Diagnostic assessment of European gross primary production

Global Change Biology

Volumn 14, Issue 10, 2008, Pages 2349-2364

  Jung, Martin ^a,b,c   Verstraete, Michel ^c   Gobron, Nadine ^c   Reichstein, Markus ^a   Papale, Dario ^d   Bondeau, Alberte ^e   Robustelli, Monica ^b   Pinty, Bernard ^b  

^a MAX PLANCK INSTITUTE FOR BIOGEOCHEMISTRY   (Germany)

^b INTERNATIONAL MAX PLANCK RESEARCH SCHOOL ON EARTH SYSTEM MODELLING   (Germany)

^c JOINT RESEARCH CENTRE   (Italy)

^d UNIVERSITY OF TUSCIA   (Italy)

^e POTSDAM INSTITUTE FOR CLIMATE IMPACT RESEARCH   (Germany)

Author keywords

Carbon cycle; Eddy covariance; Europe; FAPAR; GPP; Model

Indexed keywords

ANNUAL VARIATION; ARTIFICIAL NEURAL NETWORK; CARBON CYCLE; EDDY COVARIANCE; ENVIRONMENTAL CONDITIONS; ESTIMATION METHOD; GROWING SEASON; PHOTOSYNTHESIS; PRIMARY PRODUCTION;

EURASIA; EUROPE;

EID: 52449126202     PISSN: 13541013     EISSN: 13652486     Source Type: Journal    
DOI: 10.1111/j.1365-2486.2008.01647.x     Document Type: Article

References (65)

1. 2 storage and advection conditions at night at diferent carboeuroflux sites. Boundary-Layer Meteorology, 116, 63-93.
2. Beer C, Reichstein M, Ciais P, Farquhar GD, Papale D (2007) Mean annual GPP of Europe derived from its water balance. Geophysical Research Letters, 34, 1-4.
3. Bondeau A, Smith PC, Zaehle S et al. (2007) Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology, 13, 679-706.
4. 2 flux. Vegetatio, 80, 71-89.
5. Bradley BA, Jacob RW, Hermance JF, Mustard JF (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment, 106, 137-145.
6. 2 increase and climate change during the last 150 years. Global Change Biology, 10, 1253-1266.
7. Chen Y, Churkina C, Heimann M (2007) A comparison of regional climate variables between various data sources. Technical Reports, Max-Planck-Institut für Biogeochemie 8, 36 pp.
8. Ciais P, Reichstein M, Viovy N et al. (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529-533.
9. Cook EA, Iverson LR, Graham RL (1989) Estimating forest productivity with thematic mapper and biogeographical data. Remote Sensing of Environment, 28, 131-141.
10. Desai AR, Richardson AD, Moffat AM et al. (2008) Cross-site evaluation of eddy covariance GPP and RE decomposition techniques. Agricultural and Forest Meteorology, 148, 821-838.
11. Duchemin B, Goubier J, Courrier G (1999) Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sensing of Environment, 67, 68-82.
12. Feser F, Weisse R, von Storch H (2001) Multi-decadal atmospheric modeling for Europe yields multi-purpose data. EOS Transactions, 82, 305-310.
13. Friedlingstein P, Cox PM, Betts RA et al. (2006) Climate-carbon cycle feedback analysis: Results from C4MIP model intercomparison. Journal of Climate, 19, 3337-3353.
14. Gobron N, Pinty B, Aussedat O et al. (2006) Evaluation FAPAR products for different canopy radiation transfer regimes: Methodology and results using JRC products derived from SeaWiFS against ground-based estimations. Journal of Geophysical Research - Atmospheres, 111, 1-15.
15. Gobron N, Pinty B, Aussedat O et al. (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, 1871-1883.
16. Gobron N, Pinty B, Melin F et al. (2005) The state vegetation in Europe following the 2003 drought. International Journal of Remote Sensing Letters, 26, 2013-2020.
17. Gobron N, Pinty B, Verstraete M, Wildowski J-L (2000) Advanced vegetation indices optimized for up-coming sensors: Design, performance and applications. IEEE Transactions on Geoscience and Remote Sensing, 38, 2489-2505.
18. Goward SN, Tucker C, Dye D (1985) North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio, 64, 3-14.
19. Hagemann S, Machenhauer B, Jones R, Christensen OB, Deque M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Climate Dynamics, 23, 547-567.
20. Haxeltine A, Prentice IC (1996) BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability and competition among plant functional types. Global Biogeochemical Cycles, 10, 693-710.
21. Heinsch FA, Zhao MS, Running SW et al. (2006) Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing, 44, 1908-1925.
22. Jacob D, Bärring L, Christensen OB et al. (2007) An inter-comparison of regional climate models for Europe: Model performance in present-day climate. Climatic Change, 81, 31-52.
23. Jacob D, Podzun R (1997) Sensitivity studies with the regional climate model REMO. Meteorology and Atmospheric Physics, 63, 119-129.
24. Jung M, Henkel K, Herold M, Churkina G (2006) Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, 534-553.
25. Jung M, Le Maire G, Zaehle S et al. (2007a) Assessing the ability of three land ecosystem models to simulate gross carbon uptake of forests from boreal to Mediterranean climate in Europe. Biogeosciences, 4, 647-656.
26. Jung M, Vetter M, Herold M et al. (2007b) 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, GB4021.
27. Kalnay E, Kanamitsu M, Kistler R et al. (1996) The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77, 437-471.
28. Krinner G, Viovy N, de Noblet-Ducoudre N et al. (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19, GB1015.
29. Litton CM, Raich JW, Ryan MG (2007) Carbon allocation in forest ecosystems. Global Change Biology, 13, 2089-2109.
30. Lucht W, Prentice IC, Myneni RB et al. (2002) Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296, 1687-1689.
31. Luyssaert S, Janssens IA, Sulkava M et al. (2007) Photosynthesis drives anomalies in net carbon-exchange of pine forests at different latitudes. Global Change Biology, 13, 2110-2127.
32. 2 exchanges of an alpine forest. Agricultural and Forest Meteorology, 130, 193-206.
33. Moffat AM, Papale D, Reichstein M et al. (2007) Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology, 147, 209-232.
34. Monsi M, Saeki T (1953) Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. Japanese Journal of Botany, 14, 22-52.
35. Monteith JL (1965) Light distribution and photosynthesis in field crops. Annals of Botany, 29, 17-37.
36. Morales P, Sykes MT, Prentice IC et al. (2005) Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Global Change Biology, 11, 2211-2233.
37. Mu QZ, Zhao MS, Heinsch FA, Liu ML, Tian HQ, Running SW (2007) Evaluating water stress controls on primary production in biogeochemical and remote sensing based models. Journal of Geophysical Research - Biogeosciences, 112, G01012.
38. 2 flux under varied sky conditions. Remote Sensing of Environment, 109, 274-284.
39. Papale D, Reichstein M, Aubinet M et al. (2006) Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: Algorithms and uncertainty estimation. Biogeosciences, 3, 571-583.
40. Papale D, Valentini A (2003) A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology, 9, 525-535.
41. Pinty B, Lavergne T, Vossbeck M et al. (2007) Retrieving surface parameters for climate models from Moderate Resolution Imaging Spectroradiometer (MODIS)-Multiangle Imaging Spectroradiometer (MISR) albedo products. Journal of Geophysical Research -Atmospheres, 112, D10116.
42. Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) A global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography, 19, 117-134.
43. Rahman AF, Sims DA, Cordova VD, El-Masri BZ (2005) Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes. Geophysical Research Letters, 32, L19404.
44. Reichstein M (2006) Integration of FLUXNET and Earth observation data with biogeochemical modelling. iLEAPS Newsletter, 3, 32-34.
45. Reichstein M, Ciais P, Papale D et al. (2007a) Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: A joint flux tower, remote sensing and modelling analysis. Global Change Biology, 13, 634-651.
46. Reichstein M, Falge E, Baldocchi D et al. (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology, 11, 1424-1439.
47. Reichstein M, Papale D, Valentini R et al. (2007b) Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophysical Research Letters, 34, L01402.
48. Running SW, Nemani RR, Heinsch FA, Zhao MS, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54, 547-560.
49. Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96, 366-374.
50. Schaphoff S, Lucht W, Gerten D, Sitch S, Cramer W, Prentice IC (2006) Terrestrial biosphere carbon storage under alternative climate projections. Climatic Change, 74, 97-122.
51. 2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sensing of Environment, 103, 289-303.
52. Sims DA, Rahman AF, Cordova VD et al. (2006b) On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research - Biogeosciences, 111, G04015.
53. 2 and climate. Global Biogeochemical Cycles, 19, GB2013.
54. Sitch S, Smith B, Prentice IC et al. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161-185.
55. Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems, 89, 225-247.
56. Thornton P (1998) Regional ecosystem simulation: Combining surface- and satellite-based observations to study linkages between terrestrial energy and mass budgets. PhD thesis, University of Montana, Missoula.
57. Uppala SM, Kallberg PW, Simmons AJ et al. (2005) The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society, 131, 2961-3012.
58. van Dijk A, Dolman A, Schulze ED (2005) Radiation, temperature, and leaf area explain ecosystem carbon fluxes in boreal and temperate European forests. Global Biogeochemical Cycles, 19, GB2029.
59. Verstraete MM, Gobron N, Aussedat O, Robustelli M, Pinty B, Widlowski JL, Taberner M (2007) An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products. Advances in Space Research, in press.
60. Vetter M, Churkina G, Jung M et al. (2008) Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly in Europe using seven models. Biogeosciences, 5, 561-583.
61. White MA, Nemani RR (2006) Real-time monitoring and short-term forecasting of land surface phenology. Remote Sensing of Environment, 104, 43-49.
62. Xiao XM, Hollinger D, Aber J, Goltz M, Davidson EA, Zhang QY, Moore B (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment, 89, 519-534.
63. Yang L, Ichii K, White MA et al. (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, 109-122.
64. Zhang X, Friedl MA, Schaaf CB et al. (2003) Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84, 471-475.
65. Zhao M, Running SW, Nemani RR (2006) Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Journal of Geophysical Research - Biogeosciences, 111, G01002.