植被光合有效辐射吸收比率遥感研究进展
Progress of fraction of absorbed photosynthetically active radiation retrieval from remote sensing data
- 2020年24卷第11期 页码:1307-1324
纸质出版日期: 2020-11-07
DOI: 10.11834/jrs.20208498
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田定方,范闻捷,任华忠.2020.植被光合有效辐射吸收比率遥感研究进展.遥感学报,24(11): 1307-1324
Tian D F,Fan W J and Ren H Z. 2020. Progress of fraction of absorbed photosynthetically active radiation retrieval from remote sensing data. Journal of Remote Sensing(Chinese), 24(11):1307-1324
植被光合有效辐射吸收比率FPAR(Fraction of absorbed Photosynthetically Active Radiation)反映了植被冠层的光学特性,是表征植被光合作用水平和生长状态的重要参量,因此成为全球变化研究中多种过程模型的重要输入参数。随着定量遥感研究的深入和新型传感器的使用,从区域到全球尺度上的FPAR遥感估算方法不断提出,多样化的遥感FPAR产品越来越多地应用于碳循环、能量循环、生产力估算及作物估产等研究领域。本文梳理了遥感估算的植被光合有效辐射的相关概念和算法,并着重对过去十年间遥感估算FPAR的新进展进行了系统总结和探讨。研究表明,近年来FPAR遥感的研究工作一方面聚焦于对现有算法的改进与各类型产品的验证,更多的研究则侧重于FPAR概念体系的拓展,叶片、叶绿素水平的FPAR估算,直射光、散射光的FPAR建模等新方向逐渐成为研究热点。
The Fraction of absorbed Photosynthetically Active Radiation(FPAR) is a key parameter in various global change process model
which characterizes the optical properties
photosynthesis process and growth state of canopy. The great progress of quantitative remote sensing and various data products make FPAR products widely used in carbon cycle
energy exchange and vegetation research both in global and regional scale. Because of the spatial heterogeneity of landscape
remote sensing is the only to monitor in large scale. Various methods were developed to obtain FPAR based on remote sensing technique. Empirical method based on the relationship between FPAR and vegetation index. High efficiency is the main feature of empirical method and the limit is that the generality of empirical relationship is weak. Physical method based on canopy model such as geometrical optics model and radiative transfer model which can be used in different kinds of land cover and large scale areas. But the input parameter and calculation process of physical method is relatively complex
which can influence the accuracy of result. In order to improve the accuracy of research
high quality and temporal resolution FPAR estimation is needed. In recent years
the improvement of FPAR algorithms
validation of FPAR products
FPAR of leaf and chlorophyll levels
direct and scattered FPAR (direct light and diffused light) and FPAR vertical distribution became new topics in this area. This paper reviewed the theory and methods of FPAR retrieval from remote sensing
and discussed the new progress of remotely sensed FPAR in past 10 years. The conclusion shows that research of FPAR is more and more important in recent years and the concept and scientific problems are gradually clear. New canopy models and algorithms improve the accuracy of products which promote the use of FPAR in various study areas. Especially
neural network becomes a new way of FPAR inversion which can avoid weak point of physic methods and improve the efficiency of the process. But there are also many aspects need to do in future. The accuracy of FPAR products still cannot reach the standard and products based on high spatial resolution data are required. Day average FPAR product is also important work to Net Primary Productivity (NPP) models. Canopy models also need to be improved in order to fit different kinds of vegetation. On the other hand
we need more high quality FPAR observation systems over the world to get enough reliable in-situ data for validation. Progress in photosynthesis mechanism research and sensors make it possible to realization these targets. New sensors were put in use in recent years. Improve the accuracy and diversity of remote sensing FPAR based on new generation satellite instrument will promote the application for FPAR in various fields.
植被定量遥感光合有效辐射吸收比率冠层吸收模型遥感算法产品及验证
vegetation quantitative remote sensingFPARCanopy Absorption Modelremote sensing algorithmproducts and validation
Asner G P, Wessman C A and Archer S. 1998. Scale dependence of absorption of photosynthetically active radiation in terrestrial ecosystems. Ecological Applications, 8(4): 1003-1021 [DOI: 10.1890/1051-0761(1998)008http://dx.doi.org/10.1890/1051-0761(1998)008[1003:SDOAOP]2.0.CO;2]
Bacour C, Baret F, Béal D, Weiss M and Pavageau K. 2006. Neural network estimation of LAI, FAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: principles and validation. Remote Sensing of Environment, 105(4): 313-325 [DOI: 10.1016/j.rse.2006.07.014http://dx.doi.org/10.1016/j.rse.2006.07.014]
Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Niño F, Weiss M, Samain O, Roujean J L and Leroy M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: part 1: principles of the algorithm. Remote Sensing of Environment, 110(3): 275-286 [DOI: 10.1016/j.rse.2007.02.018http://dx.doi.org/10.1016/j.rse.2007.02.018]
Baret F, Weiss M, Lacaze R, Camacho F, Makhmara H, Pacholcyzk P and Smets B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sensing of Environment, 137: 299-309 [DOI: 10.1016/j.rse.2012.12.027http://dx.doi.org/10.1016/j.rse.2012.12.027]
Bonan G B, Oleson K W, Vertenstein M, Levis S, Zeng X B, Dai Y J, Dickinson R E and Yang Z L. 2002. The land surface climatology of the community land model coupled to the NCAR community climate model. Journal of Climate, 15(22): 3123-3149 [DOI: 10.1175/1520-0442(2002)015<3123:TLSCOT>2.0.CO;2http://dx.doi.org/10.1175/1520-0442(2002)015<3123:TLSCOT>2.0.CO;2]
Braswell B H, Schimel D S, Privette J L, Moore III B, Emery W J, Sulzman E W and Hudak A T. 1996. Extracting ecological and biophysical information from AVHRR optical data: an integrated algorithm based on inverse modeling. Journal of Geophysical Research: Atmospheres, 101(D18): 23335-23348 [DOI: 10.1029/96JD02181http://dx.doi.org/10.1029/96JD02181]
Camacho F, Cernicharo J, Lacaze R, Baret F and Weiss M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. part 2: validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310-329 [DOI: 10.1016/j.rse.2013.02.030http://dx.doi.org/10.1016/j.rse.2013.02.030]
Cao R Y, Shen M G, Chen J and Tang Y H. 2014. A simple method to simulate diurnal courses of PAR absorbed by grassy canopy. Ecological Indicators, 46: 129-137 [DOI: 10.1016/j.ecolind.2014.06.017http://dx.doi.org/10.1016/j.ecolind.2014.06.017]
Carrer D, Roujean J L, Lafont S, Calvet J C, Boone A, Decharme B, Delire C and Gastellu-Etchegorry J P. 2013. A canopy radiative transfer scheme with explicit FAPAR for the interactive vegetation model ISBA-A-gs: impact on carbon fluxes. Journal of Geophysical Research: Biogeosciences, 118(2): 888-903 [DOI: 10.1002/jgrg.20070http://dx.doi.org/10.1002/jgrg.20070]
Casanova D, Epema G F and Goudriaan J. 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research, 55(1/2): 83-92 [DOI: 10.1016/S0378-4290(97)00064-6http://dx.doi.org/10.1016/S0378-4290(97)00064-6]
Chasmer L, Hopkinson C, Treitz P, McCaughey H, Barr A and Black A. 2008. A Lidar-based hierarchical approach for assessing MODIS FPAR. Remote Sensing of Environment, 112(12): 4344-4357 [DOI: 10.1016/j.rse.2008.08.003http://dx.doi.org/10.1016/j.rse.2008.08.003]
Chen J M. 1996. Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests. IEEE Transactions on Geoscience and Remote Sensing, 34(6): 1353-1368 [DOI: 10.1109/36.544559http://dx.doi.org/10.1109/36.544559]
Chen L F, Gao Y H, Li L, Liu Q H and Gu X F. 2008. Forest NPP estimation based on MODIS data under cloudless condition. Science in China Series D: Earth Sciences, 51(3): 331-338 [DOI: 10.1007/s11430-008-0013-8http://dx.doi.org/10.1007/s11430-008-0013-8]
Clevers J G P W, Van Leeuwen H J C and Verhoef W. 1994. Estimating the fraction APAR by means of vegetation indices: a sensitivity analysis with a combined prospect-sail model. Remote Sensing Reviews, 9(3): 203-220 [DOI: 10.1080/02757259409532225http://dx.doi.org/10.1080/02757259409532225]
Cristiano P M, Posse G, Di Bella C M and Jaimes F R. 2010. Uncertainties in fPAR estimation of grass canopies under different stress situations and differences in architecture. International Journal of Remote Sensing, 31(15): 4095-4109 [DOI: 10.1080/01431160903229192http://dx.doi.org/10.1080/01431160903229192]
D’Odorico P, Gonsamo A, Pinty B, Gobron N, Coops N, Mendez E and Schaepman M E. 2014. Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe. Remote Sensing of Environment, 142: 141-154 [DOI: 10.1016/j.rse.2013.12.005http://dx.doi.org/10.1016/j.rse.2013.12.005]
Daughtry C S T, Gallo K P, Goward S N, Prince S D and Kustas W P. 1992. Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sensing of Environment, 39(2): 141-152 [DOI: 10.1016/0034-4257(92)90132-4http://dx.doi.org/10.1016/0034-4257(92)90132-4]
Dong J W, Xiao X M, Wagle P, Zhang G L, Zhou Y T, Jin C, Torn M S, Meyers T P, Suyker A E, Wang J B, Yan H M, Biradar C and Moore III B. 2015. Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought. Remote Sensing of Environment, 162: 154-168 [DOI: 10.1016/j.rse.2015.02.022http://dx.doi.org/10.1016/j.rse.2015.02.022]
Donohue R J, McVicar T R and Roderick M L. 2009. Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981-2006. Global Change Biology, 15(4): 1025-1039 [DOI: 10.1111/j.1365-2486.2008.01746.xhttp://dx.doi.org/10.1111/j.1365-2486.2008.01746.x]
Du S S, Liu L Y, Liu X J and Hu J C. 2017. Response of canopy solar-induced chlorophyll fluorescence to the absorbed photosynthetically active radiation absorbed by chlorophyll. Remote Sensing, 9(9): 911 [DOI: 10.3390/rs9090911http://dx.doi.org/10.3390/rs9090911]
Fan W J, Liu Y, Xu X R, Chen G X and Zhang B T. 2014. A new FAPAR analytical model based on the law of energy conservation: a case study in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3945-3955 [DOI: 10.1109/JSTARS.2014.2325673http://dx.doi.org/10.1109/JSTARS.2014.2325673]
Farquhar G D and Roderick M L. 2003. Pinatubo, diffuse light, and the carbon cycle. Science, 299(5615): 1997-1998 [DOI: 10.1126/science.1080681http://dx.doi.org/10.1126/science.1080681]
Fensholt R, Sandholt I and Rasmussen M S. 2004. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91(3/4): 490-507 [DOI: 10.1016/j.rse.2004.04.009http://dx.doi.org/10.1016/j.rse.2004.04.009]
Fernández N, Paruelo J M and Delibes M. 2010. Ecosystem functioning of protected and altered mediterranean environments: a remote sensing classification in Doñana, Spain. Remote Sensing of Environment, 114(1): 211-220 [DOI: 10.1016/j.rse.2009.09.001http://dx.doi.org/10.1016/j.rse.2009.09.001]
Fu G and Wu J S. 2017. Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the northern Tibetan plateau. Remote Sensing Letters, 8(9): 831-838 [DOI: 10.1080/2150704X.2017.1331054http://dx.doi.org/10.1080/2150704X.2017.1331054]
Gao Y H, Chen L F, Liu Q H, Gu X F and Tian G L. 2006. Research on remote sensing model for FPAR absorbed by chlorophyll. Journal of Remote Sensing, 10(5): 798-803
高彦华, 陈良富, 柳钦火, 顾行发, 田国良. 2006. 叶绿素吸收的光合有效辐射比率的遥感估算模型研究. 遥感学报, 10(5): 798-803 [DOI: 10.3321/j.issn:1007-4619.2006.05.029http://dx.doi.org/10.3321/j.issn:1007-4619.2006.05.029]
García M, Sandholt I, Ceccato P, Ridler M, Mougin E, Kergoat L, Morillas L, Timouk F, Fensholt R and Domingo F. 2013. Actual evapotranspiration in drylands derived from in-situ and satellite data: assessing biophysical constraints. Remote Sensing of Environment, 131: 103-118 [DOI: 10.1016/j.rse.2012.12.016http://dx.doi.org/10.1016/j.rse.2012.12.016]
GCOS. 2011. Systematic Observation Requirements for Satellite-Based Data Products for Climate. WMO/TD No.1338. WMO: 79-83
Gitelson A A, Viña A, Verma S B, Rundquist D C, Arkebauer T J, Keydan G, Leavitt B, Ciganda V, Burba G G and Suyker A E. 2006. Relationship between gross primary production and chlorophyll content in crops: implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research: Atmospheres, 111(D8): D08S11 [DOI: 10.1029/2005JD006017http://dx.doi.org/10.1029/2005JD006017]
Gitelson A A. 2019. Remote estimation of fraction of radiation absorbed by photosynthetically active vegetation: generic algorithm for maize and soybean. Remote Sensing Letters, 10(3): 283-291 [DOI: 10.1080/2150704X.2018.1547445http://dx.doi.org/10.1080/2150704X.2018.1547445]
Gobron N, Pinty B, Aussedat O, Chen J M, Cohen W B, Fensholt R, Gond V, Huemmrich K F, Lavergne T, Mélin F, Privette J L, Sandholt I, Taberner M, Turner D P, Verstraete M M and Widlowski J L. 2006. Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: methodology and results using joint research center products derived from SeaWIFS against ground-based estimations. Journal of Geophysical Research: Atmospheres, 111(D13): D13110 [DOI: 10.1029/2005JD006511http://dx.doi.org/10.1029/2005JD006511]
Gobron N, Pinty B, Aussedat O, Taberner M, Faber O, Mélin F, Lavergne T, Robustelli M And Snoeij P. 2008. Uncertainty estimates for the FAPAR operational products derived from MERIS — impact of top-of-atmosphere radiance uncertainties and validation with field data. Remote Sensing of Environment, 112(4): 1871-1883 [DOI: 10.1016/j.rse.2007.09.011http://dx.doi.org/10.1016/j.rse.2007.09.011]
Gobron N, Pinty B, Mélin F, Taberner M, Verstraete M M, Robustelli M and Widlowski J L. 2007. Evaluation of the MERIS/ENVISAT FAPAR product. Advances in Space Research, 39(1): 105-115 [DOI: 10.1016/j.asr.2006.02.048http://dx.doi.org/10.1016/j.asr.2006.02.048]
Gobron N, Pinty B, Verstraete M and Govaerts Y. 1999. The MERIS Global Vegetation Index (MGVI): description and preliminary application. International Journal of Remote Sensing, 20(9): 1917-1927 [DOI: 10.1080/014311699212542http://dx.doi.org/10.1080/014311699212542]
Goetz S J, Prince S D, Goward S N, Thawley M M, Small J and Johnston A. 1999. Mapping net primary production and related biophysical variables with remote sensing: application to the BOREAS region. Journal of Geophysical Research: Atmospheres, 104(D22): 27719-27734 [DOI: 10.1029/1999JD900269http://dx.doi.org/10.1029/1999JD900269]
Goward S N and Huemmrich K F. 1992. Vegetation canopy PAR absorptance and the normalized difference vegetation index: an assessment using the SAIL model. Remote Sensing of Environment, 39(2): 119-140 [DOI: 10.1016/0034-4257(92)90131-3http://dx.doi.org/10.1016/0034-4257(92)90131-3]
Gower S T, Kucharik C J and Norman J M. 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, 70(1): 29-51 [DOI: 10.1016/S0034-4257(99)00056-5http://dx.doi.org/10.1016/S0034-4257(99)00056-5]
Gu L H, Baldocchi D, Verma S B, Black T A, Vesala T, Falge E M and Dowty P R. 2002. Advantages of diffuse radiation for terrestrial ecosystem productivity. Journal of Geophysical Research: Atmospheres, 107(D6): 4050 [DOI: 10.1029/2001JD001242http://dx.doi.org/10.1029/2001JD001242]
Gu L H, Baldocchi D D, Wofsy S C, Munger J W, Michalsky J J, Urbanski S P and Boden T A. 2003. Response of a deciduous forest to the mount pinatubo eruption: enhanced photosynthesis. Science, 299(5615): 2035-2038 [DOI: 10.1126/science.1078366http://dx.doi.org/10.1126/science.1078366]
Guanter L, Zhang Y G, Jung M, Joiner J, Voigt M, Berry J A, Frankenberg C, Huete A R, Zarco-Tejada P, Lee J E, Moran M S, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, Gianelle D, Klumpp K, Cescatti A, Baker J M and Griffis T J. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 111(14): E1327-E1333 [DOI: 10.1073/pnas.1320008111http://dx.doi.org/10.1073/pnas.1320008111]
Hanan N P, Burba G, Verma S B, Berry J A, Suyker A and Walter-Shea E A. 2002. Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption. Global Change Biology, 8(6): 563-574 [DOI: 10.1046/j.1365-2486.2002.00488.xhttp://dx.doi.org/10.1046/j.1365-2486.2002.00488.x]
Hanan N P, Kabat P, Dolman A J and Elbers J A. 1998. Photosynthesis and carbon balance of a Sahelian fallow savanna. Global Change Biology, 4(5): 523-538 [DOI: 10.1046/j.1365-2486.1998.t01-1-00126.xhttp://dx.doi.org/10.1046/j.1365-2486.1998.t01-1-00126.x]
He L M, Chen J M, Pan Y D, Birdsey R and Kattge J. 2012. Relationships between net primary productivity and forest stand age in U.S. forests. Global Biogeochemical Cycles, 26(3):
GB3009 [DOI: 10.1029/2010GB003942http://dx.doi.org/10.1029/2010GB003942]
Jenkins J P, Richardson A D, Braswell B H, Ollinger S V, Hollinger D Y and Smith M L. 2007. Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agricultural and Forest Meteorology, 143(1/2): 64-79 [DOI: 10.1016/j.agrformet.2006.11.008http://dx.doi.org/10.1016/j.agrformet.2006.11.008]
Kaminski T, Knorr W, Scholze M, Gobron N, Pinty B, Giering R and Mathieu P P. 2012. Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis. Biogeosciences, 9(8): 3173-3184 [DOI: 10.5194/bg-9-3173-2012http://dx.doi.org/10.5194/bg-9-3173-2012]
Kanniah K D, Beringer J, Hutley L B, Tapper N J and Zhu X. 2009. Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia. Remote Sensing of Environment, 113(9): 1808-1822 [DOI: 10.1016/j.rse.2009.04.013http://dx.doi.org/10.1016/j.rse.2009.04.013]
King D A, Turner D P and Ritts W D. 2011. Parameterization of a diagnostic carbon cycle model for continental scale application. Remote Sensing of Environment, 115(7): 1653-1664 [DOI: 10.1016/j.rse.2011.02.024http://dx.doi.org/10.1016/j.rse.2011.02.024]
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]
Lambers H, Chapin III F S and Pons T L. 1998. Plant Physiological Ecology. New York: Springer-Verlag
Li L, Du Y M, Tang Y, Xin X Z, Zhang H L, Wen J G and Liu Q H. 2015b. A new algorithm of the FPAR product in the Heihe river basin considering the contributions of direct and diffuse solar radiation separately. Remote Sensing, 7(5): 6414-6432 [DOI: 10.3390/rs70506414http://dx.doi.org/10.3390/rs70506414]
Li L, Xin X Z, Zhang H L, Yu J F, Liu Q H, Yu S S and Wen J G. 2015a. A method for estimating hourly Photosynthetically Active Radiation (PAR) in China by combining geostationary and polar-orbiting satellite data. Remote Sensing of Environment, 165: 14-26 [DOI: 10.1016/j.rse.2015.03.034http://dx.doi.org/10.1016/j.rse.2015.03.034]
Li W J and Fang H L. 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(1): 96-112 [DOI: 10.1002/2014JG 002754http://dx.doi.org/10.1002/2014JG002754]
Lin S R, Li J and Liu Q H. 2018. Overview on estimation accuracy of gross primary productivity with remote sensing method. Journal of Remote Sensing, 22(2): 234-252
林尚荣, 李静, 柳钦火. 2018. 陆地总初级生产力遥感估算精度分析. 遥感学报, 22(2): 234-252 [DOI: 10.11834/jrs.20186456http://dx.doi.org/10.11834/jrs.20186456]
Liu J G, Miller J R, Haboudane D, Pattey E and Hochheim K. 2008. Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices. Canadian Journal of Remote Sensing, 34(sup 1): S124-S138 [DOI: 10.5589/m07-062http://dx.doi.org/10.5589/m07-062]
Liu L Y, Peng D L, Hu Y and Jiao Q J. 2013. A novel in situ FPAR measurement method for low canopy vegetation based on a digital camera and reference panel. Remote Sensing, 5(1): 274-281 [DOI: 10.3390/rs5010274http://dx.doi.org/10.3390/rs5010274]
Liu R Y. 2016. FPAR Retrieval Method Development Based on Radiative Transfer Model and the Application. Beijing: Beijing Normal University
刘镕源. 2016. 基于辐射传输模型的FPAR遥感反演方法研究与应用. 北京: 北京师范大学
Liu R Y, Ren H Z, Liu S H and Liu Q. 2014a. Evaluation of MODIS, POLDER and CYCLOPES global FPAR products//Proceedings of 2014IEEE Geoscience and Remote Sensing Symposium. Quebec City: IEEE: 5068-5071 [DOI: 10.1109/IGARSS.2014.6947636http://dx.doi.org/10.1109/IGARSS.2014.6947636]
Liu R Y, Ren H Z, Liu S H, Liu Q and Li X W. 2015. Modelling of fraction of absorbed photosynthetically active radiation in vegetation canopy and its validation. Biosystems Engineering, 133: 81-94 [DOI: 10.1016/j.biosystemseng.2015.03.004http://dx.doi.org/10.1016/j.biosystemseng.2015.03.004]
Liu R Y, Ren H Z, Liu S H, Liu Q, Yan B K and Gan F P. 2018. Generalized FPAR estimation methods from various satellite sensors and validation. Agricultural and Forest Meteorology, 260-261: 55-72 [DOI: 10.1016/j.agrformet.2018.05.024http://dx.doi.org/10.1016/j.agrformet.2018.05.024]
Liu Z J, Shao Q Q and Liu J Y. 2014b. The performances of MODIS-GPP and -ET products in china and their sensitivity to input data (FPAR/LAI). Remote Sensing, 7(1): 135-152 [DOI: 10.3390/rs70100135http://dx.doi.org/10.3390/rs70100135]
Liu Z Y, Notaro M, Kutzbach J and Liu N Z. 2006. Assessing global vegetation-climate feedbacks from observations. Journal of Climate, 19(5): 787-814 [DOI: 10.1175/JCLI3658.1http://dx.doi.org/10.1175/JCLI3658.1]
Lotsch A, Tian Y, Friedl M A and Myneni R B. 2003. Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: classification methods and sensitivities to errors. International Journal of Remote Sensing, 24(10): 1997-2016 [DOI: 10.1080/01431160210154858http://dx.doi.org/10.1080/01431160210154858]
Mackey B, Berry S, Hugh S, Ferrier S, Harwood T D and Williams K J. 2012. Ecosystem greenspots: identifying potential drought, fire, and climate-change micro-refuges. Ecological Applications, 22(6): 1852-1864 [DOI: 10.1890/11-1479.1http://dx.doi.org/10.1890/11-1479.1]
Madani N, Kimball J S, Affleck D L R, Kattge J, Graham J, van Bodegom P M, Reich P B and Running S W. 2014. Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency. Journal of Geophysical Research: Biogeosciences, 119(9): 1755-1769 [DOI: 10.1002/2014JG002709http://dx.doi.org/10.1002/2014JG002709]
Majasalmi T, Rautiainen M and Stenberg P. 2016. Modeled and measured FPAR in a boreal forest: validation and application of a new model. Agricultural and Forest Meteorology, 189-190: 118-124 [DOI: 10.1016/j.agrformet.2014.01.015http://dx.doi.org/10.1016/j.agrformet.2014.01.015]
Martínez B, Camacho F, Verger A, García-Haro F J and Gilabert M A. 2013. Intercomparison and quality assessment of MERIS, MODIS and SEVIRI FAPAR products over the Iberian peninsula. International Journal of Applied Earth Observation and Geoinformation, 21: 463-476 [DOI: 10.1016/j.jag.2012.06.010http://dx.doi.org/10.1016/j.jag.2012.06.010]
Maselli F, Chiesi M, Fibbi L and Moriondo M. 2008. Integration of remote sensing and ecosystem modelling techniques to estimate forest net carbon uptake. International Journal of Remote Sensing, 29(8): 2437-2443 [DOI: 10.1080/01431160801894857http://dx.doi.org/10.1080/01431160801894857]
McCallum I, Wagner W, Schmullius C, Shvidenko A, Obersteiner M, Fritz S and Nilsson S. 2010. Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000. Remote Sensing of Environment, 114(5): 941-949 [DOI: 10.1016/j.rse.2009.12.009http://dx.doi.org/10.1016/j.rse.2009.12.009]
Melis C, Szafrańska P A, Jędrzejewska B and Bartoń K. 2006. Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. Journal of Biogeography, 33(5): 803-811 [DOI: 10.1111/j.1365-2699.2006.01434.xhttp://dx.doi.org/10.1111/j.1365-2699.2006.01434.x]
Mercado L M, Bellouin N, Sitch S, Boucher O, Huntingford C, Wild M and Cox P M. 2009. Impact of changes in diffuse radiation on the global land carbon sink. Nature, 458(7241): 1014-1017 [DOI: 10.1038/nature07949http://dx.doi.org/10.1038/nature07949]
Monteith J L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9(3): 747-766 [DOI: 10.2307/2401901http://dx.doi.org/10.2307/2401901]
Monteith J L. 1977. Climate and the efficiency of crop production in Britain. Philosophical Transaction of the Royal Society: Biological Sciences, 281(980): 277-294 [DOI: 10.1098/rstb.1977.0140http://dx.doi.org/10.1098/rstb.1977.0140]
Moreno Á, García-Haro F J, Martínez B and Gilabert M A. 2014. Noise reduction and gap filling of FAPAR time series using an adapted local regression filter. Remote Sensing, 6(9): 8238-8260 [DOI: 10.3390/rs6098238http://dx.doi.org/10.3390/rs6098238]
Myneni R B, Hoffman S, Knyazikhin Y, Privette J L, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith G R, Lotsch A, Friedl M, Morisette J T, Votava P, Nemani R R and Running S W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1/2): 214-231 [DOI: 10.1016/S0034-4257(02)00074-3http://dx.doi.org/10.1016/S0034-4257(02)00074-3]
Nürnberg D J, Morton J, Santabarbara S, Telfer A, Joliot P, Antonaru L A, Ruban A V, Cardona T, Krausz E, Boussac A, Fantuzzi A and Rutherford A W. 2018. Photochemistry beyond the red limit in chlorophyll f-containing photosystems. Science, 360(6394): 1210-1213 [DOI: 10.1126/science.aar8313http://dx.doi.org/10.1126/science.aar8313]
Ogutu B O and Dash J. 2013. An algorithm to derive the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (FAPARps) from eddy covariance flux tower data. New Phytologist, 197(2): 511-523 [DOI: 10.1111/nph.12039http://dx.doi.org/10.1111/nph.12039]
Pickett-Heaps C A, Canadell J G, Briggs P R, Gobron N, Haverd V, Paget M J, Pinty B and Raupach M R. 2014. Evaluation of six satellite-derived fraction of absorbed photosynthetic active radiation (FAPAR) products across the Australian continent. Remote Sensing of Environment, 140: 241-256 [DOI: 10.1016/j.rse.2013.08.037http://dx.doi.org/10.1016/j.rse.2013.08.037]
Pinty B, Lavergne T, Widlowski J L, Gobron N and Verstraete M M. 2009. On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption. Remote Sensing of Environment, 113(1): 10-23 [DOI: 10.1016/j.rse.2008.08.017http://dx.doi.org/10.1016/j.rse.2008.08.017]
Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel E E, Moreno J, Frankenberg C and Berry J A. 2014. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65(15): 4065-4095 [DOI: 10.1093/jxb/eru191http://dx.doi.org/10.1093/jxb/eru191]
Potter C S, Randerson J T, Field C B, Matson P A, Vitousek P M, Mooney H A and Klooster S A. 1993. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 7(4): 811-841 [DOI: 10.1029/93GB02725http://dx.doi.org/10.1029/93GB02725]
Prince S D and Goward S N. 1995. Global primary production: a remote sensing approach. Journal of Biogeography, 22(4/5): 815-835 [DOI: 10.2307/2845983http://dx.doi.org/10.2307/2845983]
Rahman M M, Lamb D W and Stanley J N. 2015. The impact of solar illumination angle when using active optical sensing of NDVI to infer FAPAR in a pasture canopy. Agricultural and Forest Meteorology, 202: 39-43 [DOI: 10.1016/j.agrformet.2014.12.001http://dx.doi.org/10.1016/j.agrformet.2014.12.001]
Ridao E, Conde J R and Mı́nguez M I. 1998. Estimating fAPAR from nine vegetation indices for irrigated and nonirrigated faba bean and semileafless pea canopies. Remote Sensing of Environment, 66(1): 87-100 [DOI: 10.1016/S0034-4257(98)00050-9http://dx.doi.org/10.1016/S0034-4257(98)00050-9]
Seixas J, Carvalhais N, Nunes C and Benali A. 2009. Comparative analysis of MODIS-FAPAR and MERIS–MGVI datasets: potential impacts on ecosystem modeling. Remote Sensing of Environment, 113(12): 2547-2559 [DOI: 10.1016/j.rse.2009.07.018http://dx.doi.org/10.1016/j.rse.2009.07.018]
Steinberg D C, Goetz S J and Hyer E J. 2006. Validation of MODIS FPAR products in boreal forests of Alaska. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1818-1828 [DOI: 10.1109/TGRS.2005.862266http://dx.doi.org/10.1109/TGRS.2005.862266]
Sun R and Zhu Q J. 1999. Net primary productivity of terrestrial vegetation—a review on relatedrsearches. Chinese Journal of Applied Ecology, 10(6): 757-760
孙睿, 朱启疆. 1999. 陆地植被净第一性生产力的研究. 应用生态学报, 10(6): 757-760
Tan C W, Samanta A, Jin X L, Tong L, Ma C, Guo W S, Knyazikhin Y and Myneni R B. 2013. Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies. International Journal of Remote Sensing, 34(24): 8789-8802 [DOI: 10.1080/01431161.2013.853143http://dx.doi.org/10.1080/01431161.2013.853143]
Tao X, Fan W J, Wang D C, Yan B Y and Xu X R. 2009. Remote sensing retrieval of FAPAR: model and analysis. Advances in Earth Science, 24(7): 741-747
陶欣, 范闻捷, 王大成, 闫彬彦, 徐希孺. 2009. 植被FAPAR的遥感模型与反演研究. 地球科学进展, 24(7): 741-747 [DOI: 10.3321/j.issn:1001-8166.2009.07.007http://dx.doi.org/10.3321/j.issn:1001-8166.2009.07.007]
Tao X, Liang S L and He T. 2013. Estimation of fraction of Absorbed Photosynthetically Active Radiation from multiple satellite data//Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium. Melbourne: IEEE: 3072-3075 [DOI: 10.1109/IGARSS.2013.6723475http://dx.doi.org/10.1109/IGARSS.2013.6723475]
Tao X, Liang S L and Wang D D. 2015. Assessment of five global satellite products of fraction of absorbed photosynthetically active radiation: intercomparison and direct validation against ground-based data. Remote Sensing of Environment, 163: 270-285 [DOI: 10.1016/j.rse.2015.03.025http://dx.doi.org/10.1016/j.rse.2015.03.025]
Tian Y, Dickinson R E, Zhou L, Zeng X, Dai Y, Myneni R B, Knyazikhin Y, Zhang X, Friedl M, Yu H, Wu W and Shaikh M. 2004. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. Journal of Geophysical Research: Atmospheres, 109(D1): D01103 [DOI: 10.1029/2003JD003777http://dx.doi.org/10.1029/2003JD003777]
Tian Y H, Zhang Y, Knyazikhin Y, Myneni R B, Glassy J M, Dedieu G and Running S W. 2000. Prototyping of MODIS LAI and FPAR algorithm with LASUR and LANDSAT data. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2387-2401 [DOI: 10.1109/36.868894http://dx.doi.org/10.1109/36.868894]
Traore A K, Ciais P, Vuichard N, MacBean N, Dardel C, Poulter B, Piao S L, Fisher J B, Viovy N, Jung M and Myneni R. 2014. 1982-2010 trends of light use efficiency and inherent water use efficiency in african vegetation: sensitivity to climate and atmospheric CO2 concentrations. Remote Sensing, 6(9): 8923-8944 [DOI: 10.3390/rs6098923http://dx.doi.org/10.3390/rs6098923]
Turner D P, Ritts W D, Zhao M S, Kurc S A, Dunn A L, Wofsy S, Small E E and Running S W. 2006. Assessing interannual variation in MODIS-based estimates of gross primary production. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1899-1907 [DOI: 10.1109/TGRS.2006.876027http://dx.doi.org/10.1109/TGRS.2006.876027]
van der Tol C, Verhoef W, Timmermans J, Verhoef A and Su Z. 2009. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6(12): 3109-3129 [DOI: 10.5194/bg-6-3109-2009http://dx.doi.org/10.5194/bg-6-3109-2009]
Verger A, Baret F and Weiss M. 2011. A multisensor fusion approach to improve LAI time series. Remote Sensing of Environment, 115(10): 2460-2470 [DOI: 10.1016/j.rse.2011.05.006http://dx.doi.org/10.1016/j.rse.2011.05.006]
Verhoef W and Bach H. 2007. Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sensing of Environment, 109(2): 166-182 [DOI: 10.1016/j.rse.2006.12.013http://dx.doi.org/10.1016/j.rse.2006.12.013]
Wang L, Fan W J, Xu X R and Liu Y. 2015. Scaling transform method for remotely sensed FAPAR based on FAPAR-P model. IEEE Geoscience and Remote Sensing Letters, 12(4): 706-710 [DOI: 10.1109/LGRS.2014.2359051http://dx.doi.org/10.1109/LGRS.2014.2359051]
Wang Y J, Tian Y H, Zhang Y, El-Saleous N, Knyazikhin Y, Vermote E and Myneni R B. 2001. Investigation of product accuracy as a function of input and model uncertainties: case study with SeaWiFS and MODIS LAI/FPAR algorithm. Remote Sensing of Environment, 78(3): 299-313 [DOI: 10.1016/S0034-4257(01)00225-5http://dx.doi.org/10.1016/S0034-4257(01)00225-5]
Wang Y T, Xie D H, Liu S, Hu R H, Li Y H and Yan G J. 2016. Scaling of FAPAR from the field to the satellite. Remote Sensing, 8(4): 310 [DOI: 10.3390/rs8040310http://dx.doi.org/10.3390/rs8040310]
Weiss M, Baret F, Garrigues S and Lacaze R. 2007. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sensing of Environment, 110: 317-331 [DOI: 10.1016/j.rse.2007.03.001http://dx.doi.org/10.1016/j.rse.2007.03.001]
Widlowski J L. 2010. On the bias of instantaneous FAPAR estimates in open-canopy forests. Agricultural and Forest Meteorology, 150(12): 1501-1522 [DOI: 10.1016/j.agrformet.2010.07.011http://dx.doi.org/10.1016/j.agrformet.2010.07.011]
Wu B F, Zeng Y and Huang J L. 2004. Overview of LAI/FPAR retrieval from remotely sensed data. Advances in Earth Science, 19(4): 585-590
吴炳方, 曾源, 黄进良. 2004. 遥感提取植物生理参数LAI/FPAR的研究进展与应用. 地球科学进展, 19(4): 585-590 [DOI: 10.3321/j.issn:1001-8166.2004.04.015http://dx.doi.org/10.3321/j.issn:1001-8166.2004.04.015]
Wu C Y, Niu Z and Gao S A. 2010. Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize. Journal of Geophysical Research: Atmospheres, 115(D12): D12127 [DOI: 10.1029/2009JD013023http://dx.doi.org/10.1029/2009JD013023]
Wu C Y, Niu Z, Tang Q, Huang W J, Rivard B and Feng J L. 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149(6/7): 1015-1021 [DOI: 10.1016/j.agrformet.2008.12.007http://dx.doi.org/10.1016/j.agrformet.2008.12.007]
Xiao X M, Hollinger D, Aber J, Goltz M, Davidson E A, Zhang Q Y and Moore III B. 2004b. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 89(4): 519-534 [DOI: 10.1016/j.rse.2003.11.008http://dx.doi.org/10.1016/j.rse.2003.11.008]
Xiao X M, Zhang Q Y, Braswell B, Urbanski S, Boles S, Wofsy S, Moore III B and Ojima D. 2004a. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91(2): 256-270 [DOI: 10.1016/j.rse.2004.03.010http://dx.doi.org/10.1016/j.rse.2004.03.010]
Xiao Z Q, Liang S L, Sun R, Wang J D and Jiang B. 2015b. Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product. Remote Sensing of Environment, 171: 105-117 [DOI: 10.1016/j.rse.2015.10.016http://dx.doi.org/10.1016/j.rse.2015.10.016]
Xiao Z Q, Liang S L, Wang J D, Xie D H, Song J L and Fensholt R. 2015a. A framework for consistent estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from MODIS time-series data. IEEE Transactions on Geoscience and Remote Sensing, 53(6): 3178-3197 [DOI: 10.1109/TGRS.2014.2370071http://dx.doi.org/10.1109/TGRS.2014.2370071]
Yan K, Park T, Chen C, Xu B D, Song W J, Yang B, Zeng Y L, Liu Z, Yan G J, Knyazikhin Y and Myneni R B. 2018. Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2119-2137 [DOI: 10.1109/TGRS.2017.2775247http://dx.doi.org/10.1109/TGRS.2017.2775247]
Yan K, Park T, Yan G J, Chen C, Yang B, Liu Z, Nemani R R, Knyazikhin Y and Myneni R B. 2016a. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: consistency and improvements. Remote Sensing, 8(5): 359 [DOI: 10.3390/rs8050359http://dx.doi.org/10.3390/rs8050359]
Yan K, Park T, Yan G J, Liu Z, Yang B, Chen C, Nemani R R, Knyazikhin Y and Myneni R B. 2016b. Evaluation of MODIS LAI/FPAR product collection 6. part 2: validation and intercomparison. Remote Sensing, 8(6): 460 [DOI: 10.3390/rs8060460http://dx.doi.org/10.3390/rs8060460]
Yoshida Y, Joiner J, Tucker C, Berry J, Lee J E, Walker G, Reichle R, Koster R, Lyapustin A and Wang Y. 2015. The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: insights from modeling and comparisons with parameters derived from satellite reflectances. Remote Sensing of Environment, 166: 163-177 [DOI: 10.1016/j.rse.2015.06.008http://dx.doi.org/10.1016/j.rse.2015.06.008]
Yuan W P, Cai W W, Xia J Z, Chen J Q, Liu S G, Dong W J, Merbold L, Law B, Arain A, Beringer J, Bernhofer C, Black A, Blanken P D, Cescatti A, Chen Y, Francois L, Gianelle D, Janssens I A, Jung M, Kato T, Kiely G, Liu D, Marcolla B, Montagnani L, Raschi A, Roupsard O, Varlagin A and Wohlfahrt G. 2014. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agricultural and Forest Meteorology, 192-193: 108-120 [DOI: 10.1016/j.agrformet.2014.03.007http://dx.doi.org/10.1016/j.agrformet.2014.03.007]
Zhang Q Y, Cheng Y B, Lyapustin A I, Wang Y J, Gao F, Suyker A, Verma S and Middleton E M. 2014a. Estimation of crop gross primary production (GPP): fAPARchl versus MOD15A2 FPAR. Remote Sensing of Environment, 153: 1-6 [DOI: 10.1016/j.rse.2014.07.012http://dx.doi.org/10.1016/j.rse.2014.07.012]
Zhang Q Y, Cheng Y B, Lyapustin A I, Wang Y J, Xiao X M, Suyker A, Verma S, Tan B and Middleton E M. 2014b. Estimation of crop Gross Primary Production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics. Agricultural and Forest Meteorology, 191: 51-63 [DOI: 10.1016/j.agrformet.2014.02.002http://dx.doi.org/10.1016/j.agrformet.2014.02.002]
Zhang Q Y, Cheng Y B, Lyapustin A I, Wang Y J, Zhang X Y, Suyker A, Verma S, Shuai Y M and Middleton E M. 2014c. Estimation of crop Gross Primary Production (GPP): II. do scaled MODIS vegetation indices improve performance? Agricultural and Forest Meteorology, 200: 1-8 [DOI: 10.1016/j.agrformet.2014.09.003http://dx.doi.org/10.1016/j.agrformet.2014.09.003]
Zhang Q Y, Middleton E M, Cheng Y B and Landis D R. 2013. Variations of foliage chlorophyll fAPAR and foliage non-chlorophyll fAPAR (fAPARchl, fAPARnon-chl) at the Harvard forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5): 2254-2264 [DOI: 10.1109/JSTARS.2013.2275176http://dx.doi.org/10.1109/JSTARS.2013.2275176]
Zhang Q Y, Middleton E M, Gao B C and Cheng Y B. 2012. Using EO-1 Hyperion to simulate HyspIRI products for a coniferous forest: the fraction of PAR absorbed by chlorophyll (fAPARchl) and leaf water content (LWC). IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1844-1852 [DOI: 10.1109/TGRS.2011.2169267http://dx.doi.org/10.1109/TGRS.2011.2169267]
Zhang Q Y, Middleton E M, Margolis H A, Drolet G G, Barr A A and Black T A. 2009. Can a satellite-derived estimate of the fraction of PAR absorbed by chlorophyll (FAPARchl) improve predictions of light-use efficiency and ecosystem photosynthesis for a boreal aspen forest? Remote Sensing of Environment, 113(4): 880-888 [DOI: 10.1016/j.rse.2009.01.002http://dx.doi.org/10.1016/j.rse.2009.01.002]
Zhang Q Y, Xiao X M, Braswell B, Linder E, Baret F and Moore III B. 2005. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sensing of Environment, 99(3): 357-371 [DOI: 10.1016/j.rse.2005.09.009http://dx.doi.org/10.1016/j.rse.2005.09.009]
Zhang Y, Xiao X M, Wolf S, Wu J, Wu X C, Gioli B, Wohlfahrt G, Cescatti A, van der Tol C, Zhou S, Gough C M, Gentine P, Zhang Y G, Steinbrecher R and Ardö J. 2018. Spatio-temporal convergence of maximum daily light-use efficiency based on radiation absorption by canopy chlorophyll. Geophysical Research Letters, 45(8): 3508-3519 [DOI: 10.1029/2017GL076354http://dx.doi.org/10.1029/2017GL076354]
Zhao P, Fan W J, Liu Y, Mu X H, Xu X R and Peng J J. 2016. Study of the remote sensing model of FAPAR over rugged terrains. Remote Sensing, 8(4): 309 [DOI: 10.3390/rs8040309http://dx.doi.org/10.3390/rs8040309]
Zhou X D, Zhu Q J, Wang J D, Sun R, Chen X and Wu M X. 2002. Interception of PAR, relationship between FPAR and LAI in summer maize canopy. Journal of Natural Resources, 17(1): 110-116
周晓东, 朱启疆, 王锦地, 孙睿, 陈雪, 吴门新. 2002. 夏玉米冠层内PAR截获及FPAR与LAI的关系. 自然资源学报, 17(1): 110-116 [DOI: 10.11849/zrzyxb.2002.01.016http://dx.doi.org/10.11849/zrzyxb.2002.01.016]
Zhu Z C, Bi J, Pan Y Z, Ganguly S, Anav A, Xu L, Samanta A, Piao S L, Nemani R R and Myneni R B. 2013. Global data sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sensing, 5(2): 927-948 [DOI: 10.3390/rs5020927http://dx.doi.org/10.3390/rs5020927]
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