三维辐射传输模型在森林生态系统研究中的应用与展望

doi:10.17521/cjpe.2022.0247

植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1200-1218.DOI: 10.17521/cjpe.2022.0247

所属专题：遥感生态学

三维辐射传输模型在森林生态系统研究中的应用与展望

王嘉童¹^,², 牛春跃¹, 胡天宇¹^,², 李文楷³, 刘玲莉¹^,², 郭庆华⁴, 苏艳军¹^,²^,^*()

¹中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093
²中国科学院大学, 北京 100049
³中山大学地理科学与规划学院, 广州 510275
⁴北京大学遥感与地理信息系统研究所, 北京大学城市与环境学院, 北京大学生态研究中心, 北京 100871

收稿日期:2022-06-14 接受日期:2022-08-17 出版日期:2022-10-20 发布日期:2022-09-28
通讯作者: ^*苏艳军 ORCID:0000-0001-7931-339X(ysu@ibcas.ac.cn)
基金资助:
国家自然科学基金(41871332);国家自然科学基金(41901358);中国科学院战略性先导科技专项(A类)(XDA23080301)

Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities

WANG Jia-Tong¹^,², NIU Chun-Yue¹, HU Tian-Yu¹^,², LI Wen-Kai³, LIU Ling-Li¹^,², GUO Qing-Hua⁴, SU Yan-Jun¹^,²^,^*()

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
⁴Institution of Remote Sensing and Geographical Information System, College of Urban and Environmental Sciences, Institute of Ecology, Peking University, Beijing 100871, China

Received:2022-06-14 Accepted:2022-08-17 Online:2022-10-20 Published:2022-09-28
Contact: ^*SU Yan-Jun ORCID:0000-0001-7931-339X(ysu@ibcas.ac.cn)
Supported by:
National Natural Science Foundation of China(41871332);National Natural Science Foundation of China(41901358);Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301)

摘要/Abstract

摘要：

太阳辐射是森林生态系统功能与服务得以维持并发展的基础, 对森林中的辐射传输过程进行建模对于理解森林生态系统过程具有重要意义。近年来, 三维辐射传输模型的迅速发展使得冠层辐射能量分布格局与动态的准确模拟成为可能。为更好地理解三维辐射传输模型以使其更有效地服务于森林生态系统研究, 该文从模型的原理、应用及发展趋势3个角度展开论述。首先简要介绍了辐射度方法、光线追踪法等森林三维辐射传输模型常用的原理及目前代表性的模型, 然后总结了三维辐射传输模型在森林生态系统研究中的应用, 最后对模型未来如何通过提高易用性、增加多模型耦合等方式更好地应用于森林生态系统研究进行了展望。随着森林生态系统大数据的积累与过程模型的不断完善, 三维辐射传输模型将在未来森林生态理论研究与实践中发挥更加重要的作用。

关键词: 激光雷达, 三维辐射传输模型, 森林生态系统过程, 森林结构

Abstract:

Solar radiation is fundamental to the maintenance and development of forest ecosystem functions and services. Therefore, modeling the radiation transfer process in forest is of great significance for understanding forest ecosystem processes. In recent years, the rapid development of three-dimensional radiative transfer models makes it possible to accurately simulate the distribution and dynamics of radiation within forest canopies. In order to better understand three-dimensional radiative transfer models and make them better serve forest ecosystem research, we review the principles, applications and future prospects of these models. Firstly, common principles of three-dimensional radiative transfer models such as radiosity and ray tracing are briefly introduced, and then the applications of three-dimensional radiative transfer models in forest ecosystem research are summarized. Finally, future opportunities of integrating multiple datasets and models to better facilitate forest ecosystem research, such as model coupling and making various models easier to use, are discussed. With the accumulation of ecological big data and improvement of ecosystem progress models, three-dimensional radiative transfer models will play a more important role in theoretical research and practices of forest ecology in the future.

Key words: light detection and ranging, three-dimensional radiative transfer model, forest ecosystem progress, forest structural attributes

王嘉童, 牛春跃, 胡天宇, 李文楷, 刘玲莉, 郭庆华, 苏艳军. 三维辐射传输模型在森林生态系统研究中的应用与展望. 植物生态学报, 2022, 46(10): 1200-1218. DOI: 10.17521/cjpe.2022.0247

WANG Jia-Tong, NIU Chun-Yue, HU Tian-Yu, LI Wen-Kai, LIU Ling-Li, GUO Qing-Hua, SU Yan-Jun. Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities. Chinese Journal of Plant Ecology, 2022, 46(10): 1200-1218. DOI: 10.17521/cjpe.2022.0247

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图/表 6

参考文献 151

[1]	Ariès F, Prévot L, Monestiez P. (1993). Geometrical canopy modelling in radiation simulation studies//Varlet-Grancher C, Bonhomme R, Sinoquet H. Crop Structure and Light Microclimate Characterization and Applications. INRA, Paris. 518.
[2]	Arthur Sampson D, Smith FW. (1993). Influence of canopy architecture on light penetration in lodgepole pine (Pinus contorta var. latifolia) forests. Agricultural and Forest Meteorology, 64, 63-79. DOI URL
[3]	Atzberger C. (2004). Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models. Remote Sensing of Environment, 93, 53-67. DOI URL
[4]	Bailey BN, Stoll R, Pardyjak ER, Miller NE. (2016). A new three-dimensional energy balance model for complex plant canopy geometries: model development and improved validation strategies. Agricultural and Forest Meteorology, 218- 219, 146-160.
[5]	Balandier P, Collet C, Miller JH, Reynolds PE, Zedaker SM. (2006). Designing forest vegetation management strategies based on the mechanisms and dynamics of crop tree competition by neighbouring vegetation. Forestry, 79, 3-27. DOI URL
[6]	Banskota A, Wynne RH, Thomas VA, Serbin SP, Kayastha N, Gastellu-Etchegorry JP, Townsend PA. (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, 2639-2659. DOI URL
[7]	Barbier S, Gosselin F, Balandier P. (2008). Influence of tree species on understory vegetation diversity and mechanisms involved—A critical review for temperate and boreal forests. Forest Ecology and Management, 254, 1-15. DOI URL
[8]	Beaudet M, Harvey BD, Messier C, Coates KD, Poulin JL, Kneeshaw DD, Brais S, Bergeron Y. (2011). Managing understory light conditions in boreal mixedwoods through variation in the intensity and spatial pattern of harvest: a modelling approach. Forest Ecology and Management, 261, 84-94. DOI URL
[9]	Bian ZJ, Qi JB, Wu SB, Wang YS, Wang SY, Xu BD, Du YM, Cao B, Li H, Huang HG. (2021). A review on the development and application of three dimensional computer simulation mode of optical remote sensing. National Remote Sensing Bulletin, 25, 559-576.
	[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 (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, 559-576.]
[10]	Braghiere RK, Wang Y, Doughty R, Sousa D, Magney T, Widlowski JL, Longo M, Bloom AA, Worden J, Gentine P, Frankenberg C. (2021). Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model. Remote Sensing of Environment, 261, 112497. DOI: 10.1016/j.rse.2021.112497. DOI
[11]	Brelsford CC, Nybakken L, Kotilainen TK, Robson TM. (2019). The influence of spectral composition on spring and autumn phenology in trees. Tree Physiology, 39, 925-950. DOI PMID
[12]	Bremer M, Wichmann V, Rutzinger M. (2018). Multi-temporal fine-scale modelling of Larix decidua forest plots using terrestrial LiDAR and hemispherical photographs. Remote Sensing of Environment, 206, 189-204. DOI URL
[13]	Brunner A. (1998). A light model for spatially explicit forest stand models. Forest Ecology and Management, 107, 19-46. DOI URL
[14]	Bye IJ, North PRJ, Los SO, Kljun N, Rosette JAB, Hopkinson C, Chasmer L, Mahoney C. (2017). Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model. Remote Sensing of Environment, 188, 177-189. DOI URL
[15]	Calders K, Origo N, Burt A, Disney M, Nightingale J, Raumonen P, Åkerblom M, Malhi Y, Lewis P. (2018). Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sensing, 10, 933. DOI: 10.3390/rs10060933. DOI
[16]	Canham CD, Coates KD, Bartemucci P, Quaglia S. (1999). Measurement and modeling of spatially explicit variation in light transmission through interior cedar-hemlock forests of British Columbia. Canadian Journal of Forest Research, 29, 1775-1783. DOI URL
[17]	Cescatti A. (1997). Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. I. Model structure and algorithms. Ecological Modelling, 101, 263-274. DOI URL
[18]	Chen JM, Leblanc SG. (1997). A four-scale bidirectional reflectance model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337. DOI URL
[19]	Chen JM, Li X, Nilson T, Strahler A. (2000). Recent advances in geometrical optical modelling and its applications. Remote Sensing Reviews, 18, 227-262. DOI URL
[20]	Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y, 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, 1-15. DOI URL
[21]	Côté JF, Widlowski JL, Fournier RA, Verstraete MM. (2009). The structural and radiative consistency of three- dimensional tree reconstructions from terrestrial lidar. Remote Sensing of Environment, 113, 1067-1081. DOI URL
[22]	Damm A, Paul-Limoges E, Kükenbrink D, Bachofen C, Morsdorf F. (2020). Remote sensing of forest gas exchange: considerations derived from a tomographic perspective. Global Change Biology, 26, 2717-2727. DOI URL
[23]	de Frenne P, Lenoir J, Luoto M, Scheffers BR, Zellweger F, Aalto J, Ashcroft MB, Christiansen DM, Decocq G, de Pauw K, Govaert S, Greiser C, Gril E, Hampe A, Jucker T, et al. (2021). Forest microclimates and climate change: importance, drivers and future research agenda. Global Change Biology, 27, 2279-2297. DOI PMID
[24]	de Wergifosse L, André F, Goosse H, Boczon A, Cecchini S, Ciceu A, Collalti A, Cools N, DʼAndrea E, de Vos B, Hamdi R, Ingerslev M, Knudsen MA, Kowalska A, Leca S, et al. (2022). Simulating tree growth response to climate change in structurally diverse oak and beech forests. Science of the Total Environment, 806, 150422. DOI: 10.1016/j.scitotenv.2021.150422. DOI
[25]	Deussen O. (2003). A framework for geometry generation and rendering of plants with applications in landscape architecture. Landscape and Urban Planning, 64, 105-113. DOI URL
[26]	Disney MI, Lewis P, North PRJ. (2000). Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18, 163-196. DOI URL
[27]	Dixon RK, Solomon AM, Brown S, Houghton RA, Trexier MC, Wisniewski J. (1994). Carbon pools and flux of global forest ecosystems. Science, 263, 185-190. DOI PMID
[28]	Duffy JP, Anderson K, Fawcett D, Curtis RJ, MacLean IMD. (2021). Drones provide spatial and volumetric data to deliver new insights into microclimate modelling. Landscape Ecology, 36, 685-702. DOI URL
[29]	Fang HL, Liang SL. (2005). A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sensing of Environment, 94, 405-424. DOI URL
[30]	Fang J, Chen A, Peng C, Zhao S, Ci L. (2001). Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 292, 2320-2322. DOI PMID
[31]	Ferreira MP, Féret JB, Grau E, Gastellu-Etchegorry JP, do Amaral CH, Shimabukuro YE, de Souza Filho CR (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 URL
[32]	Finisdore J, Rhodes C, Haines-Young R, Maynard S, Wielgus J, Dvarskas A, Houdet J, Quétier F, Lamothe KA, Ding H, Soulard F, van Houtven G, Rowcroft P. (2020). The 18 benefits of using ecosystem services classification systems. Ecosystem Services, 45, 101160. DOI: 10.1016/j.ecoser.2020.101160. DOI
[33]	Fischer FJ, Labrière N, Vincent G, Hérault B, Alonso A, Memiaghe H, Bissiengou P, Kenfack D, Saatchi S, Chave J. (2020). A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories. Remote Sensing of Environment, 251, 112056. DOI: 10.1016/j.rse.2020.112056. DOI
[34]	Forrester DI, Tang X. (2016). Analysing the spatial and temporal dynamics of species interactions in mixed- species forests and the effects of stand density using the 3-PG model. Ecological Modelling, 319, 233-254. DOI URL
[35]	Froelich NJ, Grimmond CSB, Schmid HP. (2011). Nocturnal cooling below a forest canopy: model and evaluation. Agricultural and Forest Meteorology, 151, 957-968. DOI URL
[36]	Galen C, Rabenold JJ, Liscum E. (2007). Functional ecology of a blue light photoreceptor: effects of phototropin-1 on root growth enhance drought tolerance in Arabidopsis thaliana. New Phytologist, 173, 91-99. DOI URL
[37]	Gastellu-Etchegorry JP, Demarez V, Pinel V, Zagolski F. (1996). Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58, 131-156. DOI URL
[38]	Gastellu-Etchegorry JP, Lauret N, Yin T, Landier L, Kallel A, Malenovský Z, Al Bitar A, Aval J, Benhmida S, Qi J, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N, 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, 2640-2649. DOI URL
[39]	Gersonde R, Battles JJ, O’Hara KL. (2004). Characterizing the light environment in Sierra Nevada mixed-conifer forests using a spatially explicit light model. Canadian Journal of Forest Research, 34, 1332-1342. DOI URL
[40]	Goel NS, Rozehnal I, Thompson RL. (1991). A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sensing of Environment, 36, 73-104. DOI URL
[41]	Goodenough AA, Brown SD. (2017). DIRSIG5: next-generation remote sensing data and image simulation framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4818-4833. DOI URL
[42]	Govaerts YM, Jacquemoud S, Verstraete MM, Ustin SL. (1996). Three-dimensional radiation transfer modeling in a dicotyledon leaf. Applied Optics, 35, 6585-6598. DOI PMID
[43]	Govaerts YM, Verstraete MM. (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, 493-505. DOI URL
[44]	Govind A. (2014). On the nature of canopy illumination due to differences in elemental orientation and aggregation for radiative transfer. International Journal of Biometeorology, 58, 1803-1809. DOI PMID
[45]	Govind A, Guyon D, Roujean JL, Yauschew-Raguenes N, Kumari J, Pisek J, Wigneron JP. (2013). Effects of canopy architectural parameterizations on the modeling of radiative transfer mechanism. Ecological Modelling, 251, 114-126. DOI URL
[46]	Guan HC, Su YJ, Hu TY, Wang R, Ma Q, Yang QL, Sun XL, Li YM, Jin SC, Zhang J, Ma Q, Liu M, Wu FY, Guo QH. (2020). A novel framework to automatically fuse multiplatform LiDAR data in forest environments based on tree locations. IEEE Transactions on Geoscience and Remote Sensing, 58, 2165-2177. DOI URL
[47]	Guillen-Climent ML, Zarco-Tejada PJ, Berni JAJ, North PRJ, Villalobos FJ. (2012a). Mapping radiation interception in row-structured orchards using 3D simulation and high- resolution airborne imagery acquired from a UAV. Precision Agriculture, 13, 473-500. DOI URL
[48]	Guillen-Climent ML, Zarco-Tejada PJ, Villalobos FJ. (2012b). Estimating radiation interception in an olive orchard using physical models and multispectral airborne imagery. Israel Journal of Plant Sciences, 60, 107-121. DOI URL
[49]	Guo J, Niu Z. (2009). Progress in 3D modeling and visualization of vegetation. Computer Engineering and Applications, 45(10), 26-29. DOI
	[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.]
[50]	Guo QH, Jin SC, Li M, Yang QL, Xu KX, Ju YZ, Zhang J, Xuan J, Liu J, Su YJ, Xu Q, Liu Y. (2020). Application of deep learning in ecological resource research: theories, methods, and challenges. Science China Earth Sciences, 50, 1354-1373.
	[郭庆华, 金时超, 李敏, 杨秋丽, 徐可心, 巨袁臻, 张菁, 宣晶, 刘瑾, 苏艳军, 许强, 刘瑜 (2020). 深度学习在生态资源研究领域的应用: 理论, 方法和挑战. 中国科学: 地球科学, 50, 1354-1373.]
[51]	Gupta R, Sharma LK. (2019). The process-based forest growth model 3-PG for use in forest management: a review. Ecological Modelling, 397, 55-73. DOI URL
[52]	Härdtle W, von Oheimb G, Westphal C. (2003). The effects of light and soil conditions on the species richness of the ground vegetation of deciduous forests in northern Germany (Schleswig-Holstein). Forest Ecology and Management, 182, 327-338. DOI URL
[53]	Hernández-Clemente R, North PRJ, Hornero A, Zarco-Tejada PJ. (2017). Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193, 165-179. DOI URL
[54]	Hornero A, North PRJ, Zarco-Tejada PJ, Rascher U, Martín MP, Migliavacca M, Hernandez-Clemente R. (2021). Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data. Remote Sensing of Environment, 253, 112195. DOI: 10.1016/j.rse.2020.112195. DOI
[55]	Hu T, Sun X, Su Y, Guan H, Sun Q, Kelly M, Guo Q. (2020). Development and performance evaluation of a very low-cost UAV-Lidar system for forestry applications. Remote Sensing, 13, 77. DOI: 10.3390/rs13010077. DOI
[56]	Huang H, Qin W, Liu Q. (2013). RAPID: a radiosity applicable to porous individual objects for directional reflectance over complex vegetated scenes. Remote Sensing of Environment, 132, 221-237. DOI URL
[57]	Janoutová R, Homolová L, Malenovský Z, Hanuš J, Lauret N, Gastellu-Etchegorry JP. (2019). Influence of 3D spruce tree representation on accuracy of airborne and satellite forest reflectance simulated in DART. Forests, 10, 292. DOI: 10.3390/f10030292. DOI
[58]	Jiang J, Weiss M, Liu S, Rochdi N, Baret F. (2020). Speeding up 3D radiative transfer simulations: a physically based metamodel of canopy reflectance dependency on wavelength, leaf biochemical composition and soil reflectance. Remote Sensing of Environment, 237, 111614. DOI: 10.1016/j.rse.2019.111614. DOI
[59]	Jin DC, Qi JB, Huang HG, Li LY. (2021). Combining 3D radiative transfer model and convolutional neural network to accurately estimate forest canopy cover from very high- resolution satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10953-10963. DOI URL
[60]	Jonard M, André F, de Coligny F, de Wergifosse L, Beudez N, Davi H, Ligot G, Ponette Q, Vincke C. (2020). HETEROFOR 1.0: a spatially explicit model for exploring the response of structurally complex forests to uncertain future conditions—Part 1: carbon fluxes and tree dimensional growth. Geoscientific Model Development, 13, 905-935. DOI URL
[61]	Jurdao S, Yebra M, Guerschman JP, Chuvieco E. (2013). Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sensing of Environment, 132, 59-70. DOI URL
[62]	Kajiya JT. (1986). The rendering equation. Proceedings of the 13^th Annual Conference on Computer Graphics and Interactive Techniques, 20, 143-150.
[63]	Kim HS, Palmroth S, Thérézien M, Stenberg P, Oren R, Niinemets Ü. (2011). Analysis of the sensitivity of absorbed light and incident light profile to various canopy architecture and stand conditions. Tree Physiology, 31, 30-47. DOI URL
[64]	Kimes D, Gastellu-Etchegorry J, Estève P. (2002). Recovery of forest canopy characteristics through inversion of a complex 3D model. Remote Sensing of Environment, 79, 320-328. DOI URL
[65]	Klir GJ. (1991). A review of: “The Algorithmic Beauty of Plants” by Przemyslaw Prusinkiewicz and Aristid Lindenmayer, Springer-Verlag, New York, 1990. International Journal of General Systems, 18, 407-408. DOI URL
[66]	Knyazikhin Y, Kranigk J, Myneni RB, Panfyorov O, Gravenhorst G. (1998). Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies. Journal of Geophysical Research: Atmospheres, 103, 6133-6144. DOI URL
[67]	Kobayashi H, Baldocchi DD, Ryu Y, Chen Q, Ma S, Osuna JL, Ustin SL. (2012). Modeling energy and carbon fluxes in a heterogeneous oak woodland: a three-dimensional approach. Agricultural and Forest Meteorology, 152, 83-100. DOI URL
[68]	Kobayashi H, 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, 173-185. DOI URL
[69]	Koetz B, Sun GQ, Morsdorf F, Ranson KJ, Kneubühler M, Itten K, Allgöwer B. (2007). Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization. Remote Sensing of Environment, 106, 449-459. DOI URL
[70]	Koop H, Sterck FJ. (1994). Light penetration through structurally complex forest canopies: an example of a lowland tropical rainforest. Forest Ecology and Management, 69, 111-122. DOI URL
[71]	Kötz B, Schaepman M, Morsdorf F, Bowyer P, Itten K, Allgöwer B. (2004). Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sensing of Environment, 92, 332-344. DOI URL
[72]	Landsberg JJ, Waring RH, Coops NC. (2003). Performance of the forest productivity model 3-PG applied to a wide range of forest types. Forest Ecology and Management, 172, 199-214. DOI URL
[73]	le Maire G, Nouvellon Y, Christina M, Ponzoni FJ, Gonçalves JLM, Bouillet JP, Laclau JP. (2013). Tree and stand light use efficiencies over a full rotation of single- and mixed- species Eucalyptus grandis and Acacia mangium plantations. Forest Ecology and Management, 288, 31-42. DOI URL
[74]	Le Roux X, Gauthier H, Bégué A, Sinoquet H. (1997). Radiation absorption and use by humid savanna grassland: assessment using remote sensing and modelling. Agricultural and Forest Meteorology, 85, 117-132. DOI URL
[75]	Lefsky MA, Cohen WB, Parker GG, Harding DJ. (2002). Lidar remote sensing for ecosystem studies: lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience, 52, 19-30. DOI URL
[76]	Lewis P. (1999). Three-dimensional plant modelling for remote sensing simulation studies using the Botanical Plant Modelling System. Agronomie, 19, 185-210. DOI URL
[77]	Li L, Mu X, Qi J, Pisek J, Roosjen P, Yan G, Huang H, Liu S, Baret F. (2021). Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV- based multiangular images. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 263-278. DOI URL
[78]	Li WJ, Fang HL. (2015). Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground-based estimates over six FLUXNET sites. Journal of Geophysical Research: Biogeosciences, 120, 96-112. DOI URL
[79]	Li WK, Guo QH, Tao SL, Su YJ. (2018). VBRT: a novel voxel- based radiative transfer model for heterogeneous three- dimensional forest scenes. Remote Sensing of Environment, 206, 318-335. DOI URL
[80]	Li X, Strahler AH. (1985). Geometric-optical modeling of a conifer forest canopy. IEEE Transactions on Geoscience and Remote Sensing, 23, 705-721.
[81]	Liang S, Cheng J, Jia K, Jiang B, Liu Q, Liu S, Xiao Z, Xie X, Yao Y, Yuan W, Zhang X, Zhao X. (2016a). Recent progress in land surface quantitative remote sensing. Journal of Remote Sensing, 20, 875-898.
[82]	Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F, Holopainen M, Vastaranta M. (2016b). Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 63-77. DOI URL
[83]	Lieffers VJ, Messier C, Stadt KJ, Gendron F, Comeau PG. (1999). Predicting and managing light in the understory of boreal forests. Canadian Journal of Forest Research, 29, 796-811. DOI URL
[84]	Ligot G, Balandier P, Courbaud B, Claessens H. (2014). Forest radiative transfer models: Which approach for which application? Canadian Journal of Forest Research, 44, 391-403. DOI URL
[85]	Linkosalo T, Lechowicz MJ. (2006). Twilight far-red treatment advances leaf bud burst of silver birch (Betula pendula). Tree Physiology, 26, 1249-1256. PMID
[86]	Liu QH, Cao B, Zeng YL, Li J, Du YM, Wen JG, Fan WL, Zhao J, Yang L. (2016). Recent progresses on the remote sensing radiative transfer modeling over heterogeneous vegetation canopy. Journal of Remote Sensing, 20, 933-945.
	[柳钦火, 曹彪, 曾也鲁, 李静, 杜永明, 闻建光, 范渭亮, 赵静, 杨乐 (2016). 植被遥感辐射传输建模中的异质性研究进展. 遥感学报, 20, 933-945.]
[87]	Liu RY, Ren HZ, Liu SH, Liu Q, Li XW. (2015). Modelling of fraction of absorbed photosynthetically active radiation in vegetation canopy and its validation. Biosystems Engineering, 133, 81-94. DOI URL
[88]	Lu XL, Chen M, Liu YL, Miralles DG, Wang FM. (2017). Enhanced water use efficiency in global terrestrial ecosystems under increasing aerosol loadings. Agricultural and Forest Meteorology, 237-238, 39-49.
[89]	Ma L, Zheng G, Ying Q, Hancock S, Ju W, Yu D. (2021). Characterizing the three-dimensional spatiotemporal variation of forest photosynthetically active radiation using terrestrial laser scanning data. Agricultural and Forest Meteorology, 301-302, 108346. DOI: 10.1016/j.agrformet.2021.108346. DOI
[90]	Maclean IMD, Mosedale JR, Bennie JJ. (2019). Microclima: an r package for modelling meso- and microclimate. Methods in Ecology and Evolution, 10, 280-290. DOI
[91]	Malenovský Z, Regaieg O, Yin T, Lauret N, Guilleux J, Chavanon E, Duran N, Janoutová R, Delavois A, Meynier J, Medjdoub G, Yang P, van der Tol C, Morton D, Cook BD, Gastellu-Etchegorry JP. (2021). Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: structural impacts in geometrically explicit vegetation canopies. Remote Sensing of Environment, 263, 112564. DOI: 10.1016/j.rse.2021.112564. DOI
[92]	Mariscal MJ, Martens SN, Ustin SL, Chen J, Weiss SB, Roberts DA. (2004). Light-transmission profiles in an old-growth forest canopy: simulations of photosynthetically active radiation by using spatially explicit radiative transfer models. Ecosystems, 7, 454-467.
[93]	Messier C, Doucet R, Ruel JC, Claveau Y, Kelly C, Lechowicz MJ. (1999). Functional ecology of advance regeneration in relation to light in boreal forests. Canadian Journal of Forest Research, 29, 812-823. DOI URL
[94]	Miller JR, Steven MD, Demetriades-Shah TH. (1992). Reflection of layered bean leaves over different soil backgrounds: measured and simulated spectra. International Journal of Remote Sensing, 13, 3273-3286. DOI URL
[95]	Miraglio T, Adeline K, Huesca M, Ustin S, Briottet X. (2020). Monitoring LAI, chlorophylls, and carotenoids content of a woodland savanna using hyperspectral imagery and 3D radiative transfer modeling. Remote Sensing, 12, 28. DOI: 10.3390/rs12010028. DOI
[96]	Mohammed GH, Colombo R, Middleton EM, Rascher U, van der Tol C, Nedbal L, Goulas Y, Pérez-Priego O, Damm A, Meroni M, Joiner J, Cogliati S, Verhoef W, Malenovský Z, Gastellu-Etchegorry JP, et al. (2019). Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sensing of Environment, 231, 111177. DOI: 10.1016/j.rse.2019.04.030. DOI
[97]	Monsi M, Saeki T. (2005). On the factor light in plant communities and its importance for matter production. Annals of Botany, 95, 549-567. DOI URL
[98]	Moon Z, Fuentes JD, Staebler RM. (2020). Impacts of spectrally resolved irradiance on photolysis frequency calculations within a forest canopy. Agricultural and Forest Meteorology, 291, 108012. DOI: 10.1016/j.agrformet.2020.108012. DOI
[99]	Morsdorf F, Nichol C, Malthus T, Woodhouse IH. (2009). Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sensing of Environment, 113, 2152-2163. DOI URL
[100]	Myneni RB. (1991). Modeling radiative transfer and photosynthesis in three-dimensional vegetation canopies. Agricultural and Forest Meteorology, 55, 323-344. DOI URL
[101]	Ni WG, Li XW, Woodcock CE, Caetano MR, Strahler AH. (1999). An analytical hybrid GORT model for bidirectional reflectance over discontinuous plant canopies. IEEE Transactions on Geoscience and Remote Sensing, 37, 987-999. DOI URL
[102]	Niyogi D, Chang HI, Saxena VK, Holt T, Alapaty K, Booker F, Chen F, Davis KJ, Holben B, Matsui T, Meyers T, Oechel WC, Pielke RA, Wells R, Wilson K, Xue YK. (2004). Direct observations of the effects of aerosol loading on net ecosystem CO₂ exchanges over different landscapes. Geophysical Research Letters, 31, L20506. DOI: 10.1029/2004GL020915. DOI
[103]	Norouzi S, Sadeghi M, Liaghat A, Tuller M, Jones SB, Ebrahimian H. (2021). Information depth of NIR/SWIR soil reflectance spectroscopy. Remote Sensing of Environment, 256, 112315. DOI: 10.1016/j.rse.2021.112315. DOI
[104]	North PRJ. (1996). Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34, 946-956. DOI URL
[105]	Oker-Blom P, Kaufmann MR, Ryan MG. (1991). Performance of a canopy light interception model for conifer shoots, trees and stands. Tree Physiology, 9, 227-243. PMID
[106]	Olsen JE. (2010). Light and temperature sensing and signaling in induction of bud dormancy in woody plants. Plant Molecular Biology, 73, 37-47. DOI PMID
[107]	Pacala SW, Canham CD, Saponara J, Silander Jr JA, Kobe RK, Ribbens E. (1996). Forest models defined by field measurements: estimation, error analysis and dynamics. Ecological Monographs, 66, 1-43. DOI URL
[108]	Planchais I, Sinoquet H. (1998). Foliage determinants of light interception in sunny and shaded branches of Fagus sylvatica (L.). Agricultural and Forest Meteorology, 89, 241-253. DOI URL
[109]	Plekhanova E, Niklaus PA, Gastellu-Etchegorry JP, Schaepman- Strub G (2021). How does leaf functional diversity affect the light environment in forest canopies? An in-silico biodiversity experiment. Ecological Modelling, 440, 109394. DOI: 10.1016/j.ecolmodel.2020.109394. DOI
[110]	Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel EE, Moreno J, Frankenberg C, Berry JA. (2014). Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65, 4065-4095. DOI PMID
[111]	Pradal C, Boudon F, Nouguier C, Chopard J, Godin C. (2009). PlantGL: a Python-based geometric library for 3D plant modelling at different scales. Graphical Models, 71, 1-21. DOI URL
[112]	Pretzsch H, Forrester DI, Rötzer T. (2015). Representation of species mixing in forest growth models. A review and perspective. Ecological Modelling, 313, 276-292. DOI URL
[113]	Pukkala T, Kuuluvainen T, Stenberg P. (1993). Below-Canopy distribution of photosynthetically active radiation and its relation to seedling growth in a boreal Pinus sylvestris stand—A simulation approach. Scandinavian Journal of Forest Research, 8, 313-325. DOI URL
[114]	Qi J, Xie D, Yin T, Yan G, Gastellu-Etchegorry JP, Li L, Zhang W, Mu X, Norford LK. (2019a). LESS: large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221, 695-706. DOI URL
[115]	Qi J, Yin T, Xie D, Gastellu-Etchegorry JP. (2019b). Hybrid scene structuring for accelerating 3D radiative transfer simulations. Remote Sensing, 11, 2637. DOI: 10.3390/rs11222637. DOI
[116]	Qin WH, Gerstl SAW. (2000). 3-D scene modeling of semidesert vegetation cover and its radiation regime. Remote Sensing of Environment, 74, 145-162. DOI URL
[117]	Regaieg O, Yin T, Malenovský Z, Cook BD, Morton DC, Gastellu-Etchegorry JP. (2021). Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART. Remote Sensing of Environment, 265, 112673. DOI: 10.1016/j.rse.2021.112673. DOI
[118]	Ross J. (1981). The radiation regime and architecture of plant stands//Lieth H. Tasks for Vegetation Sciences. Dr W. Junk Publishers, the Hague, the Netherlands.
[119]	Schlerf M, Atzberger C. (2006). Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sensing of Environment, 100, 281-294. DOI URL
[120]	Schneider FD, Leiterer R, Morsdorf F, Gastellu-Etchegorry JP, Lauret N, Pfeifer N, Schaepman ME. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152, 235-250. DOI URL
[121]	Schott JR, Brown SD, Raque RV, Gross HN, Robinson G. (1999). Advanced synthetic image generation models and their application to multi/hyperspectral algorithm development. Proceedings of the SPIE, 3584, 211-220.
[122]	Sinoquet H, Le Roux X, Adam B, Ameglio T, Daudet FA. (2001). RATP: a model for simulating the spatial distribution of radiation absorption, transpiration and photosynthesis within canopies: application to an isolated tree crown. Plant, Cell & Environment, 24, 395-406.
[123]	Sprugel DG, Rascher KG, Gersonde R, Dovčiak M, Lutz JA, Halpern CB. (2009). Spatially explicit modeling of overstory manipulations in young forests: effects on stand structure and light. Ecological Modelling, 220, 3565-3575. DOI URL
[124]	Stadt KJ, Lieffers VJ. (2000). MIXLIGHT: a flexible light transmission model for mixed-species forest stands. Agricultural and Forest Meteorology, 102, 235-252. DOI URL
[125]	Tian H, Chen G, Liu M, Zhang C, Sun G, Lu C, Xu X, Ren W, Pan S, Chappelka A. (2010). Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895-2007. Forest Ecology and Management, 259, 1311-1327. DOI URL
[126]	Tong CM, Bao YF, Zhao F, Fan CR, Li ZJ, Huang QL. (2021). Evaluation of the FluorWPS model and study of the parameter sensitivity for simulating solar-induced chlorophyll fluorescence. Remote Sensing, 13, 1091. DOI: 10.3390/rs13061091. DOI
[127]	van der Tol C, Verhoef W, Timmermans J, Verhoef A, Su Z. (2009). An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6, 3109-3129. DOI URL
[128]	van der Zande D, Stuckens J, Verstraeten WW, Mereu S, Muys B, Coppin P. (2011). 3D modeling of light interception in heterogeneous forest canopies using ground-based LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 13, 792-800. DOI URL
[129]	Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera JP, Veroustraete F, Clevers JGPW, Moreno J. (2015). Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273-290. DOI URL
[130]	Verrelst J, Sabater N, Rivera JP, Muñoz-Marí J, Vicent J, Camps-Valls G, Moreno J. (2016). Emulation of leaf, canopy and atmosphere radiative transfer models for fast global sensitivity analysis. Remote Sensing, 8, 673. DOI: 10.3390/rs8080673. DOI
[131]	Vincent G, Antin C, Laurans M, Heurtebize J, Durrieu S, Lavalley C, Dauzat J. (2017). Mapping plant area index of tropical evergreen forest by airborne laser scanning. A cross-validation study using LAI2200 optical sensor. Remote Sensing of Environment, 198, 254-266. DOI URL
[132]	Wang B, Wang ZH, Wang CZ, Wang X, Li J, Jia Z, Li P, Wu J, Chen M, Liu LL. (2021). Field evidence reveals conservative water use of poplar saplings under high aerosol conditions. Journal of Ecology, 109, 2190-2202. DOI URL
[133]	Wang X, Wu J, Chen M, Xu XT, Wang ZH, Wang B, Wang CZ, Piao SL, Lin WL, Miao GF, Deng MF, Qiao CL, Wang J, Xu S, Liu LL. (2018). Field evidences for the positive effects of aerosols on tree growth. Global Change Biology, 24, 4983-4992. DOI PMID
[134]	Wang ZH, Wang CZ, Wang B, Wang X, Li J, Wu J, Liu LL. (2020). Interactive effects of air pollutants and atmospheric moisture stress on aspen growth and photosynthesis along an urban-rural gradient. Environmental Pollution, 260, 114076. DOI: 10.1016/j.envpol.2020.114076. DOI
[135]	Webster C, Mazzotti G, Essery R, Jonas T. (2020). Enhancing airborne LiDAR data for improved forest structure representation in shortwave transmission models. Remote Sensing of Environment, 249, 112017. DOI: 10.1016/j.rse.2020.112017. DOI
[136]	Widlowski JL, Lavergne T, Pinty B, Verstraete M, Gobron N. (2006a). Rayspread: a virtual laboratory for rapid BRF simulations over 3-D plant canopies//Graziani F. Computational Methods in Transport. Springer, Berlin, Heidelberg.
[137]	Widlowski JL, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R, Côté JF, Eenmäe A, Essery R, Gastellu- Etchegorry JP, et al. (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 URL
[138]	Widlowski JL, Pinty B, Lavergne T, Verstraete MM, Gobron N. (2006b). Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: simulated impact on canopy absorption. Remote Sensing of Environment, 103, 379-397. DOI URL
[139]	Wojnowski W, Wei SS, Li WJ, Yin TG, Li XX, Ow GLF, Mohd Yusof ML, Whittle AJ. (2021). Comparison of absorbed and intercepted fractions of PAR for individual trees based on radiative transfer model simulations. Remote Sensing, 13, 1069. DOI: 10.3390/rs13061069. DOI
[140]	Xiao YF, Zhou DM, Zhao WJ. (2013). Review of inversing biophysical and biochemical vegetation parameters in various spatial scales using radiative transfer models. Acta Ecologica Sinica, 33, 3291-3297. DOI URL
	[肖艳芳, 周德民, 赵文吉 (2013). 辐射传输模型多尺度反演植被理化参数研究进展. 生态学报, 33, 3291-3297.]
[141]	Xie DH, Sun R, Zhu QJ, Wang JD, Wu MX, Qin WH. (2006). Reflectance distribution of corn canopies simulated with radiosity-graphics combined model. Acta Agronomica Sinica, 32, 317-323.
	[谢东辉, 孙睿, 朱启疆, 王锦地, 吴门新, 覃文汉 (2006). 利用辐射度模型模拟玉米冠层辐射分布. 作物学报, 32, 317-323.]
[142]	Yan GJ, Hu RH, Luo JH, Weiss M, Jiang HL, Mu XH, Xie DH, Zhang WM. (2019). Review of indirect optical measurements of leaf area index: recent advances, challenges, and perspectives. Agricultural and Forest Meteorology, 265, 390-411. DOI URL
[143]	Yang P, van der Tol C, Campbell PKE, Middleton EM. (2021). Unraveling the physical and physiological basis for the solar- induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop. Biogeosciences, 18, 441-465. DOI URL
[144]	Yang P, van der Tol C, Yin T, Verhoef W. (2020). The SPART model: a soil-plant-atmosphere radiative transfer model for satellite measurements in the solar spectrum. Remote Sensing of Environment, 247, 111870. DOI: 10.1016/j.rse.2020.111870. DOI
[145]	Yang X, Wang Y, Yin T, Wang C, Lauret N, Regaieg O, Xi X, Gastellu-Etchegorry JP. (2022). Comprehensive LiDAR simulation with efficient physically-based DART-Lux model (I): theory, novelty, and consistency validation. Remote Sensing of Environment, 272, 112952. DOI: 10.1016/j.rse.2022.112952. DOI
[146]	Yuan H, Dickinson RE, Dai YJ, Shaikh MJ, Zhou LM, Shangguan W, Ji DY. (2014). A 3D canopy radiative transfer model for global climate modeling: description, validation, and application. Journal of Climate, 27, 1168-1192. DOI URL
[147]	Zarco-Tejada PJ, Catalina A, González MR, Martín P. (2013). Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery. Remote Sensing of Environment, 136, 247-258. DOI URL
[148]	Zarco-Tejada PJ, Miller JR, Harron J, Hu BX, Noland TL, Goel N, Mohammed GH, Sampson P. (2004). Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sensing of Environment, 89, 189-199. DOI URL
[149]	Zellweger F, de Frenne P, Lenoir J, Rocchini D, Coomes D. (2019). Advances in microclimate ecology arising from remote sensing. Trends in Ecology & Evolution, 34, 327-341. DOI URL
[150]	Zhan CH, Zhang ZY, Zhang YG. (2020). Recent advances in the radiative transfer models of sun-induced chlorophyll fluorescence. Journal of Remote Sensing (Chinese), 24, 945-957.
	[詹春晖, 章钊颖, 张永光 (2020). 日光诱导叶绿素荧光辐射传输模型研究进展. 遥感学报, 24, 945-957.]
[151]	Zhang ZY, Wang SH, Qiu B, Song L, Zhang YG. (2019). Retrieval of sun-induced chlorophyll fluorescence and vancements in carbon cycle application. Journal of Remote Sensing (Chinese), 23, 37-52.
	[章钊颖, 王松寒, 邱博, 宋练, 张永光 (2019). 日光诱导叶绿素荧光遥感反演及碳循环应用进展. 遥感学报, 23, 37-52.]

原理 Principle	模型 Model	基本单元 Component	参考文献 Reference
比尔定律 Beer’s law	MIXLIGHT	几何体 Geometric primitives	Stadt & Lieffers, 2000
	OLTREE	几何体 Geometric primitives	Mariscal et al., 2004
	SILVI-STAR	几何体 Geometric primitives	Koop & Sterck, 1994
	tRAYci	几何体 Geometric primitives	Brunner, 1998
	FOREST	几何体 Geometric primitives	Cescatti, 1997
光线追踪 Ray-tracing	FLIGHT	体素 Voxel	North, 1996
	FLiES	几何体 Geometric primitives	Kobayashi & Iwabuchi, 2008
	Librat	面元 Facet	Lewis, 1999
	Raytran	面元、几何体 Facet, geometric primitives	Govaerts & Verstraete, 1998
	Rayspread	面元、几何体 Facet, geometric primitives	Widlowski et al., 2006a
	DART	体素、面元 Voxel, facet	Gastellu-Etchegorry et al., 1996
	DIRSIG	体素、面元 Voxel, facet	Schott et al., 1999
	VBRT	体素 Voxel	Li et al., 2018
	LESS	面元 Facet	Qi, et al., 2019a
辐射度 Radiosity	DIANA	面元 Facet	Goel et al., 1991
	RGM, TRGM	面元 Facet	Qin & Gerstl, 2000
	RAPID	面元 Facet	Huang et al., 2013

原理 Principle	模型 Model	基本单元 Component	参考文献 Reference
比尔定律 Beer’s law	MIXLIGHT	几何体 Geometric primitives	Stadt & Lieffers, 2000
	OLTREE	几何体 Geometric primitives	Mariscal et al., 2004
	SILVI-STAR	几何体 Geometric primitives	Koop & Sterck, 1994
	tRAYci	几何体 Geometric primitives	Brunner, 1998
	FOREST	几何体 Geometric primitives	Cescatti, 1997
光线追踪 Ray-tracing	FLIGHT	体素 Voxel	North, 1996
	FLiES	几何体 Geometric primitives	Kobayashi & Iwabuchi, 2008
	Librat	面元 Facet	Lewis, 1999
	Raytran	面元、几何体 Facet, geometric primitives	Govaerts & Verstraete, 1998
	Rayspread	面元、几何体 Facet, geometric primitives	Widlowski et al., 2006a
	DART	体素、面元 Voxel, facet	Gastellu-Etchegorry et al., 1996
	DIRSIG	体素、面元 Voxel, facet	Schott et al., 1999
	VBRT	体素 Voxel	Li et al., 2018
	LESS	面元 Facet	Qi, et al., 2019a
辐射度 Radiosity	DIANA	面元 Facet	Goel et al., 1991
	RGM, TRGM	面元 Facet	Qin & Gerstl, 2000
	RAPID	面元 Facet	Huang et al., 2013

参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
结构参数 Structure parameter	叶面积指数 Leaf area index	光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 Deciduous broad- leaved forest (DBF)	DART	R² = 0.99 RMSE = 0.2	Kimes et al., 2002
		全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.68 RMSE = 1.23	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	混交林 Mixed forest	DART	R² = 0.6 RMSE = 0.47	Banskota et al., 2013
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.8 RMSE = 0.22	Miraglio et al., 2020
		多光谱影像 Multispectral image	美国、加拿大 USA, Canada	落叶阔叶林、常绿针叶林 DBF, evergreen coniferou forest (ECF)	LIBERTY, GeoSAIL	R² = 0.98 RMSE = 0.24	Fang & Liang, 2005
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	-	模拟场景 Simulated scenes	POVRAY	R² = 0.99 RMSE = 0.046 7	Morsdorf et al., 2009
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.74 MAE = 3.8	Bye et al., 2017
	森林覆盖度 Canopy cover	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.84 RMSE = 0.07	Koetz et al., 2007
		全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.5 MAE = 0.1	Bye et al., 2017
		光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 DBF	DART	R² = 0.99 RMSE = 0.011	Kimes et al., 2002
		多光谱影像 Multispectral image	中国 China	针叶林 Coniferous forest	RAPID	R² = 0.832 RMSE = 0.121	Jin et al., 2021
生化参数 Biochemical parameter	叶绿素含量 Chlorophyll content	高光谱影像 Hyperspectral image	加拿大 Canada	针叶林 Coniferous forest	PROSPECT, SPRINT	R² = 0.4 RMSE = 8.1	Zarco-Tejada et al., 2004
		全波形激光雷达数、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.86 RMSE = 5.06	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.73 RMSE = 5.21	Miraglio et al., 2020
	叶片含水量 Leaf water content	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.79 RMSE = 0.005 3	Koetz et al., 2007
		多光谱影像 Multispectral image	西班牙 Spain	常绿阔叶林、落叶阔叶林、常绿针叶林 Evergreen broad-leaved forest (EBF), DBF, ECF	PROSPECT, GeoSAIL	R² = 0.5	Jurdao et al., 2013
		多光谱影像 Multispectral image	瑞士 Swiss	针叶林 Coniferous forest	GeoSAIL	RMSE = 0.013	K?tz et al., 2004
能量参数 Energy parameter	光合有效辐射吸收比例 Fraction of absorbed photosynthetic active radiation	光谱反射率数据 Spectral reflectance data	-	模拟场景 Simulated scenes	PARCINOPY	RMSE = 0.008 3	Combal et al., 2003
		多光谱影像 Multispectral image	美国、丹麦、德国 USA, Denmark, Germany	常绿阔叶林、落叶阔叶林、常绿针叶林 EBF, DBF, ECF	4SAIL2	R² = 0.72 RMSE = 0.07	Li & Fang, 2015
		实测数据 In-situ data	圭亚那 Guinea	稀树草原 Savanna	RIRI-3D	R² = 0.99 RMSE = 0.057	Le Roux et al., 1997
参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
	冠层截获光合有效辐射 Fraction of intercepted photosynthetically active radiation	多光谱影像 Multispectral image	西班牙 Spain	橄榄园 Olea europaea grove	ORIM, FLIGHT	R² = 0.83 RMSE = 0.05	Guillen- Climent et al., 2012b
		多光谱影像 Multispectral image	西班牙 Spain	桃园、柑橘园 Prunus persica and Citrus reticulata grove	FLIGHT	R² = 0.88 RMSE = 0.09	Guillen- Climent et al., 2012a
生态功能参数 Ecological function parameter	日光诱导叶绿素荧光 Solar-induced chlorophyll fluorescence	实测数据 In-situ data	美国 USA	混交林 Mixed forest	FluorWPS	R²= 0.998 RMSE = 1.85	Tong et al., 2021
			美国 USA	虚拟场景、常绿针叶林 Simulated scenes, ECF	CliMA-RT	R² = 0.85 RMSE = 0.18	Braghiere et al., 2021
			西班牙 Spain	阔叶林-草地 Mixed broad-leaved forest and grass	FluorFLIGHT	R² = 0.83 RMSE = 0.03	Hornero et al., 2021

参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
结构参数 Structure parameter	叶面积指数 Leaf area index	光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 Deciduous broad- leaved forest (DBF)	DART	R² = 0.99 RMSE = 0.2	Kimes et al., 2002
		全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.68 RMSE = 1.23	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	混交林 Mixed forest	DART	R² = 0.6 RMSE = 0.47	Banskota et al., 2013
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.8 RMSE = 0.22	Miraglio et al., 2020
		多光谱影像 Multispectral image	美国、加拿大 USA, Canada	落叶阔叶林、常绿针叶林 DBF, evergreen coniferou forest (ECF)	LIBERTY, GeoSAIL	R² = 0.98 RMSE = 0.24	Fang & Liang, 2005
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	-	模拟场景 Simulated scenes	POVRAY	R² = 0.99 RMSE = 0.046 7	Morsdorf et al., 2009
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.74 MAE = 3.8	Bye et al., 2017
	森林覆盖度 Canopy cover	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.84 RMSE = 0.07	Koetz et al., 2007
		全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.5 MAE = 0.1	Bye et al., 2017
		光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 DBF	DART	R² = 0.99 RMSE = 0.011	Kimes et al., 2002
		多光谱影像 Multispectral image	中国 China	针叶林 Coniferous forest	RAPID	R² = 0.832 RMSE = 0.121	Jin et al., 2021
生化参数 Biochemical parameter	叶绿素含量 Chlorophyll content	高光谱影像 Hyperspectral image	加拿大 Canada	针叶林 Coniferous forest	PROSPECT, SPRINT	R² = 0.4 RMSE = 8.1	Zarco-Tejada et al., 2004
		全波形激光雷达数、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.86 RMSE = 5.06	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.73 RMSE = 5.21	Miraglio et al., 2020
	叶片含水量 Leaf water content	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.79 RMSE = 0.005 3	Koetz et al., 2007
		多光谱影像 Multispectral image	西班牙 Spain	常绿阔叶林、落叶阔叶林、常绿针叶林 Evergreen broad-leaved forest (EBF), DBF, ECF	PROSPECT, GeoSAIL	R² = 0.5	Jurdao et al., 2013
		多光谱影像 Multispectral image	瑞士 Swiss	针叶林 Coniferous forest	GeoSAIL	RMSE = 0.013	K?tz et al., 2004
能量参数 Energy parameter	光合有效辐射吸收比例 Fraction of absorbed photosynthetic active radiation	光谱反射率数据 Spectral reflectance data	-	模拟场景 Simulated scenes	PARCINOPY	RMSE = 0.008 3	Combal et al., 2003
		多光谱影像 Multispectral image	美国、丹麦、德国 USA, Denmark, Germany	常绿阔叶林、落叶阔叶林、常绿针叶林 EBF, DBF, ECF	4SAIL2	R² = 0.72 RMSE = 0.07	Li & Fang, 2015
		实测数据 In-situ data	圭亚那 Guinea	稀树草原 Savanna	RIRI-3D	R² = 0.99 RMSE = 0.057	Le Roux et al., 1997
参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
	冠层截获光合有效辐射 Fraction of intercepted photosynthetically active radiation	多光谱影像 Multispectral image	西班牙 Spain	橄榄园 Olea europaea grove	ORIM, FLIGHT	R² = 0.83 RMSE = 0.05	Guillen- Climent et al., 2012b
		多光谱影像 Multispectral image	西班牙 Spain	桃园、柑橘园 Prunus persica and Citrus reticulata grove	FLIGHT	R² = 0.88 RMSE = 0.09	Guillen- Climent et al., 2012a
生态功能参数 Ecological function parameter	日光诱导叶绿素荧光 Solar-induced chlorophyll fluorescence	实测数据 In-situ data	美国 USA	混交林 Mixed forest	FluorWPS	R²= 0.998 RMSE = 1.85	Tong et al., 2021
			美国 USA	虚拟场景、常绿针叶林 Simulated scenes, ECF	CliMA-RT	R² = 0.85 RMSE = 0.18	Braghiere et al., 2021
			西班牙 Spain	阔叶林-草地 Mixed broad-leaved forest and grass	FluorFLIGHT	R² = 0.83 RMSE = 0.03	Hornero et al., 2021

三维辐射传输模型在森林生态系统研究中的应用与展望

Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities

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