浏览全部资源
扫码关注微信
纸质出版日期： 2021-02-07 ，
扫 描 看 全 文
卞尊健，漆建波，吴胜标，王雨生，刘守阳，徐保东，杜永明，曹彪，历华，黄华国，肖青，柳钦火.2021.光学遥感三维计算机模拟模型的研究进展与应用.遥感学报，25（2）： 559-576
Bian Z J，Qi J B，Wu S B，Wang Y S，Liu S Y，Xu B D，Du Y M，Cao B，Li H，Huang H G，Xiao Q and Liu Q H. 2021. A review on the development and application of three dimensional computer simulation mode of optical remote sensing. National Remote Sensing Bulletin， 25（2）：559-576
三维计算机模拟模型是遥感辐射传输机理研究的重要内容，在复杂地表正向模拟和遥感反演中扮演着重要角色。经过最近20年的发展，三维计算机建模研究取得了显著进展，在地表辐射传输过程分析、模型和算法的验证及遥感反演等方面应用广泛。为了更加充分理解三维计算机模拟模型的发展和模型间的差异，及探讨如何将其更好的应用于生活、生产，本文对三维计算机模拟模型在光学遥感的研究进行了综述。本文从模型的原理、应用和发展趋势3个方面展开了论述。首先，简要介绍了光线追踪方法和辐射度方法的原理及现有的模型；然后，对三维计算机模拟模型在遥感的主要应用进行了总结；最后，对模型未来的发展趋势展开了讨论，从运行效率、模拟精度和功能集成等方面存在的问题和需求出发，分析了三维计算机模拟模型发展和遥感应用的趋势。随着复杂地表遥感建模研究的深入，计算机技术的发展和多源遥感数据特别是高时空分辨率数据的应用，三维计算机模拟模型将在遥感理论研究和应用发挥更加重要的作用。
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.
三维计算机模拟模型光学遥感光线追踪通量追踪辐射度
3D computer modeloptics remote sensingray tracingflux tracingradiosity
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 [DOI: 10.3390/rs5062639http://dx.doi.org/10.3390/rs5062639]
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 [DOI: 10.1007/978-1-4020-6450-0_7http://dx.doi.org/10.1007/978-1-4020-6450-0_7]
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 [DOI: 10.1016/j.rse.2018.02.027http://dx.doi.org/10.1016/j.rse.2018.02.027]
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 [DOI: 10.3390/rs10050736http://dx.doi.org/10.3390/rs10050736]
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 [DOI: 10.1016/j.rse.2018.03.010http://dx.doi.org/10.1016/j.rse.2018.03.010]
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 [DOI: 10.1109/TGRS.2017.2719098http://dx.doi.org/10.1109/TGRS.2017.2719098]
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 [DOI: 10.1016/0034-4257(91)90028-5http://dx.doi.org/10.1016/0034-4257(91)90028-5]
Bruse M and Fleer H. 1998. Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling and Software, 13(3/4): 373-384 [DOI: 10.1016/s1364-8152(98)00042-5http://dx.doi.org/10.1016/s1364-8152(98)00042-5]
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 [DOI: 10.1109/TGRS.2019.2899600http://dx.doi.org/10.1109/TGRS.2019.2899600]
Chelle M and Andrieu B. 1998. The nested radiosity model for the distribution of light within plant canopies. Ecological Modelling, 111(1): 75-91 [DOI: 10.1016/s0304-3800(98)00100-8http://dx.doi.org/10.1016/s0304-3800(98)00100-8]
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 [DOI: 10.1016/j.jqsrt.2005.10.002http://dx.doi.org/10.1016/j.jqsrt.2005.10.002]
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
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, 33(6): 555-567 [DOI: 10.11867/j.issn.1001-8166.2018.06.0555http://dx.doi.org/10.11867/j.issn.1001-8166.2018.06.0555]
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 [DOI: 10.1016/S0034-4257(02)00035-4http://dx.doi.org/10.1016/S0034-4257(02)00035-4]
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 [DOI: 10.1016/j.rse.2009.01.017http://dx.doi.org/10.1016/j.rse.2009.01.017]
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 [DOI: 10.1016/j.rse.2005.10.003http://dx.doi.org/10.1016/j.rse.2005.10.003]
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 [DOI: 10.1080/02757250009532389http://dx.doi.org/10.1080/02757250009532389]
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 [DOI: 10.1016/j.rse.2011.03.010http://dx.doi.org/10.1016/j.rse.2011.03.010]
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 [DOI: 10.1016/j.rse.2014.10.019http://dx.doi.org/10.1016/j.rse.2014.10.019]
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 [DOI: 10.1016/j.agrformet.2008.03.011http://dx.doi.org/10.1016/j.agrformet.2008.03.011]
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 [DOI: 10.1016/s0304-3800(99)00070-8http://dx.doi.org/10.1016/s0304-3800(99)00070-8]
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 [DOI: 10.1016/j.rse.2018.04.023http://dx.doi.org/10.1016/j.rse.2018.04.023]
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 [DOI: 10.1364/AO.47.005799http://dx.doi.org/10.1364/AO.47.005799]
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 [DOI: 10.1109/TGRS.2018.2859182http://dx.doi.org/10.1109/TGRS.2018.2859182]
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 [DOI: 10.1007/s00703-008-0344-1http://dx.doi.org/10.1007/s00703-008-0344-1]
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 [DOI: 10.1016/0034-4257(95)00253-7http://dx.doi.org/10.1016/0034-4257(95)00253-7]
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 [DOI: 10.1109/JSTARS.2017.2685528http://dx.doi.org/10.1109/JSTARS.2017.2685528]
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 [DOI: 10.1080/0143116031000115166http://dx.doi.org/10.1080/0143116031000115166]
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 [DOI: 10.3390/rs70201667http://dx.doi.org/10.3390/rs70201667]
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 [DOI: 10.1080/02757259409532252http://dx.doi.org/10.1080/02757259409532252]
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 [DOI: 10.1016/0034-4257(91)90032-2http://dx.doi.org/10.1016/0034-4257(91)90032-2]
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 [DOI: 10.1109/JSTARS.2017.2758964http://dx.doi.org/10.1109/JSTARS.2017.2758964]
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 [DOI: 10.1109/36.662732http://dx.doi.org/10.1109/36.662732]
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 [DOI: 10.5194/acp-12-9965-2012http://dx.doi.org/10.5194/acp-12-9965-2012]
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 [DOI: 10.1007/s11119-012-9263-8http://dx.doi.org/10.1007/s11119-012-9263-8]
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 [DOI: 10.1029/2002jd002247http://dx.doi.org/10.1029/2002jd002247]
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 [DOI: 10.1109/LGRS.2013.2260319http://dx.doi.org/10.1109/LGRS.2013.2260319]
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) [DOI: 10.3778/j.issn.1002-8331.2009.10.009http://dx.doi.org/10.3778/j.issn.1002-8331.2009.10.009]
He Y J. 2016. Computer Graphics. 3rd ed. Beijing: Machinery Industry Press
何援军. 2016. 计算机图形学. 3版. 北京: 机械工业出版社
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
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 [DOI: 10.1109/JSTARS.2010.2046625http://dx.doi.org/10.1109/JSTARS.2010.2046625]
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 [DOI: 10.1029/2010JD014613http://dx.doi.org/10.1029/2010JD014613]
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 [DOI: 10.1016/j.rse.2013.01.013http://dx.doi.org/10.1016/j.rse.2013.01.013]
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 [DOI: 10.1016/j.rse.2018.08.011http://dx.doi.org/10.1016/j.rse.2018.08.011]
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]
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 [DOI: 10.1016/s0034-4257(01)00282-6http://dx.doi.org/10.1016/s0034-4257(01)00282-6]
Kimes D S and Kirchner J A. 1982. Radiative transfer model for heterogeneous 3-D scenes. Applied Optics, 21(22): 4119-4129 [DOI: 10.1364/AO.21.004119http://dx.doi.org/10.1364/AO.21.004119]
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 [DOI: 10.1029/98jd02462http://dx.doi.org/10.1029/98jd02462]
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 [DOI: 10.1016/j.rse.2007.04.010http://dx.doi.org/10.1016/j.rse.2007.04.010]
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 [DOI: 10.1016/j.rse.2004.05.015http://dx.doi.org/10.1016/j.rse.2004.05.015]
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]
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 [DOI: 10.1109/lgrs.2013.2282492http://dx.doi.org/10.1109/lgrs.2013.2282492]
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 [DOI: 10.1016/j.rse.2011.06.022http://dx.doi.org/10.1016/j.rse.2011.06.022]
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 [DOI: 10.1016/j.rse.2009.08.012http://dx.doi.org/10.1016/j.rse.2009.08.012]
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 [DOI: 10.1016/S0168-1923(97)00002-6http://dx.doi.org/10.1016/S0168-1923(97)00002-6]
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 [DOI: 10.1007/s00704-012-0767-yhttp://dx.doi.org/10.1007/s00704-012-0767-y]
Lewis P. 1999. Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie, 19(3/4): 185-210 [DOI: 10.1051/agro:19990302http://dx.doi.org/10.1051/agro:19990302]
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 [DOI: 10.1016/j.rse.2017.12.043http://dx.doi.org/10.1016/j.rse.2017.12.043]
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 [DOI: 10.1016/j.rse.2012.12.008http://dx.doi.org/10.1016/j.rse.2012.12.008]
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 [DOI: 10.1029/2007gl031762http://dx.doi.org/10.1029/2007gl031762]
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 [DOI: 10.1109/tgrs.2007.902272http://dx.doi.org/10.1109/tgrs.2007.902272]
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 [DOI: 10.1016/j.agrformet.2017.07.007http://dx.doi.org/10.1016/j.agrformet.2017.07.007]
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 [DOI: 10.1016/j.rse.2016.11.010http://dx.doi.org/10.1016/j.rse.2016.11.010]
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 [DOI: 10.1016/j.rse.2012.12.015http://dx.doi.org/10.1016/j.rse.2012.12.015]
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 [DOI: 10.1016/j.rse.2006.02.028http://dx.doi.org/10.1016/j.rse.2006.02.028]
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 [DOI: 10.1109/TGRS.2003.814916http://dx.doi.org/10.1109/TGRS.2003.814916]
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 [DOI: 10.1016/j.ecolmodel.2004.07.032http://dx.doi.org/10.1016/j.ecolmodel.2004.07.032]
Myneni R B. 1991. Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies. Agricultural and Forest Meteorology, 55(3/4): 323-344 [DOI: 10.1016/0168-1923(91)90069-3http://dx.doi.org/10.1016/0168-1923(91)90069-3]
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 [DOI: 10.1016/0168-1923(95)02265-yhttp://dx.doi.org/10.1016/0168-1923(95)02265-y]
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 [DOI: 10.1109/36.508411http://dx.doi.org/10.1109/36.508411]
Pharr M, Jakob W and Humphreys G. 2016. Physically Based Rendering: from Theory to Implementation. 3rd ed. Boston: Morgan Kaufmann
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 [DOI: 10.1109/tgrs.2005.863827http://dx.doi.org/10.1109/tgrs.2005.863827]
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 [DOI: 10.1016/j.rse.2018.11.036http://dx.doi.org/10.1016/j.rse.2018.11.036]
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 [DOI: 10.1016/s0034-4257(00)00129-2http://dx.doi.org/10.1016/s0034-4257(00)00129-2]
Qin W H and Goel N S. 1995. An evaluation of hotspot models for vegetation canopies. Remote Sensing Reviews, 13(1/2): 121-159 [DOI: 10.1080/02757259509532299http://dx.doi.org/10.1080/02757259509532299]
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 [DOI: 10.1080/02757259609532323http://dx.doi.org/10.1080/02757259609532323]
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 [DOI: 10.1016/j.rse.2018.08.015http://dx.doi.org/10.1016/j.rse.2018.08.015]
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 [DOI: 10.1016/j.rse.2014.06.015http://dx.doi.org/10.1016/j.rse.2014.06.015]
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 [DOI: 10.1080/07038992.1999.10874709http://dx.doi.org/10.1080/07038992.1999.10874709]
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 [DOI: 10.1016/j.agrformet.2008.12.001http://dx.doi.org/10.1016/j.agrformet.2008.12.001]
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 [DOI: 10.1109/tgrs.2005.852477http://dx.doi.org/10.1109/tgrs.2005.852477]
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 [DOI: 10.1016/j.ecolmodel.2016.02.004http://dx.doi.org/10.1016/j.ecolmodel.2016.02.004]
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 [DOI: 10.1016/j.landurbplan.2018.03.003http://dx.doi.org/10.1016/j.landurbplan.2018.03.003]
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 [DOI: 10.1016/0034-4257(81)90028-6http://dx.doi.org/10.1016/0034-4257(81)90028-6]
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 [DOI: 10.1080/01431161.2010.524672http://dx.doi.org/10.1080/01431161.2010.524672]
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 [DOI: 10.1023/B:BOUN.0000010995.62115.46http://dx.doi.org/10.1023/B:BOUN.0000010995.62115.46]
Strahler A H and Woodcock C E. 1986. On the nature of models in remote sensing. Remote Sensing of Environment, 20(2): 121-139. [DOI: 10.1016/0034-4257(86)90018-0http://dx.doi.org/10.1016/0034-4257(86)90018-0]
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
苏理宏, 李小文, 王锦地. 2002. 地表非同温像元发射率的Monte Carlo模拟. 自然科学进展, 12(4): 446-448 [DOI: 10.3321/j.issn:1002-008X.2002.04.024http://dx.doi.org/10.3321/j.issn:1002-008X.2002.04.024]
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 [DOI: 10.3390/rs3091914http://dx.doi.org/10.3390/rs3091914]
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
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 [DOI: 10.1109/TGRS.2007.895844http://dx.doi.org/10.1109/TGRS.2007.895844]
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 [DOI: 10.1016/j.rse.2013.11.016http://dx.doi.org/10.1016/j.rse.2013.11.016]
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 [DOI: 10.1007/3-540-28125-8_10http://dx.doi.org/10.1007/3-540-28125-8_10]
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 [DOI: 10.1016/j.rse.2015.08.016http://dx.doi.org/10.1016/j.rse.2015.08.016]
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 [DOI: 10.1109/tgrs.2005.853718http://dx.doi.org/10.1109/tgrs.2005.853718]
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
谢东辉, 王培娟, 覃文汉, 朱启疆, 王锦地, 吴门新. 2007. 叶片非朗伯特性影响冠层辐射分布的辐射度模型模拟与分析. 遥感学报, 11(6): 868-874 [DOI: 10.3321/j.issn:1007-4619.2007.06.014http://dx.doi.org/10.3321/j.issn:1007-4619.2007.06.014]
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 [DOI: 10.1109/IGARSS.2012.6351740http://dx.doi.org/10.1109/IGARSS.2012.6351740]
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 [DOI: 10.1109/TGRS.2017.2694483http://dx.doi.org/10.1109/TGRS.2017.2694483]
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 [DOI: 10.1016/j.rse.2013.03.030http://dx.doi.org/10.1016/j.rse.2013.03.030]
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 [DOI: 10.1016/j.rse.2015.02.020http://dx.doi.org/10.1016/j.rse.2015.02.020]
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 [DOI: 10.1175/jcli-d-13-00155.1http://dx.doi.org/10.1175/jcli-d-13-00155.1]
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]
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
占文凤, 周纪, 马伟. 2009. 基于真实结构的地表热辐射方向性计算机模拟研究进展. 地球科学进展, 24(12): 1309-1317 [DOI: 10.3321/j.issn:1001-8166.2009.12.003http://dx.doi.org/10.3321/j.issn:1001-8166.2009.12.003]
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
张阳, 柳钦火, 覃文汉, 刘强, 李静, 杜永明, 谢东辉, 黄华国, 杨乐, 倪文俭. 2015. 3维场景植被冠层多波段模拟研究进展. 遥感学报, 19(6): 894-909 [DOI: 10.11834/jrs.20154258http://dx.doi.org/10.11834/jrs.20154258]
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 [DOI: 10.1109/JSTARS.2017.2750223http://dx.doi.org/10.1109/JSTARS.2017.2750223]
相关作者
相关机构
京公网安备11010802024621