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Published： 2018-3 ，
Accepted： 12 September 2017
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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): 234–254
Lin S R, Li J and Liu Q H. 2018. Overview on estimation accuracy of gross primary productivity with remote sensing methods. Journal of Remote Sensing, 22(2): 234–254
准确估算陆地总初级生产力GPP(Gross Primary Productivity)数值对碳循环过程模拟有重要影响。本文介绍了多种基于植被指数以及基于光能利用率的遥感GPP算法，综述了不同算法在其研究区域的估算精度；并分析了MODIS/GPP以及BESS/GPP两种遥感GPP产品在不同植被类型的估算精度。通过对比全球碳通量站网络GPP数据表明，MODIS/GPP产品在全球估算结果具显著相关性(
R
2
=0.59)及中等标准误差(RMSE=2.86 gC/m
2
/day)，估算精度较高的植被类型有落叶阔叶林，草地等；估算精度较低类型包括常绿阔叶林，稀树草原等。本文对GPP产品中存在的不确定性进行分析，通过综述前人研究中发现的遥感估算GPP方法中存在的问题，指出可能的提高卫星遥感GPP产品估算精度的方法及发展趋势。
Gross primary productivity (GPP) is an important parameter in describing terrestrial ecosystem productivity. This review surveys the existing remote sensing GPP estimation algorithms including vegetation index based and light use efficiency based models and their accuracies
and summarizes two 1 km spatial resolution GPP product accuracy under eight different vegetation types. MOD17
which is the most commonly used GPP product
provides global-scale spatio-temporal continuous data. A strong correlation exists between global-scale MODIS/GPP and in-situ measurement (
R
2
=0.59) with medium estimation accuracy (RMSE=2.86 gC/m
2
/day). Estimation accuracy is high in deciduous broadleaved and evergreen coniferous forests but low in evergreen broadleaved forests and savanna. Finally
we analyze the uncertainties in GPP estimation and verification with the remote sensing method and suggest possible approaches to improve the accuracy of GPP estimation and its development tendency.
陆地总初级生产力遥感陆地生态系统模型MODIS/GPP产品BESS/GPP产品碳循环全球变化
gross primary productivityterrestrial ecosystem modelremote sensing GPP algorithmMODIS/GPP productBESS/GPP productterrestrial carbon cycleglobal change
Alton P B, North P R and Los S O. 2007. The impact of diffuse sunlight on canopy light-use efficiency, gross photosynthetic product and net ecosystem exchange in three forest biomes. Global Change Biology, 13(4): 776–787
Baldocchi D. 2008. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany, 56(1): 1–26
Baldocchi D, Falge E, Gu L H, Olson R, Hollinger D, Running S, Anthoni P, Bernhofer C, Davis K, Evans R, Fuentes J, Goldstein A, Katul G, Law B, Lee X, Malhi Y, Meyers T, Munger W, Oechel W, Paw K T, Pilegaard K, Schmid H P, Valentini R, Verma S, Vesala T, Wilson K and Wofsy S. 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82(11): 2415–2434
Baldocchi D D. 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9(4): 479–492
Beer C, Reichstein M, Tomelleri E, Ciais P, Jung M, Carvalhais N, Rödenbeck C, Arain M A, Baldocchi D, Bonan G B, Bondeau A, Cescatti A, Lasslop G, Lindroth A, Lomas M, Luyssaert S, Margolis H, Oleson K W, Roupsard O, Veenendaal E, Viovy N, Williams C, Woodward F I and Papale D. 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993): 834–838
Bonan G B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320(5882): 1444–1449
Box E O, Holben B N and Kalb V. 1989. Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux. Vegetatio, 80(2): 71–89
Burba G and Anderson D. 2007. Introduction to the Eddy Covariance Method: General Guidelines and Conventional Workflow. [s.l.]: LI-COR Biosciences
曹吉鑫, 田赟, 王小平, 孙向阳. 2009. 森林碳汇的估算方法及其发展趋势. 生态环境学报, 18(5): 2001–2005
Cao J X, Tian Y, Wang X P and Sun X Y. 2009. Estimation methods of forest sequestration and their prospects. Ecology and Environmental Sciences, 18(5): 2001–2005 (
Chen B Z, Black T A, Coops N C, Hilker T, Trofymow J A and Morgenstern K. 2009. Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements. Boundary-Layer Meteorology, 130(2): 137–167
Chen B Z, Coops N C, Fu D J, Margolis H A, Amiro B D, Black T A, Arain M A, Barr A G, Bourque C P A, Flanagan L B, Lafleur P M, McCaughey J H and Wofsy S C. 2012. Characterizing spatial representativeness of flux tower eddy-covariance measurements across the Canadian Carbon Program Network using remote sensing and footprint analysis. Remote Sensing of Environment, 124: 742–755
Cheng Y B, Zhang Q Y, Lyapustin A I, Wang Y J and Middleton E M. 2014. Impacts of light use efficiency and fPAR parameterization on gross primary production modeling. Agricultural and Forest Meteorology, 189-190: 187–197
崔霞, 冯琦胜, 梁天刚. 2007. 基于遥感技术的植被净初级生产力研究进展. 草业科学, 24(10): 36–42
Cui X, Feng Q S and Liang T G. 2007. Research progress on remote sensing based net primary productivity of terrestrialVegetation. Pratacultural Science, 24(10): 36–42 (
Danelichen V H M, Biudes M S, Velasque M C S, Machado N G, Gomes R S R, Vourlitis G L and Nogueira J S. 2015. Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery. Anais da Academia Brasileira de Ciências, 87(3): 1545–1564
De Moura Y M, Hilker T, Gonçalves F G, Galvão L S, Dos Santos J R, Lyapustin A, Maeda E E and De Jesus Silva C V. 2016. Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations. International Journal of Applied Earth Observation and Geoinformation, 52: 580–590
Drolet G G, Huemmrich K F, Hall F G, Middleton E M, Black T A, Barr A G and Margolis H A. 2005. A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Remote Sensing of Environment, 98(2/3): 212–224
方精云, 柯金虎, 唐志尧, 陈安平. 2001. 生物生产力的“4P”概念、估算及其相互关系. 植物生态学报, 25(4): 414–419
Fang J Y, Ke J H, Tang Z Y and Chen A P. 2001. Implications and estimations of four terrestrial productivity parameters. Acta Phytoecologica Sinica, 25(4): 414–419 (
Frazier A E, Renschler C S and Miles S B. 2013. Evaluating post-disaster ecosystem resilience using MODIS GPP data. International Journal of Applied Earth Observation and Geoinformation, 21: 43–52
Gamon J A, Field C B, Bilger W, Björkman O, Fredeen A L and Peñuelas J. 1990. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia, 85(1): 1–7
高帅. 2011. 星载雷达估测植被总初级生产力研究. 北京: 中国科学院研究生院
Gao S. 2011. Study of spaceborne radar estimates of gross primary productivity. Beijing: Graduate University of Chinese Academy of Sciences
高艳妮, 于贵瑞, 张黎, 刘敏, 黄玫, 王秋凤. 2012. 中国陆地生态系统净初级生产力变化特征——基于过程模型和遥感模型的评估结果. 地理科学进展, 31(1): 109–117
Gao Y N, Yu G R, Zhang L, Liu M, Huang M and Wang Q F. 2012. The changes of net primary productivity in Chinese terrestrial ecosystem: based on process and parameter models. Progress in Geography, 31(1): 109–117 (
Garbulsky M F, Peñuelas J, Gamon J, Inoue Y and Filella I. 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sensing of Environment, 115(2): 281–297
Gebremichael M and Barros A P. 2006. Evaluation of MODIS Gross Primary Productivity (GPP) in tropical monsoon regions. Remote Sensing of Environment, 100(2): 150–166
Goulden M L, Munger J W, Fan S M, Daube B C and Wofsy S C. 1996. Measurements of carbon sequestration by long-term eddy covariance: methods and a critical evaluation of accuracy. Global Change Biology, 2(3): 169–182
Groenendijk M, Dolman A J, Van Der Molen M K, Leuning R, Arneth A, Delpierre N, Gash J H C, Lindroth A, Richardson A D, Verbeeck H and Wohlfahrt G. 2011. Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data. Agricultural and Forest Meteorology, 151(1): 22–38
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 Y 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
Hall F G, Hilker T and Coops N C. 2012. Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation. Remote Sensing of Environment, 121: 301–308
Hazarika M K, Yasuoka Y, Ito A and Dye D. 2005. Estimation of net primary productivity by integrating remote sensing data with an ecosystem model. Remote Sensing of Environment, 94(3): 298–310
He M Z, Ju W M, Zhou Y L, Chen J M, He H L, Wang S Q, Wang H M, Guan D X, Yan J H, Li Y N, Hao Y B and Zhao F H. 2013. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agricultural and Forest Meteorology, 173: 28–39
He M Z, Kimball J S, Running S, Ballantyne A, Guan K Y and Huemmrich F. 2016. Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index. Remote Sensing of Environment, 186: 173–183
Hollinger D Y and Richardson A D. 2005. Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiology, 25(7): 873–885
Huang K, Wang S Q, Zhou L, Wang H M, Zhang J H, Yan J H, Zhao L, Wang Y F and Shi P L. 2014. Impacts of diffuse radiation on light use efficiency across terrestrial ecosystems based on eddy covariance observation in China. PLoS One, 9(11): e110988
Huete A R, Didan K, Shimabukuro Y E, Ratana P, Saleska S R, Hutyra L R, Yang W Z, Nemani R R and Myneni R. 2006. Amazon rainforests green-up with sunlight in dry season. Geophysical Research Letters, 33(6): L06405
Huete A R and Jackson R D. 1988. Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment, 25(1): 89–105
Huete A R, Miura T, Kim Y, Didan K and Privette J. 2006. Assessments of multisensor vegetation index dependencies with hyperspectral and tower flux data//Proceedings of SPIE Volume 6298, Remote Sensing and Modeling of Ecosystems for Sustainability III. San Diego, California: SPIE: 629814 [DOI: 10.1117/12.681382]
Huete A R, Restrepo-Coupe N, Ratana P, Didan K, Saleska S R, Ichii K, Panuthai S and Gamo M. 2008. Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia. Agricultural and Forest Meteorology, 148(5): 748–760
Inoue Y, Peñuelas J, Miyata A and Mano M. 2008. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sensing of Environment, 112(1): 156–172
Jiang C Y and Ryu Y. 2016. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sensing of Environment, 186: 528–547
焦桐, 刘荣高, 刘洋, 陈镜明. 2014. 林下植被遥感反演研究进展. 地球信息科学学报, 16(4): 602–608
Jiao T, Liu R G, Liu Y and Chen J M. 2014. The progress of forest understory retrieval from remote sensing. Journal of Geo-Information Science, 16(4): 602–608 (
Jin C, Xiao X M, Merbold L, Arneth A, Veenendaal E and Kutsch W L. 2013. Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa. Remote Sensing of Environment, 135: 189–201
John R, Chen J Q, Noormets A, Xiao X M, Xu J Y, Lu N and Chen S P. 2013. Modelling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data. International Journal of Remote Sensing, 34(8): 2829–2857
Jung M, Reichstein M, Margolis H A, Cescatti A, Richardson A D, Arain M A, Arneth A, Bernhofer C, Bonal D, Chen J Q, Gianelle D, Gobron N, Kiely G, Kutsch W, Lasslop G, Law B E, Lindroth A, Merbold L, Montagnani L, Moors E J, Papale D, Sottocornola M, Vaccari F and Williams C. 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research: Biogeosciences, 116(G3): G00J07
Jung M, Vetter M, Herold M, Churkina G, Reichstein M, Zaehle S, Ciais P, Viovy N, Bondeau A, Chen Y M, Trusilova K, Feser F and Heimann M. 2007. Uncertainties of modeling gross primary productivity over Europe: a systematic study on the effects of using different drivers and terrestrial biosphere models. Global Biogeochemical Cycles, 21(4): GB4021
Kang S, Running S W, Lim J H, Zhao M S, Park C R and Loehman R. 2003. A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index. Remote Sensing of Environment, 86(2): 232–242
Keenan T F, Baker I, Barr A, Ciais P, Davis K, Dietze M, Dragoni D, Gough C M, Grant R, Hollinger D, Hufkens K, Poulter B, McCaughey H, Raczka B, Ryu Y, Schaefer K, Tian H Q, Verbeeck H, Zhao M S and Richardson S D. 2012. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange. Global Change Biology, 18(6): 1971–1987
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
Krinner G, Viovy N, De Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S and Prentice I C. 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19(1): GB1015
Law B E and Waring R H. 1994. Remote sensing of leaf area index and radiation intercepted by understory vegetation. Ecological Applications, 4(2): 272–279
李新, 刘绍民, 马明国, 肖青, 柳钦火, 晋锐, 车涛, 王维真, 祁元, 李弘毅, 朱高峰, 郭建文, 冉有华, 闻建光, 王树果. 2012. 黑河流域生态—水文过程综合遥感观测联合试验总体设计. 地球科学进展, 27(5): 481–498
Li X, Liu S M, Ma M G, Xiao Q, Liu Q H, Jin R, Che T, Wang W Z, Qi Y, Li H Y, Zhu G F, Guo J W, Ran Y H, Wen J G and Wang S G. 2012. HIWATER: an integrated remote sensing experiment on hydrological and ecological processes in the Heihe River Basin. Advances in Earth Sciences, 27(5): 481–498 (
Li Z Q, Yu G R, Xiao X M, Li Y N, Zhao X Q, Ren C Y, Zhang L M and Fu Y L. 2007. Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data. Remote Sensing of Environment, 107(3): 510–519
梁顺林, 李小文, 王锦地. 2013. 定量遥感理念与算法: 北京: 科学出版社
Liang S L, Li X W, Wang J D. 2013. Quantitative Remote Sensing: Principle and Algorithm. Beijing: Science Press
Liu J, Chen J M, Cihlar J and Chen W. 1999. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data. Journal of Geophysical Research: Atmospheres, 104(D22): 27735–27754
Liu J, Rambal S and Mouillot F. 2015a. Soil drought anomalies in MODIS GPP of a mediterranean broadleaved evergreen forest. Remote Sensing, 7(1): 1154–1180
Liu J F, Sun O J X, Jin H M, Zhou Z Y and Han X G. 2011. Application of two remote sensing GPP algorithms at a semiarid grassland site of North China. Journal of Plant Ecology, 4(4): 302–312
柳钦火, 辛晓洲, 唐娉, 廖静娟, 吴炳方. 2010. 定量遥感模型、应用及不确定性研究. 北京: 科学出版社
Liu Q H, Xin X Z, Tang P, Liao J J and Wu B F. 2010. Research of Quantitative Remote Sensing Models, Application and Uncertaincy [M]. Beijing: Science Press
Liu S S, Peng Y, Brunsell N and Guan Q F. 2015b. Remote estimation of GPP in temperate grassland: implications of the uncertainty in GPP estimation in semi-arid ecosystems using MODIS data//Proceedings of SPIE Volume 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII. San Diego, California: SPIE: 961015 [DOI: 10.1117/12.2186786]
Liu Z J, Wang L C and Wang S S. 2014. Comparison of different GPP models in China using MODIS image and ChinaFLUX data. Remote Sensing, 6(10): 10215–10231
Ma X L, Huete A, Yu Q, Coupe N R, Davies K, Broich M, Ratana P, Beringer J, Hutley L B, Cleverly J, Boulain N and Eamus D. 2013. Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote Sensing of Environment, 139: 97–115
Ma X L, Huete A, Yu Q, Restrepo-Coupe N, Beringer J, Hutley L B, Kanniah K D, Cleverly J and Eamus D. 2014. Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI. Remote Sensing of Environment, 154: 253–271
Mahadevan P, Wofsy S C, Matross D M, Xiao X M, Dunn A L, Lin J C, Gerbig C, Munger J W, Chow V Y and Gottlieb E W. 2008. A satellite-based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM). Global Biogeochemical Cycles, 22(2): GB2005
Massman W J and Lee X. 2002. Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agricultural and Forest Meteorology, 113(1/4): 121–144
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
Moura Y M, Galvão L S, Dos Santos J R, Roberts D A and Breunig F M. 2012. Use of MISR/Terra data to study intra- and inter-annual EVI variations in the dry season of tropical forest. Remote Sensing of Environment, 127: 260–270
Nakaji T, Kosugi Y, Takanashi S, Niiyama K, Noguchi S, Tani M, Oguma H, Nik A R and Kassim A R. 2014. Estimation of light-use efficiency through a combinational use of the photochemical reflectance index and vapor pressure deficit in an evergreen tropical rainforest at Pasoh, Peninsular Malaysia. Remote Sensing of Environment, 150: 82–92
Nguy-Robertson A, Suyker A and Xiao X M. 2015. Modeling gross primary production of maize and soybean croplands using light quality, temperature, water stress, and phenology. Agricultural and Forest Meteorology, 213: 160–172
Olofsson P, Lagergren F, Lindroth A, Lindström J, Klemedtsson L, Kutsch W and Eklundh L. 2008. Towards operational remote sensing of forest carbon balance across Northern Europe. Biogeosciences, 5(3): 817–832
Peng Y, Gitelson A A, Keydan G, Rundquist D C and Moses W. 2011. Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content. Remote Sensing of Environment, 115(4): 978–989
Propastin P, Ibrom A, Knohl A and Erasmi S. 2012. Effects of canopy photosynthesis saturation on the estimation of gross primary productivity from MODIS data in a tropical forest. Remote Sensing of Environment, 121: 252–260
Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D and Valentini R. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11(9): 1424–1439
Reichstein M, Papale D, Valentini R, Aubinet M, Bernhofer C, Knohl A, Laurila T, Lindroth A, Moors E, Pilegaard K and Seufert G. 2007. Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophysical Research Letters, 34(1): L01402
Running S W, Nemani R R, Heinsch F A, Zhao M S, Reeves M and Hashimoto H. 2004. A continuous satellite-derived measure of global terrestrial primary production. BioScience, 54(6): 547–560
Saleska S R, Wu J, Guan K Y, Araujo A C, Huete A, Nobre A D and Restrepo-Coupe N. 2016. Dry-season greening of Amazon forests. Nature, 531(7594): E4–E5
Sasai T, Okamoto K, Hiyama T and Yamaguchi Y. 2007. Comparing terrestrial carbon fluxes from the scale of a flux tower to the global scale. Ecological Modelling, 208(2/4): 135–144
Sánchez M L, Pardo N, Pérez I A and García M A. 2015. GPP and maximum light use efficiency estimates using different approaches over a rotating biodiesel crop. Agricultural and Forest Meteorology, 214-215: 444–455
Schmid H P. 2002. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agricultural and Forest Meteorology, 113(1/4): 159–183
Shi H, Li L H, Eamus D, Huete A, Cleverly J, Tian X, Yu Q, Wang S Q, Montagnani L, Magliulo V, Rotenberg E, Pavelka M and Carrara A. 2017. Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types. Ecological Indicators, 72: 153–164
Shugart H H, Saatchi S and Hall F G. 2010. Importance of structure and its measurement in quantifying function of forest ecosystems. Journal of Geophysical Research: Biogeosciences, 115(G2): G00E13
Sims D A, Rahman A F, Cordova V D, El-Masri B Z, Baldocchi D D, Flanagan L B, Goldstein A H, Hollinger D Y, Misson L, Monson R K, Oechel W C, Schmid H P, Wofsy S C and Xu L K. 2006. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research: Biogeosciences, 111(G4): G04015
Sims D A, Rahman A F, Cordova V D, El-Masri B Z, Baldocchi D D, Bolstad P V, Flanagan L B, Goldstein A H, Hollinger D Y, Misson L, Monson R K, Oechel W C, Schmid H P, Wofsy S C and Xu L K. 2008. A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS. Remote Sensing of Environment, 112(4): 1633–1646
Sitch S, Smith B, Prentice I C, Arneth A, Bondeau A, Cramer W, Kaplan J O, Levis S, Lucht W, Sykes M T, Thonicke K and Venevsky S. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9(2): 161–185
Sjöström M, Ardö J, Arneth A, Boulain N, Cappelaere B, Eklundh L, De Grandcourt, A, Kutsch W L, Merbold L, Nouvellon Y, Scholes R J, Schubert P, Seaquist J and Veenendaal E M. 2011. Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote Sensing of Environment, 115(4): 1081–1089
Sjöström M, Zhao M, Archibald S, Arneth A, Cappelaere B, Falk U, De Grandcourt A, Hanan N, Kergoat L, Kutsch W, Merbold L, Mougin E, Nickless A, Nouvellon Y, Scholes R J, Veenendaal E M and Ardö J. 2013. Evaluation of MODIS gross primary productivity for Africa using eddy covariance data. Remote Sensing of Environment, 131: 275–286
Soudani K, Hmimina G, Dufrêne E, Berveiller D, Delpierre N, Ourcival J M, Rambal S and Joffre R. 2014. Relationships between photochemical reflectance index and light-use efficiency in deciduous and evergreen broadleaf forests. Remote Sensing of Environment, 144: 73–84
Turner D P, Ritts W D, Cohen W B, Gower S T, Zhao M S, Running S W, Wofsy S C, Urbanski S, Dunn A L and Munger J W. 2003. Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sensing of Environment, 88(3): 256–270
Urban O, Janouš D, Acosta M, Czerný R, Marková I, Navrátil M, Pavelka M, Pokorný R, Šprtová M, Zhang R, Špunda V, Grace J and Marek M V. 2007. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Global Change Biology, 13(1): 157–168
Verma M, Friedl M A, Richardson A D, Kiely G, Cescatti A, Law B E, Wohlfahrt G, Gielen B, Roupsard O, Moors E J, Toscano P, Vaccari F P, Gianelle D, Bohrer G, Varlagin A, Buchmann N, van Gorsel E, Montagnani L and Propastin P. 2014. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set. Biogeosciences, 11(8): 2185–2200
Veroustraete F, Sabbe H and Eerens H. 2002. Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data. Remote Sensing of Environment, 83(3): 376–399
卫亚星, 王莉雯. 2010. 净初级生产力遥感估算模型尺度效应的研究. 资源科学, 32(9): 1783–1791
Wei Y X and Wang L W. 2010. Scale effect of vegetation net primary productivity models based on remote sensing. Resources Science, 32(9): 1783–1791 (
Wu C Y, Chen J M and Huang N. 2011. Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: evaluation and calibration. Remote Sensing of Environment, 115(12): 3424–3435
Wu C Y, Gonsamo A, Chen J M, Kurz W A, Price D T, Lafleur P M, Jassal R S, Dragoni D, Bohrer G, Gough C M, Verma S B, Suyker A E and Munger J W. 2012. Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: a North America flux data synthesis. Global and Planetary Change, 92-93: 179–190
Wu C Y, Huang W J, Yang Q Y and Xie Q Y. 2015. Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI). Agriculture, Ecosystems and Environment, 199: 333–338
Wu J, Albert L P, Lopes A P, Restrepo-Coupe N, Hayek M, Wiedemann K T, Guan K Y, Stark S C, Christoffersen B, Prohaska N, Tavares J V, Marostica S, Kobayashi H, Ferreira M L, Campos K S, da Silva R, Brando P M, Dye D G, Huxman T E, Huete A R, Nelson B W and Saleska S R. 2016. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science, 351(6276): 972–976
Xia J Y, Niu S L, Ciais P, Janssens I A, Chen J Q, Ammann C, Arain A, Blanken P D, Cescatti A, Bonal D, Buchmann N, Curtis P S, Chen S P, Dong J W, Flanagan L B, Frankenberg C, Georgiadis T, Gough C M, Hui D F, Kiely G, Li J W, Lund M, Magliulo V, Marcolla B, Merbold L, Montagnani L, Moors E J, Olesen J E, Piao S L, Raschi A, Roupsard O, Suyker A E, Urbaniak M, Vaccari F P, Varlagin A, Vesala T, Wilkinson M, Weng E S, Wohlfahrt G, Yan L M and Luo Y Q. 2015. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proceedings of the National Academy of Sciences of the United States of America, 112(9): 2788–2793
Xiao X M, Zhang Q Y, Braswell B, Urbanski S, Boles S, Wofsy S, Mooer III B and Ojima D. 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91(2): 256–270
Xiao X M, Zhang Q Y, Hollinger D, Aber J and Moore III B. 2005. Modeling gross primary production of an evergreen needleleaf forest using modis and climate data. Ecological Applications, 15(3): 954–969
Xin Q C, Broich M, Suyker A E, Yu L and Gong P. 2015. Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States. Agricultural and Forest Meteorology, 201: 111–119
Yang F H, Ichii K, White M A, Hashimoto H, Michaelis A R, Votava P, Zhu A X, Huete A, Running S W and Nemani R R. 2007. Developing a continental-scale measure of gross primary production by combining MODIS and AmeriFlux data through Support Vector Machine approach. Remote Sensing of Environment, 110(1): 109–122
Yang X, Tang J W, Mustard J F, Lee J E, Rossini M, Joiner J, Munger J W, Kornfeld A and Richardson A D. 2015. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters, 42(8): 2977–2987
袁文平, 蔡文文, 刘丹, 董文杰. 2014. 陆地生态系统植被生产力遥感模型研究进展. 地球科学进展, 29(5): 541–550
Yuan W P, Cai W W, Liu D and Dong W J. 2014. Satellite-based vegetation production models of terrestrial ecosystem: an overview. Advances in Earth Science, 29(5): 541–550 (
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
于贵瑞, 方华军, 伏玉玲, 王秋凤. 2011. 区域尺度陆地生态系统碳收支及其循环过程研究进展. 生态学报, 31(19): 5449–5459
Yu G R, Fang H J, Fu Y L and Wang Q F. 2011. Research on carbon budget and carbon cycle of terrestrial ecosystems in regional scale: a review. Acta Ecologica Sinica, 31(19): 5449–5459 (
Yu G R, Zhu X J, Fu Y L, He H L, Wang Q F, Wen X F, Li X R, Zhang L M, Zhang L, Su W, Li S G, Sun X M, Zhang Y P, Zhang J H, Yan J H, Wang H M, Zhou G S, Jia B R, Xiang W H, Li Y N, Zhao L, Wang Y F, Shi P L, Chen S P, Xin X P, Zhao F H, Wang Y Y And Tong C L. 2013. Spatial patterns and climate drivers of carbon fluxes in terrestrial ecosystems of China. Global Change Biology, 19(3): 798–810
于文涛, 李静, 柳钦火, 曾也鲁, 尹高飞, 赵静, 徐保东. 2016. 中国地表覆盖异质性参数提取与分析. 地球科学进展, 31(10): 1067–1077
Yu W T, Li J, Liu Q H, Zeng Y L, Yin G F, Zhao J and Xu B D. 2016. Extraction and analysis of land cover heterogeneity over China. Advances in Earth Science, 31(10): 1067–1077 (
Yuan W P, Liu S G, Zhou G S, Zhou G Y, Tieszen L L, Baldocchi D, Bernhofer C, Gholz H, Goldstein A H, Goulden M L, Hollinger D Y, Hu Y M, Law B E, Stoy P C, Vesala T and Wofsy S C. 2007. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology, 143(3/4): 189–207
Zhang F M, Chen J M, Chen J Q, Gough C M, Martin T A and Dragoni D. 2012. Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U. S. against flux measurements and a process model. Remote Sensing of Environment, 124: 717–729
Zhang L M, Yu G R, Sun X M, Wen X F, Ren C Y, Fu Y L, Li Q K, Li Z Q, Liu Y F, Guan D X and Yan J H. 2006. Seasonal variations of ecosystem apparent quantum yield (α) and maximum photosynthesis rate (Pmax) of different forest ecosystems in China. Agricultural and Forest Meteorology, 137(3/4): 176–187
Zhang M, Yu G R, Zhang L M, Sun X M, Wen X F, Han S J and Yan J H. 2009. Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China. Biogeosciences Discussions, 6(4): 8215–8245
Zhang M, Yu G R, Zhuang J, Gentry R, Fu Y L, Sun X M, Zhang L M, Wen X F, Wang Q F, Han S J, Yan J H, Zhang Y P, Wang Y F and Li Y N. 2011. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agricultural and Forest Meteorology, 151(7): 803–816
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. 2014a. 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
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. 2014b. Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance? Agricultural and Forest Meteorology, 200: 1–8
Zhao M S, Running S, Heinsch F A and Nemani R. 2010. MODIS-Derived terrestrial primary production//Ramachandran B, Justice C O and Abrams M J. Land Remote Sensing and Global Environmental Change. New York, NY: Springer: 635–660
Zhao M S, Running S W and Nemani R R. 2006. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Journal of Geophysical Research: Biogeosciences, 111(G1): G01002
赵英时. 2013. 遥感应用分析原理与方法. 2版. 北京: 科学出版社, 374–375
Zhao Y S. 2013. Principle and methods of analysis for remote sensing application. Beijing: Science Press, 374–375
Zhou Y L, Wu X C, Ju W M, Chen J M, Wang S Q, Wang H M, Yuan W P, Black T A, Jassal R, Ibrom A, Han S J, Yan J H, Margolis H, Roupsard O, Li Y N, Zhao F H, Kiely G, Starr G, Pavelka M, Montagnani L, Wohlfahrt G, D’Odorico P, Cook D, Arain M A, Bonal D, Beringer J, Blanken P D, Loubet B, Leclerc M Y, Matteucci G, Nagy Z, Olejnik J, Paw U K T and Varlagin A. 2016. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. Journal of Geophysical Research: Biogeosciences, 121(4): 1045–1072
朱治林, 孙晓敏, 温学发, 周艳莲, 田静, 袁国富. 2006. 中国通量网(ChinaFLUX)夜间CO2涡度相关通量数据处理方法研究. 中国科学 D辑 地球科学, 36(S1): 34–44
Zhu Z L, Sun X M, Wen X F, Zhou Y L, Tian J and Yuan G F. 2006. Study on the processing method of nighttime CO2 eddy covariauce flux data in China FLUX. Science in China Series D: Earth Sciences, 49(S3): 36–46
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