真实和有效叶面积指数及聚集指数的尺度效应

doi:10.12082/dqxxkx.2021.200609

摘要/Abstract

摘要：

尺度效应是地球科学和定量遥感中的重要研究课题,目前的许多研究大多集中在估算尺度效应带来的误差,而对一些关键的植被结构参数是否存在尺度效应及其尺度转换方法尚存在诸多不同见解。本文针对真实和有效叶面积指数（Leaf Area Index, LAI和Effective LAI, LAI_e）以及聚集指数（Clumping Index, CI）3个植被关键结构参数,从基本概念和获取方法上分析参数的尺度效应及其尺度转换方法。从定义上看,LAI并不存在尺度效应,而LAI_e和CI则存在尺度效应,其中CI的尺度效应由LAIe引入（CI=LAI_e/LAI）。在野外实测中,LAI破坏测量法没有尺度效应,但由孔隙率模型获取3个参数的方法均具有尺度效应。异速生长方程和遥感反演方法的尺度效应取决于方法本身的线性或非线性特征。目前全球主要的LAI、LAI_e和CI遥感产品都基于非线性模型获取,其反演过程具有尺度效应。像元尺度的LAI本身并不具有尺度效应,而像元尺度的LAI_e和CI虽然具有尺度效应,但在实践中常常被忽略。因此,实际工作中应注意区分参数概念本身、野外测量、遥感反演方法以及遥感产品等所展示的不同尺度效应。

关键词: 叶面积指数, 真实叶面积指数, 有效叶面积指数, 聚集指数, 尺度效应, 尺度转换

Abstract:

Scaling effects describe the observational differences caused by different observation standards. It is an important research topic in Earth science and quantitative remote sensing. Current studies mainly focus on estimating the errors caused by the scaling effects, but different opinions still exist about the scaling effects in some critical vegetation structural parameters and their scale transformation methods. This paper analyzes the scaling effects and the scale transformation methods of three key vegetation structural parameters, namely Leaf Area Index (LAI), Effective LAI (LAI_e), and Clumping Index (CI), based on their definitions and acquisition methods. By definition, LAI is free from the scaling effects, whereas LAI_e and CI have scaling effects. The scaling effects of CI is introduced by LAI_e (CI=LAI_e/LAI). LAI, LAI_e, and CI can be obtained through field measurement and remote sensing inversion methods. In field measurements, LAI is obtained through the direct destructive method or the indirect optical method. LAI obtained through the destructive method has no scaling effects. The indirect optical method estimates the three parameters based on the Beer-Lambert equation with the canopy gap fraction. LAI-2200, digital hemispherical photography, and photosynthetic active radiation sensors are commonly used instruments. The non-liner gap fraction model has scaling effects in deriving these parameters. Remote sensing technology uses passive optical methods, Light Detection and Ranging (LiDAR) technology, and Synthetic Aperture Radar (SAR) methods to estimate LAI, LAIe, and CI. The classic passive optical methods can be divided into the empirical vegetation index estimation methods and the physical model inversion methods. The vegetation index methods establish an empirical relationship between vegetation structural parameters and vegetation indices. The physical model inversion methods are based on physical radiative transfer models. The scaling effects of the remote sensing methods depend on the linear or nonlinear characteristics of these methods. Currently, the major global LAI, LAI_e, and CI remote sensing products are acquired from nonlinear inversion models, thus these inversion methods are subject to scaling effects. Nevertheless, the nonlinearity in the inversion process does not necessarily mean that the LAI products have scaling effects. The scaling effects of the LAI products still follow the basic LAI definition. Therefore, current remote sensing LAI products do not subject to the scaling effects at pixel level. On the other hand, both LAI_e and CI products do have scaling effects, but the scaling effects are often ignored in practice. In the validation of the remote sensing products, the scaling effects need to be considered while homogeneous areas are preferred in the validation studies. In conclusion, attention should be paid to distinguishing the different scaling effects displayed by the parameters in their definitions, the field measurement and remote sensing inversion methods, and remote sensing products. For the scale transformation, it is more important to investigate the most suitable and efficient method rather than a universal method.

Key words: Leaf Area Index (LAI), true LAI, effective LAI, Clumping Index (CI), scaling effects, scale transformation

方红亮. 真实和有效叶面积指数及聚集指数的尺度效应[J]. 地球信息科学学报, 2021, 23(7): 1155-1168.DOI:10.12082/dqxxkx.2021.200609

FANG Hongliang. Scaling Effects of the True and Effective Leaf Area Index (LAI and LAI_e) and Clumping Index (CI)[J]. Journal of Geo-information Science, 2021, 23(7): 1155-1168.DOI:10.12082/dqxxkx.2021.200609

图/表 2

表1

表B1

参考文献 116

[1]	中国社会科学院语言研究所词典编辑室. 现代汉语词典(第6版)[M]. 北京: 商务印书馆, 2015.
	[Dictionary Editorial Office, Language Institute, Chinese Academy of Social Sciences. Modenrn Chinese Dictionary(the Sixth Edition)[M]. Beijing: The Commercial Press, 2015. ]
[2]	李小文. 地球表面时空多变要素的定量遥感项目综述[J]. 地球科学进展, 2006, 21(8):771-780.
	[ Li X W. Review of the project of quantitative remote sensing of major factors for spatial-temporal heterogeneity on the land surface[J]. Advances in Earth Science, 2006, 21(8):771-780. ]
[3]	李小文, 王祎婷. 定量遥感尺度效应刍议[J]. 地理学报, 2013, 68(9):1163-1169. doi: 10.11821/dlxb201309001
	[ Li X W, Wang Y T. Prospects on future developments of quantitative remote sensing[J]. Acta Geographica Sinica, 2013, 68(9):1163-1169. ]
[4]	苏理宏, 李小文, 黄裕霞. 遥感尺度问题研究进展[J]. 地球科学进展, 2001, 16(4):544-548.
	[ Su L H, Li X W, Huang Y X. An review on scale in remote sensing[J]. Advance in Earth Sciences, 2001, 16(4):544-548. ]
[5]	李新, 晋锐, 刘绍民, 等. 黑河遥感试验中尺度上推研究的进展与前瞻[J]. 遥感学报, 2016, 20(5):921-932.
	[ Li X, Jin R, Liu S M, et al. Upscaling research in HiWATER: Progress and prospects[J]. Journal of Remote Sensing, 2016, 20(5):921-932. ]
[6]	王祎婷, 谢东辉, 李小文. 构造地理要素趋势面的尺度转换普适性方法探讨[J]. 遥感学报, 2014, 18(6):1139-1146.
	[ Wang Y T, Xie D H, Li X W. Universal scaling methodology in remote sensing science by constructing geographic trend surface[J]. Journal of Remote Sensing, 2014, 18(6):1139-1146. ]
[7]	郝大磊, 肖青, 闻建光, 等. 定量遥感升尺度转换方法研究进展[J]. 遥感学报, 2018, 22(3):408-423.
	[ Hao D L, Xiao Q, Wen J G, et al. Advances in upscaling methods of quantitative remote sensing[J]. Journal of Remote Sensing, 2018, 22(3):408-423. ]
[8]	刘良云. 叶面积指数遥感尺度效应与尺度纠正[J]. 遥感学报, 2014, 18(6):1158-1168.
	[ Liu L Y. Simulation and correction of spatialscaling effects for leaf area index[J]. Journal of Remote Sensing, 2014, 18(6):1158-1168. ]
[9]	Fang H, Zhang Y, Wei S, et al. Validation of global moderate resolution Leaf Area Index (LAI) products over croplands in northeastern China[J]. Remote Sensing of Environment, 2019, 111377.
[10]	GCOS, The global observing system for climate: Implementation Needs (GCOS-200). 2016,World Meteorological Organization.
[11]	范闻捷, 盖颖颖, 徐希孺, 等. 遥感反演离散植被有效叶面积指数的空间尺度效应[J]. 中国科学(地球科学), 2013, 43(2):280-286.
	[ Fan W J, Gai Y Y, Xu X R, et al. The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing[J]. Scientia Sinica (Terrae), 2013, 43(2):280-286. ]
[12]	朱小华, 冯晓明, 赵英时, 等. 作物LAI的遥感尺度效应与误差分析[J]. 遥感学报, 2010, 14(3):579-592.
	[ Zhu X H, Feng X M, Zhao Y S, et al. Scale effect and error analysis of crop LAI inversion[J]. Journal of Remote Sensing, 2010, 14(3):579-592. ]
[13]	张仁华, 田静, 李召良, 等. 定量遥感产品真实性检验的基础与方法[J]. 中国科学:地球科学, 2010, 40(2):211-22.
	[ Zhang R H, Tian J, Li Z L, et al. The basis and method of authenticity test of quantitative remote sensing products[J]. Scientia Sinica Terrae, 2010, 40(2):211-222. ]
[14]	欧盛华, 邵杉杉, 杨涛, 等. 遥感尺度效应的数值模拟——以叶面积指数为例[J]. 测绘通报, 2018(8):106-110.
	[ Ou S H, Shao S S, Yang T, et al. Numerical simulation of remote sensing scale effect: A case study for leaf area index[J]. Bulletin of Surveying and Mapping, 2018(8):106-110. ]
[15]	麻庆苗, 李静, 刘强, 等. 混合像元聚集指数研究及尺度分析[J]. 遥感学报, 2012, 16(5):895-908.
	[ Ma Q M, Li J, Liu Q, et al. Mixed-pixel clumping index calculation and scale analysis[J]. Journal of Remote Sensing, 2012, 16(5):895-908. ]
[16]	Chen J. Spatial scaling of a remotely sensed surface parameter by contexture[J]. Remote Sensing of Environment, 1999, 69(1):30-42. doi: 10.1016/S0034-4257(99)00006-1
[17]	Garrigues S, Allard D, Baret F, et al. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data[J]. Remote Sensing of Environment, 2006, 105(4):286-298. doi: 10.1016/j.rse.2006.07.013
[18]	Wu H, Li Z L. Scale issues in remote sensing: A review on analysis, processing and modeling[J]. Sensors (Basel), 2009, 9(3):1768-1793. doi: 10.3390/s90301768
[19]	Greene B. The elegent universe[M]. New York: W. W. Norton & Company, 2010.
[20]	李志林. 地理空间数据处理的尺度理论[J]. 地理信息世界, 2005, 3(2):1-5.
	[ Li Z L. Scale theory of geospatial data processing[J]. Geomatics World, 2005, 3(2):1-5. ]
[21]	吴骅, 姜小光, 习晓环, 等. 两种普适性尺度转换方法比较与分析研究[J]. 遥感学报, 2009, 13(2):183-189.
	[ Wu H, Jiang X G, Xi X H, et al. Comparison and analysis of two general scaling methods for remotely sensed information[J]. Journal of Remote Sensing, 2009, 13(2):183-189. ]
[22]	晋锐, 李新, 马明国, 等. 陆地定量遥感产品的真实性检验关键技术与试验验证[J]. 地球科学进展, 2017, 32(6):630-642.
	[ Jin R, Li X, Ma M G, et al. Key methods and experiment verification for the validation of quantitative remote sensing products[J]. Advances in Earth Science, 2017, 32(6):630-642. ]
[23]	Woodcock C E, Strahler A H. The factor of scale in remote sensing[J]. Remote Sensing of Environment, 1987, 21(3):311-332. doi: 10.1016/0034-4257(87)90015-0
[24]	Chen J M. Spatial scaling of a remotely sensed surface parameter by contexture[J]. Remote Sensing of Environment, 1999, 69(1):30-42. doi: 10.1016/S0034-4257(99)00006-1
[25]	Liu Y, Liu R, Chen J M. Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data[J]. Journal of Geophysical Research - Biogeosciences, 2012, 117(G04003)
[26]	Zhang H K, Chen J M, Huang B, et al. Reconstructing seasonal variation of landsat vegetation index related to leaf area index by fusing with MODIS data[J]. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(3):950-960. doi: 10.1109/JSTARS.2013.2284528
[27]	柳钦火, 曹彪, 曾也鲁, 等. 植被遥感辐射传输建模中的异质性研究进展[J]. 遥感学报, 2016, 20(5):933-945.
	[ Liu Q H, Cao B, Zeng Y L, et al. Recent progresses on the remote sensing radiative transfer modeling over heterogeneous vegetation canopy[J]. Journal of Remote Sensing, 2016, 20(5):933-945. ]
[28]	Hu Z, Islam S. A framework for analyzing and designing scale invariant remote sensing algorithms[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):747-755. doi: 10.1109/36.581996
[29]	赵明伟, 岳天祥. 高精度曲面建模方法研究进展与分类[J]. 地理科学进展, 2016, 35(4):401-408. doi: 10.18306/dlkxjz.2016.04.001
	[ Zhao M W, Yue T X. Classification of high accuracy surface modeling (HASM) methods and their recent developments[J]. Progress in Geography, 2016, 35(4):401-408. ]
[30]	Chen J M, Menges C H, Leblanc S G. Global mapping of foliage clumping index using multi-angular satellite data[J]. Remote Sensing of Environment, 2005, 97(4):447-457. doi: 10.1016/j.rse.2005.05.003
[31]	Monsi M, Saeki T. Uber den lichtfaktor in den pflanzegeesellschaften und seine bedeutung fur die stoffproduktion[J]. Japanese Journal of Botany, 1953, 14:22-52.
[32]	Monsi M, Saeki T. On the factor light in plant communities and its importance for matter production[J]. Annals of Botany, 2005, 95(3):549-567. doi: 10.1093/aob/mci052
[33]	Miller J B. A formula for average foliage density[J]. Australian Journal of Botany, 1967, 15(1):141-144. doi: 10.1071/BT9670141
[34]	Nilson T. A theoretical analysis of the frequency of gaps in plant stands[J]. Agricultural Meteorology, 1971, 8:5-38. doi: 10.1016/0002-1571(71)90091-4
[35]	Fang H, Baret F, Plummer S, et al. An overview of global leaf area index (LAI): Methods, products, validation, and applications[J]. Reviews of Geophysics, 2019, 57(3):739-799. doi: 10.1029/2018RG000608
[36]	Baret F, De Solan B, Lopez-Lozano R, et al. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops[J]. Agricultural and Forest Meteorology, 2010, 150(11):1393-1401. doi: 10.1016/j.agrformet.2010.04.011
[37]	Gower S T, Kucharik C J, Norman J M. Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems[J]. Remote Sensing of Environment, 1999, 70(1):29-51. doi: 10.1016/S0034-4257(99)00056-5
[38]	Maire G L, Marsden C, Verhoef W, et al. Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations[J]. Remote Sensing of Environment, 2011, 115(2):586-599. doi: 10.1016/j.rse.2010.10.004
[39]	Majasalmi T, Rautiainen M, Stenberg P, et al. An assessment of ground reference methods for estimating LAI of boreal forests[J]. Forest Ecology & Management, 2013, 292(3):10-18.
[40]	Döbert T F, Webber B L, Sugau J B, et al. Can leaf area index and biomass be estimated from Braun-Blanquet cover scores in tropical forests?[J]. Journal of Vegetation Science, 2015, 26(6):1043-1053. doi: 10.1111/jvs.12310
[41]	Heiskanen J, Rautiainen M, Stenberg P, et al. Seasonal variation in MODIS LAI for a boreal forest area in Finland[J]. Remote Sensing of Environment, 2012, 126(0):104-115. doi: 10.1016/j.rse.2012.08.001
[42]	Kucharik C J, Norman J M, Murdock L M, et al. Characterizing canopy non-randomness with a multiband vegetation imager (MVI)[J]. Journal of Geophysical Research, 1997, 102(D24):29455-29473. doi: 10.1029/97JD01175
[43]	Kucharik C J, Norman J M, Gower S T. Characterization of radiation regimes in nonrandom forest canopies: theory, measurements, and a simplified modeling approach[J]. Tree Physiology, 1999, 19(11):695-706. doi: 10.1093/treephys/19.11.695
[44]	Nilson T. Inversion of gap frequency data in forest stands[J]. Agricultural and Forest Meteorology, 1999,98-99(0):437-448.
[45]	Fang H, Li S, Zhang Y, et al. The relationship between canopy Clumping Index (CI), Fractional Vegetation Cover (FVC), and Leaf Area Index (LAI): An analysis of global satellite products; Proceedings of the IGARSS, 2020[C].
[46]	Jonckheere I, Fleck S, Nackaerts K, et al. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography[J]. Agricultural & Forest Meteorology, 2004, 121(1-2):19-35.
[47]	Yan G, Hu R, Luo J, et al. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives[J]. Agricultural and Forest Meteorology, 2019, 265:390-411. doi: 10.1016/j.agrformet.2018.11.033
[48]	Lang A R G, Yueqin X. Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies[J]. Agricultural & Forest Meteorology, 1986, 37(3):229-243.
[49]	Chen J M. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods[J]. Geoence & Remote Sensing IEEE Transactions on, 1995, 33(3):777-787.
[50]	方红亮. 我国叶面积指数卫星遥感产品生产及验证[J]. 遥感技术与应用, 2020, 35(5):990-1003.
	[ Fang H L. Development and validation of satellite leaf area index (LAI) products in China[J]. Remote Sensing Technology and Application, 2020, 35(5):990-1003. ]
[51]	Ryu Y, Nilson T, Kobayashi H, et al. On the correct estimation of effective leaf area index: Does it reveal information on clumping effects?[J]. Agricultural and Forest Meteorology, 2010, 150(3):463-472. doi: 10.1016/j.agrformet.2010.01.009
[52]	Fang H, Liu W, LI W, et al. Estimation of the directional and whole Apparent Clumping Index (ACI) from indirect optical measurements[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144:1-13. doi: 10.1016/j.isprsjprs.2018.06.022
[53]	Leblanc S G, Chen J M, Fernandes R, et al. Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests[J]. Agricultural & Forest Meteorology, 2005, 129(3-4):187-207.
[54]	Ma Q M, Li Y J, Li J, et al. Modeling of Mixed-Pixel Clumping index from remote sensing data and its evaluation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(7):2320-2331. doi: 10.1109/JSTARS.4609443
[55]	Oker-Blom P, Smolander H. The ratio of shoot silhouette area to total needle area in Scots pine[J]. Forest Science, 1988, 34(4):894-906.
[56]	Smolander S, Stenberg P. A method to account for shoot scale clumping in coniferous canopy reflectance models[J]. Remote Sensing of Environment, 2003, 88(4):363-373. doi: 10.1016/j.rse.2003.06.003
[57]	Zheng G, Moskal L M. Retrieving Leaf Area Index (LAI) using remote sensing: Theories, methods and sensors[J]. Sensors (Basel, Switzerland), 2009, 9(4):2719-2745. doi: 10.3390/s90402719
[58]	Chen J M. Remote sensing of leaf area index and clumping index[M]. LIANG S. Comprehensive Remote Sensing. Oxford: Elsevier. 2018: 53-77.
[59]	Wei S, Fang H. Estimation of canopy clumping index from MISR and MODIS sensors using the Normalized Difference Hotspot and Darkspot (NDHD) method: The influence of BRDF models and solar zenith angle[J]. Remote Sensing of Environment, 2016, 187:476-491. doi: 10.1016/j.rse.2016.10.039
[60]	Jiao Z, Dong Y, Schaaf C B, et al. An algorithm for the retrieval of the Clumping Index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model[J]. Remote Sensing of Environment, 2018, 209:594-611. doi: 10.1016/j.rse.2018.02.041
[61]	Simic A, Chen J M, Freemantle J R, et al. Improving clumping and LAI algorithms based on multiangle airborne imagery and ground measurements[J]. IEEE Transactions on Geoence & Remote Sensing, 2010, 48(4):1742-1759.
[62]	Bergen K M, Goetz S J, Dubayah R O, et al. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions[J]. Journal of Geophysical Research: Biogeosciences, 2009,114(G2):G00E6.
[63]	Hall F G, Betgen K, Blair J B, et al. Characterizing 3D vegetation structure from space: Mission requirements[J]. Remote Sensing of Environment, 2011, 115(11):2753-2775. doi: 10.1016/j.rse.2011.01.024
[64]	Leeuwen M, Nieuwenhuis M. Retrieval of forest structural parameters using LiDAR remote sensing[J]. European Journal of Forest Research, 2010, 129(4):749-770. doi: 10.1007/s10342-010-0381-4
[65]	Zhao K G, Popescu S, Meng X L, et al. Characterizing forest canopy structure with lidar composite metrics and machine learning[J]. Remote Sensing of Environment, 2011, 115(8):1978-1996. doi: 10.1016/j.rse.2011.04.001
[66]	Wang Y, Fang H. Estimation of LAI with the LiDAR technology: A Review[J]. Remote Sensing, 2020, 12(20):3457. doi: 10.3390/rs12203457
[67]	Bye I J, North P R J, Los S O, et al. Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model[J]. Remote Sensing of Environment, 2017, 188:177-189. doi: 10.1016/j.rse.2016.10.048
[68]	Ma H, Song J, Wang J. Forest canopy LAI and vertical FAVD profile inversion from airborne full-waveform LiDAR data based on a aadiative transfer Model[J]. Remote Sensing, 2015, 7(2):1897-1914. doi: 10.3390/rs70201897
[69]	Kpetz B, Morsdorf F, Sun G, et al. Inversion of a lidar waveform model for forest biophysical parameter estimation[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1):49-53. doi: 10.1109/LGRS.2005.856706
[70]	Tang H, Dubayah R, Swatantran A, et al. Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica[J]. Remote Sensing of Environment, 2012, 124:242-250. doi: 10.1016/j.rse.2012.05.005
[71]	Korhonen L, Korpela I, Heiskanen J, et al. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index[J]. Remote Sensing of Environment, 2011, 115(4):1065-1080. doi: 10.1016/j.rse.2010.12.011
[72]	Olsoy P J, Mitchell J J, Levia D F, et al. Estimation of big sagebrush leaf area index with terrestrial laser scanning[J]. Ecological Indicators, 2016, 61:815-821. doi: 10.1016/j.ecolind.2015.10.034
[73]	Luo S, Wang C, Pan F, et al. Estimation of wetland vegetation height and leaf area index using airborne laser scanning data[J]. Ecological Indicators, 2015, 48:550-559. doi: 10.1016/j.ecolind.2014.09.024
[74]	Riaño D, Vallanares F, Condés S, et al. Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests[J]. Agricultural and Forest Meteorology, 2004, 124(3-4):269-275. doi: 10.1016/j.agrformet.2004.02.005
[75]	Zhao K, Popescu S. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA[J]. Remote Sensing of Environment, 2009, 113(8):1628-1645. doi: 10.1016/j.rse.2009.03.006
[76]	Bao Y F, Ni W J, Wang D Z, et al. Effects of rree trunks on estimation of clumping index and LAI from HemiView and Terrestrial LiDAR[J]. Forests, 2018, 9(3):16-29. doi: 10.3390/f9010016
[77]	Kuusk A, Pisek J, Lang M, et al. Estimation of gap fraction and foliage clumping in forest canopies[J]. Remote Sensing, 2018, 10(7):1153-1163. doi: 10.3390/rs10071153
[78]	García M, Gajardo J, Riaño D, et al. Canopy clumping appraisal using terrestrial and airborne laser scanning[J]. Remote Sensing of Environment, 2015, 161:78-88. doi: 10.1016/j.rse.2015.01.030
[79]	Li Y M, Guo Q H, Su Y J, et al. Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130:308-316. doi: 10.1016/j.isprsjprs.2017.06.006
[80]	Zhao F, Strahler A H, Schaaf C L, et al. Measuring gap fraction, element clumping index and LAI in Sierra Forest stands using a full-waveform ground-based lidar[J]. Remote Sensing of Environment, 2012, 125:73-79. doi: 10.1016/j.rse.2012.07.007
[81]	Ma L, Zheng G, Wang X, et al. Retrieving forest canopy clumping index using terrestrial laser scanning data[J]. Remote Sensing of Environment, 2018, 10:452-472.
[82]	Chen Q, Baldocchi D, Gong P, et al. Modeling radiation and photosynjournal of a heterogeneous savanna woodland landscape with a hierarchy of model complexities[J]. Agricultural and Forest Meteorology, 2008, 148(6-7):1005-1020. doi: 10.1016/j.agrformet.2008.01.020
[83]	Thomas V, Noland T, Treitz P, et al. Leaf area and clumping indices for a boreal mixed-wood forest: Lidar, hyperspectral, and Landsat models[J]. International Journal of Remote Sensing, 2011, 32(23):8271-8297. doi: 10.1080/01431161.2010.533211
[84]	Morisette J T. Toward a standard nomenclature for imagery spatial resolution[J]. International Journal of Remote Sensing, 2010, 31(9):2347-2349. doi: 10.1080/01431160902994457
[85]	Xiao Z, Liang S, Wang J, et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):209-223. doi: 10.1109/TGRS.2013.2237780
[86]	Pinty B, Andredakis I, Clerici M, et al. Exploiting the MODIS albedos with the Two-stream Inversion Package (JRC-TIP): 1. Effective leaf area index, vegetation, and soil properties[J]. Journal of Geophysical Research, 2011, 116(D9):D09105.
[87]	Disne M, Muller J P, Kharbouche S S, et al. A new global fAPAR and LAI dataset derived from optimal Albedo Estimates: Comparison with MODIS product[J]. Remote Sensing, 2016, 8(4):275-289. doi: 10.3390/rs8040275
[88]	He L, Liu J, Chen J M, et al. Inter-and intra-annual variations of clumping index derived from the MODIS BRDF product[J]. International Journal of Applied Earth Observation and Geo-information, 2016, 44:53-60.
[89]	Wei S, Fang H, Schaaf C B, et al. Global 500 m clumping index product derived from MODIS BRDF data (2001-2017)[J]. Remote Sensing of Environment, 2019, 232:111296. doi: 10.1016/j.rse.2019.111296
[90]	Mao J, Yan B. Global monthly mean Leaf Area Index climatology, 1981-2015[M]. DAAC O. Oak Ridge, Tennessee, USA, 2019.
[91]	Plummer S, Arino O, Simon M, et al. Establishing A earth observation product service for the terrestrial carbon community: The globcarbon initiative[J]. Mitigation and Adaptation Strategies for Global Change, 2006, 11(1):97-111. doi: 10.1007/s11027-006-1012-8
[92]	Pu J, Yan K, Zhou G, et al. Evlauation of the MODIS LAI/FPAR algorithm based on 3D-RTM simulations: A case study of grassland[J]. Remote Sens, 2020, 12(20):3391. doi: 10.3390/rs12203391
[93]	吴小丹, 闻建光, 肖青, 等. 关键陆表参数遥感产品真实性检验方法研究进展[J]. 遥感学报, 2015, 19(1):75-92.
	[ Wu X D, Wen J G, Xiao Q, et al. Advances in validation methods for remote sensing products of land surface parameters[J]. Journal of Remote Sensing, 2015, 19(1):75-92. ]
[94]	Fernandes R, Plummer S, Nightingale J, et al. Global Leaf Area Index Product Validation Good Practices, in Best Practice for Satellite-Derived Land Product Validation, G Schaepman-Strub, M. Román, and J Nickeson, Editors. 2014:78.
[95]	Wu X, Xiao Q, Wen J, et al. Advances in quantitative remote sensing product validation: Overview and current status[J]. Earth-Science Reviews, 2019: 102875.
[96]	Ding Y L, Ge Y, Hu M G, et al. Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products[J]. International Journal of Remote Sensing, 2014, 35(20):7230-7244. doi: 10.1080/01431161.2014.967889
[97]	Martinez B, Cassiraga E, Camacho F, et al. Geostatistics for mapping Leaf Area Index over a Cropland Landscape: Efficiency sampling assessment[J]. Remote Sensing, 2010, 2(11):2584-2606. doi: 10.3390/rs2112584
[98]	Fang H, Ye Y, Liu W, et al. Continuous estimation of canopy leaf area index (LAI) and clumping index over broadleaf crop fields: An investigation of the PASTIS-57 instrument and smartphone applications[J]. Agricultural and Forest Meteorology, 2018, (253-254):48-61.
[99]	Xu B, Li J, Park T, et al. Improving leaf area index retrieval over heterogeneous surface mixed with water[J]. Remote Sensing of Environment, 2020, 240:111700. doi: 10.1016/j.rse.2020.111700
[100]	于文涛, 李静, 柳钦火, 等. 中国地表覆盖异质性参数提取与分析[J]. 地球科学进展, 2016, 31(10):1067-1077.
	[ Yu W T, Li J, Liu Q H, et al. Extraction and analysis of land cover heterogeneity over China[J]. Advances in Earth Science, 2016, 31(10):1067-1077. ]
[101]	姚延娟, 刘强, 柳钦火, 等. 异质性地表的叶面积指数反演的不确定性分析[J]. 遥感学报, 2007, 11(6):763-770.
	[ Yao Y J, Liu Q, Liu Q H, et al. LAI inversion uncertainties in heterogeneous surface[J]. Journal of Remote Sensing, 2007, 11(6):763-770. ]
[102]	苏理宏, 李小文, 梁顺林, 等. 典型地物波谱库的数据体系与波谱模拟[J]. 地球信息科学, 2002, 4(4):7-15.
	[ Su L H, Li X W, Liang S L, et al. Data frame and spectral simulation for remote sensing spectral data base[J]. Geo-information Science, 2002, 4(4):7-15. ]
[103]	Ichoku C, Karnieli A. A review of mixture modeling techniques for sub-pixel land cover estimation[J]. Remote Sensing Reviews, 1996, 13(3/4):161-186.
[104]	万华伟, 王锦地, 屈永华, 等. 植被波谱空间尺度效应及尺度转换方法初步研究[J]. 遥感学报, 2008, 12(4):538-545.
	[ Wan H W, Wang J D, Qu Y H, et al. Preliminary research on scale effect and scaling-up of the vegetation spectrum[J]. Journal of Remote Sensing, 2008, 12(4):538-545. ]
[105]	Wood E F and Lakshmi V. Scaling water and energy fluxes in climate systems-3 Land atmospheric modeling experiments[J]. Journal of Climate, 1993, 6(5):839-857. doi: 10.1175/1520-0442(1993)006<0839:SWAEFI>2.0.CO;2
[106]	陈健, 倪绍祥, 李静静, 等. 植被叶面积指数遥感反演的尺度效应及空间变异性[J]. 生态学报, 2006, 26(5):1502-1508.
	[ Chen J, Ni S X, Li J J, et al. Scaling effect and spatial variability in retrieval of vegetation LAI from remotely sensed data[J]. Acta Ecologica Sinica, 2006, 26(5):1502-1508. ]
[107]	田庆久, 金震宇. 森林叶面积指数遥感反演与空间尺度转换研究[J]. 遥感信息, 2006(4):5-11,1.
	[ Tian Q J, Jin Z Y. Research on calculation and spatial scaling of forest leaf area index from remote sensing image[J]. Remote Sensing Information, 2006(4):5-11,1. ]
[108]	Zhang X, Yan G, Li Q, et al. Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model[J]. International Journal of Remote Sensing, 2006, 27(24):5359-5372. doi: 10.1080/01431160600658107
[109]	吴小丹, 肖青, 闻建光, 等. 遥感数据产品真实性检验不确定性分析研究进展[J]. 遥感学报, 2014, 18(5):1011-1023.
	[ Wu X D, Xiao Q, Wen J G, et al. Advances in uncertainty analysis for the validation of remote sensing products: Take Leaf Area Index for example[J]. Journal of Remote Sensing, 2014, 18(5):1011-1023. ]
[110]	García-Haro F J, Campos-Taberner M, Muñoz-Marí J, et al. Derivation of global vegetation biophysical parameters from EUMETSAT Polar System[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 139:57-74. doi: 10.1016/j.isprsjprs.2018.03.005
[111]	Baret F, Weiss M, Lacaze R, et al. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production[J]. Remote Sensing of Environment, 2013, 137:399-409.
[112]	Tum M, Günther K, Böttcher M, et al. Global gap-free MERIS LAI time series (2002-2012)[J]. Remote Sensing, 2016, 8(1):69. doi: 10.3390/rs8010069
[113]	Huang D, Knyazikhin Y, Wang W, et al. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements[J]. Remote Sensing of Environment, 2008, 112(1):35-50. doi: 10.1016/j.rse.2006.05.026
[114]	Baret F, Weiss M, Verger A, et al. ATBD for LAI, FAPAR and FCOVER from PROBA-V products at 300M resolution (GEOV3). 2016:61.
[115]	Gonsamo A, Chen J M. Improved LAI algorithm implementation to MODIS data by incorporating background, topography, and foliage clumping information[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2):1076-1088. doi: 10.1109/TGRS.2013.2247405
[116]	Yan K, Park T, Chen C, et al. Generating global products of LAI and FPAR from SNPP-VIIRS data: Theoretical Background and Implementation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4):2119-2137. doi: 10.1109/TGRS.2017.2775247

	基本概念	实测方法		异速生长法^a	遥感估算方法			遥感产品^c
	基本概念	破坏法	孔隙率模型	异速生长法^a	经验统计^a	模型反演^b	混合反演方法^b	遥感产品^c
LAI	-	-	+	+/-	+/-	+	+	-
LAI_e	+		+	+/-	+/-	+	+	+
CI	+		+	+/-	+/-	+	+	+

产品	传感器	空间精度	时间精度	算法	真实/有效LAI	参考文献
EPS (V1)	MetOp/AVHRR	1.1 km	10 d (2015年— )	高斯过程回归	真实	[110]
GA-TIP (V1)	SPOT/VEGETATION, EnviSAT/MERIS	1 km	8 d (2002年—2011年)	基于反照率的同化反演	有效	[87]
GEOV2 (V2)	SPOT/VEGETATION, MODIS	1/112º	10 d (1999年— )	神经网络模型	真实	[111]
GLASS (V5)	SPOT/VEGETATION, MODIS	500 m	8 d (2000年— )	神经网络模型	真实	[85]
GLOBMAP (V3)	MODIS	500 m	8 d (2000年— )	RSR-LAI关系	真实	[25]
JRC-TIP (V1)	MODIS	0.01º	16 d (2000年— )	基于反照率的同化反演	有效	[86]
MERIS (V1)	EnviSAT/MERIS	300 m	10 d (2003年—2011年)	神经网络模型	真实	[112]
MODIS (V6)	MODIS	500 m	4 d (2000年— )	查找表反演	真实	[113]
PROBA-V (V1)	PROBA-V	300 m	10 d (2014年— )	神经网络模型	真实	[114]
University of Toronto (UofT, V2)	MODIS, MISR	250 m	10 d (2003年)	RSR-LAI关系	真实	[115]
VIIRS (V1)	SNPP/VIIRS	500 m	8 d (2012年— )	查找表反演	真实	[116]