References

1. 1.
   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
2. 2.
   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
3. 3.
   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
4. 4.
   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
5. 5.
   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
6. 6.
   Bonan G B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 320(5882): 1444–1449
7. 7.
   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
8. 8.
   Burba G and Anderson D. 2007. Introduction to the Eddy Covariance Method: General Guidelines and Conventional Workflow. [s.l.]: LI-COR Biosciences
9. 9.
   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 (
10. 10.
    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
11. 11.
    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
12. 12.
    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
13. 13.
    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 (
14. 14.
    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
15. 15.
    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
16. 16.
    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
17. 18.
    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 (
18. 19.
    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
19. 20.
    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
20. 21.
    Gao S. 2011. Study of spaceborne radar estimates of gross primary productivity. Beijing: Graduate University of Chinese Academy of Sciences
21. 22.
    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 (
22. 23.
    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
23. 24.
    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
24. 25.
    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
25. 26.
    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
26. 27.
    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
27. 28.
    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
28. 29.
    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
29. 30.
    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
30. 31.
    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
31. 32.
    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
32. 33.
    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
33. 34.
    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
34. 35.
    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
35. 36.
    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]
36. 37.
    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
37. 38.
    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
38. 39.
    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
39. 40.
    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 (
40. 41.
    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
41. 42.
    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
42. 43.
    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
43. 44.
    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
44. 45.
    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
45. 46.
    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
46. 47.
    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
47. 48.
    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
48. 49.
    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
49. 50.
    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 (
50. 51.
    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
51. 52.
    Liang S L, Li X W, Wang J D. 2013. Quantitative Remote Sensing: Principle and Algorithm. Beijing: Science Press
52. 53.
    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
53. 54.
    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
54. 55.
    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
55. 56.
    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
56. 57.
    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]
57. 58.
    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
58. 59.
    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
59. 60.
    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
60. 61.
    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
61. 62.
    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
62. 63.
    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
63. 64.
    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
64. 65.
    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
65. 66.
    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
66. 67.
    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
67. 68.
    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
68. 69.
    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
69. 70.
    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
70. 71.
    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
71. 72.
    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
72. 73.
    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
73. 74.
    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
74. 75.
    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
75. 76.
    Schmid H P. 2002. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agricultural and Forest Meteorology, 113(1/4): 159–183
76. 77.
    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
77. 78.
    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
78. 79.
    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
79. 80.
    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
80. 81.
    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
81. 82.
    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
82. 83.
    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
83. 84.
    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
84. 85.
    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
85. 86.
    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
86. 87.
    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
87. 88.
    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
88. 89.
    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 (
89. 90.
    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
90. 91.
    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
91. 92.
    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
92. 93.
    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
93. 94.
    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
94. 95.
    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
95. 96.
    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
96. 97.
    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
97. 98.
    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
98. 99.
    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
99. 100.
    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 (
100. 101.
     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
101. 102.
     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 (
102. 103.
     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
103. 104.
     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 (
104. 105.
     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
105. 106.
     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
106. 107.
     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 (P_max) of different forest ecosystems in China. Agricultural and Forest Meteorology, 137(3/4): 176–187
107. 108.
     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
108. 109.
     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
109. 110.
     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
110. 111.
     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
111. 112.
     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
112. 113.
     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
113. 114.
     Zhao Y S. 2013. Principle and methods of analysis for remote sensing application. Beijing: Science Press, 374–375
114. 115.
     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
115. 116.
     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