Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2019, Vol. 34 Issue (5): 914-924    DOI: 10.11873/j.issn.1004-0323.2019.5.0914
    
Principles and Applications of the 3D Radiative Transfer Model LESS
Jianbo Qi1,2(),Donghui Xie2,Yue Xu2,Guangjian Yan2
1.The Key Laboratory for Silviculture and Conservation of Ministry of Education,Beijing Forestry University,Beijing 100083,China
2.Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
Download:  HTML  PDF (6161KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Three-dimensional (3D) radiative transfer model can accurately describe the interactions between solar radiation and heterogeneous land surfaces. Recently, it has become an important tool for quantitative remote sensing studies. LESS is a ray-tracing based 3D radiative transfer model, which take full advantage of the forward ray-tracing techniques for simulating radiative budget and backward ray-tracing for simulating large-scale images, which makes it possible to simulate various remote sensing data in a single model. Currently, LESS can simulate multi-angle Bidirectional Reflectance Factor (BRF), multi-spectral/high-spectral images, fish-eye cameras, upwelling/downwelling shortwave radiation in rugged terrains and layered FPAR, etc. This simulated dataset can be used for validating physical modes, developing parameterized models, as well as training neural networks. This paper presents the fundamentals of LESS and its applications. LESS can be downloaded from www.lessrt.org.

Key words:  LESS      Ray tracing      3D Radiative transfer      Realistic scene     
Received:  11 September 2019      Published:  05 December 2019
ZTFLH:  S771.8  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Jianbo Qi
Donghui Xie
Yue Xu
Guangjian Yan

Cite this article: 

Jianbo Qi,Donghui Xie,Yue Xu,Guangjian Yan. Principles and Applications of the 3D Radiative Transfer Model LESS. Remote Sensing Technology and Application, 2019, 34(5): 914-924.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.5.0914     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I5/914

Fig.1  Surface scattering calculation in backward path tracing
Fig.2  Multiple scattering calculation in backward path tracing
Fig.3  The main screen and major functionalities of LESS
Fig.4  Run Python script in LESS
Fig.5  Individual Trees generated by Onyx Tree
Fig.6  Comparison of the simulated image and actual image
Fig.7  Solar shorwatve radiation simulation over rugged terrains (Obsv stands for actual measurement and LESS stands for simulated data)
Fig.8  FPAR simulations in LESS
Fig.9  Reflectance influenced by branches
1 Verhoef W. Light Scattering By Leaf Layers With Application To Canopy Reflectance Modeling: the SAIL model[J]. Remote Sensing of Environment, 1984, 16(2): 125-141.
2 C van der Tol, Verhoef W, Timmermans J, et al. An Integrated Model of Soil-Canopy Spectral Radiances, Photosynthesis, Fluorescence, Temperature and Energy Balance[J]. Biogeosciences, 2009, 6(12): 3109-3129.
3 Li X, Strahler A H. Geometric-Optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy[J].IEEE Transactions on Geoscience and Remote Sensing,1986,GE-24(6): 906-919.
4 Ni W, Li X, Woodcock C E, et al. An Analytical Hybrid Gort Model for Bidirectional Reflectance over Discontinuous Plant Canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 987-999.
5 Koetz B, Sun G, Morsdorf F, et al. Fusion of Imaging Spectrometer and Lidar Data over Combined Radiative Transfer Models For Forest Canopy Characterization[J]. Remote Sensing of Environment, 2007, 106(4): 449-459.
6 Zhu X, Skidmore A K, Darvishzadeh R, et al. Estimation of Forest Leaf Water Content through Inversion of A Radiative Transfer Model from Lidar And Hyperspectral Data[J]. International Journal of Applied Earth Observation and Geoinformation,2019, 74: 120-129.
7 Qin W, Gerstl S A. 3-D Scene Modeling of Semidesert Vegetation Cover And Its Radiation Regime[J]. Remote Sensing of Environment, 2000, 74(1): 145-162.
8 Huang H, Qin W, Liu Q. RAPID: A Radiosity Applicable to Porous Individual Objects for Directional Reflectance over Complex Vegetated Scenes[J]. Remote Sensing of Environment,2013, 132: 221-237.
9 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.
10 Gastellu-Etchegorry J P, Martin E, Gascon F. DART: A 3D Model for Simulating Satellite Images and Studying Surface Radiation Budget[J]. International Journal of Remote Sensing, 2004, 25(1): 73-96.
11 Wang Zhangang, Zhuang Dafang, Ming Tao. Research on Simulation of the PAR Distribution in Tree Canopy[J]. Geo-Information Science,2008,10(6):697-702.
11 王占刚, 庄大方, 明涛. 林木冠层光合有效辐射分布模拟的研究[J]. 地球信息科学学报, 2008, 10(6): 697-702.
12 Lao Cailian. Three Dimensional Canopy Radiation Transfer Model based on Monte Carlo Ray Tracing[D]. Beijing: China Agricultural University, 2005.
12 劳彩莲. 基于蒙特卡罗光线跟踪方法的植物三维冠层辐射传输模型[D]. 北京:中国农业大学, 2005.
13 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.
14 Kobayashi H, Iwabuchi H. A Coupled 1-D Atmosphere and 3-D Canopy Radiative Transfer Model for Canopy Reflectance, Light Environment, and Photosynthesis Simulation in A Heterogeneous Landscape[J]. Remote Sensing of Environment, 2008, 112(1): 173-185.
15 Widlowski J L, Lavergne T, Pinty B, et al. Rayspread: A Virtual Laboratory for Rapid BRF Simulations over 3-D Plant Canopies[J]. Computational Methods in Transport, 2006: 211-231.
16 Zhao F, Dai X, Verhoef W, et al. FluorWPS: A Monte Carlo Ray-tracing Model to Compute Sun-Induced Chlorophyll Fluorescence of Three-dimensional Canopy[J]. Remote Sensing of Environment,2016, 187: 385-399.
17 Disney M I, Lewis P, North P. Monte Carlo Ray Tracing in Optical Canopy Reflectance Modelling[J]. Remote Sensing Reviews, 2000, 18(2-4): 163-196.
18 Goodenough A A, Brown S D. Dirsig5: Core Design and Implementation[C]∥SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2012: 83900H-83900H.
19 Pharr M, Jakob W, Humphreys G. Physically based Rendering: from Theory to Implementation[M]. San Francisco: Morgan Kaufmann, 2016.
20 Plachetka T. POV Ray: Persistence of Vision Parallel Raytracer[C]∥Spring Conference on Computer Graphics, Budmerice, Slovakia. 1998: 123-129.
21 Auer S, Hinz S, Bamler R. Ray-tracing Simulation Techniques for Understanding High-resolution SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3): 1445-1456.
22 Goodenough A A, Brown S D. DIRSIG5: Next-generation Remote Sensing Data and Image Simulation Framework[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11): 4818-4833.
23 JAKOB W. Mitsuba Renderer[EB/OL]. , 2010.
24 Qi J, Xie D, Yin T, et al. LESS: Large-Scale Remote Sensing Data and Image Simulation Framework over Heterogeneous 3D Scenes[J]. Remote Sensing of Environment,2019, 221: 695-706.
25 Thompson R L, Goel N S. Two Models for Rapidly Calculating Bidirectional Reflectance of Complex Vegetation Scenes: Photon Spread (PS) Model And Statistical Photon Spread (Sps) Model[J]. Remote Sensing Reviews, 1998, 16(3): 157-207.
26 Veach E. Robust Monte Carlo Methods for Light Transport Simulation[D]. Palo Alto: Stanford University, 1997.
27 Schneider F D, Leiterer R, Morsdorf F, et al. Simulating Imaging Spectrometer Data: 3D Forest Modeling based on Lidar and In Situ Data[J]. Remote Sensing of Environment,2014, 152: 235-250.
28 Qi J, Xie D, Guo D, et al. A Large-scale Emulation System for Realistic Three-dimensional (3-D) Forest Simulation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11): 4834-4843.
29 Xie D, Wang P, Liu R,et al. Research on PAR and FPAR of Crop Canopies based on RGM[C]∥2010 IEEE International Geoscience and Remote Sensing Symposium,2010:1493-1496.
[1] TIAN Xu-wen, YANG Ru-liang. The Analyzation of SAR Image Adaptive Sidelobe Reduction Algorithms and Experiments[J]. Remote Sensing Technology and Application, 2002, 17(3): 148 -153 .
[2] XI Yu-xiao, YANG Ru-liang. Experiment on Whitt's Polarimetric SAR Calibration
Algorithm Using Point Targets
[J]. Remote Sensing Technology and Application, 2002, 17(4): 220 -223 .
[3] . [J]. Remote Sensing Technology and Application, 1987, 2(3): 61 -66 .
[4] . [J]. Remote Sensing Technology and Application, 1988, 3(4): 29 -36 .
[5] . [J]. Remote Sensing Technology and Application, 1988, 3(4): 44 -49 .
[6] . [J]. Remote Sensing Technology and Application, 2001, 16(1): 40 -44 .
[7] XU Wei-ming, SHU Rong. Two Methods of Multi-sensor Synchronization with GPS Receiver[J]. Remote Sensing Technology and Application, 2003, 18(6): 404 -406 .
[8] YANG Jian, PENG Ying-ning. Recent Development of the Optimization of Polarimetric Contrast Enhancement[J]. Remote Sensing Technology and Application, 2005, 20(1): 38 -41 .
[9] . [J]. Remote Sensing Technology and Application, 2001, 16(3): 200 -204 .
[10] QIN Xian-lin, YI Hao-ruo. Study on Detecting Fires by Using MODIS Data[J]. Remote Sensing Technology and Application, 2002, 17(2): 66 -69 .