-Etchegorry J P, Lauret N, Pfeifer N and Schaepman M E. 2014. Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152: 235-250
  78. 78.
    Schott J R, Brown S D, Raqueño R V, Gross H N and Robinson G. 1999. An advanced synthetic image generation model and its application to multi/hyperspectral algorithm development. Canadian Journal of Remote Sensing, 25(2): 99-111
  79. 79.
    Sepulcre-Cantó G, Zarco-Tejada P J, Sobrino J A, Berni J A J, Jiménez-Muñoz J C and Gastellu-Etchegorry J P. 2009. Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. Agricultural and Forest Meteorology, 149(6/7): 962-975
  80. 80.
    Shabanov N V, Huang D, Yang W Z, Tan B, Knyazikhin Y, Myneni R B, Ahl D E, Gower S T, Huete A R and Aragão L E O C. 2005. Analysis and optimization of the MODIS leaf area index algorithm retrievals over broadleaf forests. IEEE Transactions on Geoscience and Remote Sensing, 43(8): 1855-1865
  81. 81.
    Simioni G, Marie G and Huc R. 2016. Influence of vegetation spatial structure on growth and water fluxes of a mixed forest: results from the NOTG 3D model. Ecological Modelling, 328: 119-135
  82. 82.
    Simon H, Lindén J, Hoffmann D, Braun P, Bruse M and Esper J. 2018. Modeling transpiration and leaf temperature of urban trees–A case study evaluating the microclimate model ENVI-met against measurement data. Landscape and Urban Planning, 174: 33-40
  83. 83.
    Smith J A, Ranson K J, Nguyen D, Balick L, Link L E, Fritschen L and Hutchison B. 1981. Thermal vegetation canopy model studies. Remote Sensing of Environment, 11: 311-326
  84. 84.
    Sobrino J A, Mattar C, Gastellu-Etchegorry J P, Jiménez-Muñoz J C and Grau E. 2011. Evaluation of the DART 3D model in the thermal domain using satellite/airborne imagery and ground-based measurements. International Journal of Remote Sensing, 32(22): 7453-7477
  85. 85.
    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, 111(1): 109-132
  86. 86.
    Strahler A H and Woodcock C E. 1986. On the nature of models in remote sensing. Remote Sensing of Environment, 20(2): 121-139.
  87. 87.
    Su L H, Li X W and Wang J D. 2002. Simulation of emissivity for non-isothermal pixels using Monte Carlo theory. Progress in Natural Science, 12(4): 446-448
  88. 88.
    Thomas C, Doz S, Briottet X and Lachérade S. 2011. AMARTIS v2: 3D radiative transfer code in the [0.4; 2.5µm] spectral domain dedicated to urban areas. Remote Sensing, 3(9): 1914-1942
  89. 89.
    Thompson R and Goel N. 1999. SPRINT: a universal canopy reflectance model for kilometer level scene//Proceedings of the 2nd International Workshop on Multiple-Angle Measurements and Models. Italy: ISPRA
  90. 90.
    Verhoef W, Jia L, Xiao Q and Su Z. 2007. Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1808-1822
  91. 91.
    Widlowski J L, Côté J F and Béland M. 2014. Abstract tree crowns in 3D radiative transfer models: impact on simulated open-canopy reflectances. Remote Sensing of Environment, 142: 155-175
  92. 92.
    Widlowski J L, Lavergne T, Pinty B, Verstraete M and Gobron N. 2006. Rayspread: a virtual laboratory for rapid BRF simulations over 3-D plant canopies//Graziani F, ed. Computational Methods in Transport: Granlibakken 2004. Berlin, Heidelberg: Springer: 211-231
  93. 93.
    Widlowski J L, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R, Côté J F, Eenmäe A, Essery R, Gastellu-Etchegorry J P, Gobron N, Grau E, Haverd V, Homolová L, Huang H G, Hunt L, Kobayashi H, Koetz B, Kuusk A, Kuusk J, Lang M, Lewis P E, Lovell J L, Malenovský Z, Meroni M, Morsdorf F, Mõttus M, Ni-Meister W, Pinty B, Rautiainen M, Schlerf M, Somers B, Stuckens J, Verstraete M M, Yang W Z, Zhao F and Zenone T. 2015. The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: actual canopy scenarios and conformity testing. Remote Sensing of Environment, 169: 418-437
  94. 94.
    Widlowski J L, Pinty B, Lavergne T, Verstraete M M and Gobron N. 2005. Using 1-D models to interpret the reflectance anisotropy of 3-D canopy targets: issues and caveats. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2008-2017
  95. 95.
    Xie D H, Wang P J, Qin W H, Zhu Q J, Wang J D and Wu M X. 2007. A study on the radiance distribution in the canopy affected by non-lambert characteristics of leaf based on RGM. Journal of Remote Sensing, 11(6): 868-874
  96. 96.
    Xie D H, Wang P J, Yan G J and Zhu Q J. 2012. Extending RGM to simulate the directional reflectance for complex mountainous regions//Proceedings of 2012 IEEE Geoscience and Remote Sensing Symposium. Munich, Germany: IEEE: 4209-4212
  97. 97.
    Yin G F, Li A N, Zhao W, Jin H A, Bian J H and Wu S B. 2017. Modeling canopy reflectance over sloping terrain based on path length correction. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4597-4609
  98. 98.
    Yin T G, Gastellu-Etchegorry J P, Lauret N, Grau E and Rubio J. 2013. A new approach of direction discretization and oversampling for 3D anisotropic radiative transfer modeling. Remote Sensing of Environment, 135: 213-223
  99. 99.
    Yin T G, Lauret N and Gastellu-Etchegorry J P. 2015. Simulating images of passive sensors with finite field of view by coupling 3-D radiative transfer model and sensor perspective projection. Remote Sensing of Environment, 162: 169-185
  100. 100.
    Yuan H, Dickinson R E, Dai Y J, Shaikh M J, Zhou L M, Shangguan W and Ji D Y. 2014. A 3D canopy radiative transfer model for global climate modeling: description, validation, and application. Journal of Climate, 27(3): 1168-1192
  101. 101.
    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
  102. 102.
    Zhan W F, Zhou J and Ma W. 2009. Computer simulation of land surface thermal anisotropy based on realistic structure model: a review. Advances in Earth Science, 24(12): 1309-1317
  103. 103.
    Zhang Y, Liu Q H, Qin W H, Liu Q, Li J, Du Y M, Xie D H, Huang H G, Yang L and Ni W J. 2015. Advances in canopy radiation and scattering characteristic three-dimensional joint simulation models. Journal of Remote Sensing, 19(6): 894-909
  104. 104.
    Zhang Y, Liu Q H, Tan L F, Huang H G, Ni W J, Yin T G, Qin W H and Sun G Q. 2017. A3-D joint simulation platform for multiband remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11): 4763-4778

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