Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables

References

• Asner, G. P., R. E. Martin, D. E. Knapp, R. Tupayachi, C. Anderson, L. Carranza, P. Martinez, M. Houcheime, F. Sinca, and P. Weiss. 2011. “Spectroscopy of Canopy Chemicals in Humid Tropical Forests.” Remote Sensing of Environment 115 (12): 3587–3598. doi:10.1016/j.rse.2011.08.020.
   Web of Science ®Google Scholar
• Baccini, A., S. J. Goetz, W. S. Walker, N. T. Laporte, M. Sun, D. Sulla-Menashe, J. Hackler, et al. 2012. “Estimated Carbon Dioxide Emissions From Tropical Deforestation Improved by Carbon-Density Maps.” Nature Climate Change 2 (3): 182–185. doi:10.1038/nclimate1354.
   Web of Science ®Google Scholar
• Bala, G., K. Caldeira, M. Wickett, T. J. Phillips, D. B. Lobell, C. Delire, and A. Mirin. 2007. “Combined Climate and Carbon-Cycle Effects of Large-Scale Deforestation.” Proceedings of the National Academy of Sciences 104 (16): 6550–6555. doi:10.1073/pnas.0608998104.
   PubMed Web of Science ®Google Scholar
• Bloom, A. A., and M. Williams. 2015. “Constraining Ecosystem Carbon Dynamics in a Data-Limited World: Integrating Ecological ‘Common Sense’ in a Model-Data Fusion Framework.” Biogeosciences (online) 12 (5): 1299–1315. doi:10.5194/bg-12-1299-2015.
   Web of Science ®Google Scholar
• Brovkin, V., L. Boysen, T. Raddatz, V. Gayler, A. Loew, and M. Claussen. 2013. “Evaluation of Vegetation Cover and Land-Surface Albedo in MPI-ESM CMIP5 Simulations.” Journal of Advances in Modeling Earth Systems 5 (1): 48–57. doi:10.1029/2012MS000169.
   Web of Science ®Google Scholar
• Butler, E. E., A. Datta, H. Flores-Moreno, M. Chen, K. R. Wythers, F. Fazayeli, A. Banerjee, et al. 2017. “Mapping Local and Global Variability in Plant Trait Distributions.” Proceedings of the National Academy of Sciences 201708984. doi:10.1073/pnas.1708984114.
   Web of Science ®Google Scholar
• Carvalhais, N., M. Forkel, M. Khomik, J. Bellarby, M. Jung, M. Migliavacca, M. Mu, et al. 2014. “Global Covariation of Carbon Turnover Times with Climate in Terrestrial Ecosystems.” Nature 514 (7521): 213–217. doi:10.1038/nature13731.
   PubMed Web of Science ®Google Scholar
• Chambers, J. Q, G. P Asner, D. C Morton, L. O Anderson, S. S Saatchi, F. D.B. Espírito-Santo, M. Palace, and C. Souza. 2007. “Regional Ecosystem Structure and Function: Ecological Insights From Remote Sensing of Tropical Forests.” Trends in Ecology and Evolution. doi:10.1016/j.tree.2007.05.001.
   PubMed Web of Science ®Google Scholar
• Chang, J. F., N. Viovy, N. Vuichard, P. Ciais, T. Wang, A. Cozic, R. Lardy, et al. 2013. “Incorporating Grassland Management in ORCHIDEE: Model Description and Evaluation at 11 Eddy-Covariance Sites in Europe.” Geoscientific Model Development 6 (6): 2165–2181. doi:10.5194/gmd-6-2165-2013.
   Web of Science ®Google Scholar
• Chuvieco, E., J. Lizundia-Loiola, M. L. Pettinari, R. Ramo, M. Padilla, K. Tansey, F. Mouillot, et al. 2018. “Generation and Analysis of a New Global Burned Area Product Based on MODIS 250 m Reflectance Bands and Thermal Anomalies.” Earth System Science Data 10 (4): 2015–2031. doi:10.5194/essd-10-2015-2018.
   Web of Science ®Google Scholar
• Chuvieco, E., C. Yue, A. Heil, F. Mouillot, I. Alonso-Canas, M. Padilla, J. M. Pereira, D. Oom, and K. Tansey. 2016. “A New Global Burned Area Product for Climate Assessment of Fire Impacts.” Global Ecology and Biogeography 25 (5): 619–629. doi:10.1111/geb.12440.
   Web of Science ®Google Scholar
• Ciais, Ph, M. Reichstein, N. Viovy, A. Granier, J. Ogée, V. Allard, M. Aubinet, et al. 2005. “Europe-Wide Reduction in Primary Productivity Caused by the Heat and Drought in 2003.” Nature 437 (7058): 529–533. doi:10.1038/nature03972.
   PubMed Web of Science ®Google Scholar
• Clark, M. L., D. A. Roberts, and D. B. Clark. 2005. “Hyperspectral Discrimination of Tropical Rain Forest Tree Species at Leaf to Crown Scales.” Remote Sensing of Environment 96 (3–4): 375–398. doi:10.1016/j.rse.2005.03.009.
   Web of Science ®Google Scholar
• DeAngelis, D. L., and V. Grimm. 2014. “Individual-Based Models in Ecology After Four Decades.” F1000Prime Reports 6 (June). doi:10.12703/P6-39.
   PubMedGoogle Scholar
• Dietze, M. C., D. S. Lebauer, and R. Kooper. 2013. “On Improving the Communication Between Models and Data.” Plant, Cell and Environment 36 (9): 1575–1585. doi:10.1111/pce.12043.
   PubMed Web of Science ®Google Scholar
• Dietze, M., S. Serbin, C. Davidson, A. Desai, X. Feng, R. Kelly, R. Kooper, et al. 2014. “A Quantitative Assessment of a Terrestrial Biosphere Model’s Data Needs Across North American Biomes.” Journal of Geophysical Research: Biogeosciences 119 (3): 286–300. doi:10.1002/2013JG002392.
   Web of Science ®Google Scholar
• Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026.
   Web of Science ®Google Scholar
• Fensholt, R., I. Sandholt, and M. S. Rasmussen. 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.2004.04.009.
   Web of Science ®Google Scholar
• Fischer, R., F. Bohn, M. Dantas de Paula, C. Dislich, J. Groeneveld, A. G. Gutiérrez, M. Kazmierczak, et al. 2016. “Lessons Learned From Applying a Forest Gap Model to Understand Ecosystem and Carbon Dynamics of Complex Tropical Forests.” Ecological Modelling 326: 124–133. doi:10.1016/j.ecolmodel.2015.11.018.
   Web of Science ®Google Scholar
• Forkel, M., N. Andela, S. P. Harrison, G. Lasslop, M. van Marle, E. Chuvieco, W. Dorigo, et al. 2018. “Emergent Relationships on Burned Area in Global Satellite Observations and Fire-Enabled Vegetation Models.” Biogeosciences Discussions 1–31. doi:10.5194/bg-2018-427.
   Web of Science ®Google Scholar
• Forkel, M., N. Carvalhais, S. Schaphoff, W. V. Bloh, M. Migliavacca, M. Thurner, and K. Thonicke. 2014. “Identifying Environmental Controls on Vegetation Greenness Phenology Through Model-Data Integration.” Biogeosciences (online) 11 (23): 7025–7050. doi:10.5194/bg-11-7025-2014.
   Web of Science ®Google Scholar
• Forkel, M., W. Dorigo, G. Lasslop, I. Teubner, E. Chuvieco, and K. Thonicke. 2017. “A Data-Driven Approach to Identify Controls on Global Fire Activity From Satellite and Climate Observations (SOFIA V1).” Geoscientific Model Development 10 (12): 4443–4476. doi:10.5194/gmd-10-4443-2017.
   Web of Science ®Google Scholar
• Forkel, M., M. Migliavacca, K. Thonicke, M. Reichstein, S. Schaphoff, U. Weber, and N. Carvalhais. 2015. “Codominant Water Control on Global Interannual Variability and Trends in Land Surface Phenology and Greenness.” Global Change Biology 21 (9): 3414–3435. doi:10.1111/gcb.12950.
   PubMed Web of Science ®Google Scholar
• GEO BON. 2015. “Global Biodiversity Change Indicators Version 1.2.” doi:10.978.39817938/19.
   Google Scholar
• Gonzalez, P., R. P. Neilson, J. M. Lenihan, and R. J. Drapek. 2010. “Global Patterns in the Vulnerability of Ecosystems to Vegetation Shifts Due to Climate Change.” Global Ecology and Biogeography 19 (6): 755–768. doi:10.1111/j.1466-8238.2010.00558.x.
   Web of Science ®Google Scholar
• Goodenough, D. G., A. S. Bhogal, A. Dyk, A. Hollinger, Z. Mah, K. O. Niemann, J. Pearlman, et al. 2002. “Monitoring Forests with Hyperion and ALI.” IEEE International Geoscience and Remote Sensing Symposium 2 (July 2001): 882–885. doi:10.1109/IGARSS.2002.1025717.
   Google Scholar
• Grimm, V., U. Berger, F. Bastiansen, S. Eliassen, V. Ginot, J. Giske, J. Goss-Custard, et al. 2006. “A Standard Protocol for Describing Individual-Based and Agent-Based Models.” Ecological Modelling 198 (1–2): 115–126. doi:10.1016/j.ecolmodel.2006.04.023.
   Web of Science ®Google Scholar
• Guimberteau, M., D. Zhu, F. Maignan, Y. Huang, C. Yue, S. Dantec-Nédélec, C. Ottl, et al. 2018. “ORCHIDEE-MICT (v8.4.1), a Land Surface Model for the High Latitudes: Model Description and Validation.” Geoscientific Model Development 11 (1): 121–163. doi:10.5194/gmd-11-121-2018.
   Web of Science ®Google Scholar
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–853. doi:10.1126/science.1244693.
   PubMed Web of Science ®Google Scholar
• Harfoot, M. B.J., T. Newbold, D. P. Tittensor, S. Emmott, J. Hutton, V. Lyutsarev, M. J. Smith, J. P.W. Scharlemann, and D. W. Purves. 2014. “Emergent Global Patterns of Ecosystem Structure and Function From a Mechanistic General Ecosystem Model.” PLoS Biology 12 (4). doi:10.1371/journal.pbio.1001841.
   PubMed Web of Science ®Google Scholar
• Harper, A. B., A. J. Wiltshire, P. M. Cox, P. Friedlingstein, C. D. Jones, L. M. Mercado, S. Sitch, K. Williams, and C. Duran-Rojas. 2018. “Vegetation Distribution and Terrestrial Carbon Cycle in a Carbon Cycle Configuration of JULES4.6 with New Plant Functional Types.” Geoscientific Model Development 11 (7): 2857–2873. doi:10.5194/gmd-11-2857-2018.
   Web of Science ®Google Scholar
• Hartley, A. J., N. MacBean, G. Georgievski, and S. Bontemps. 2017. “Uncertainty in Plant Functional Type Distributions and Its Impact on Land Surface Models.” Remote Sensing of Environment 203 (December): 71–89. doi:10.1016/J.RSE.2017.07.037.
   Google Scholar
• Hickler, T., K. Vohland, J. Feehan, P. A. Miller, B. Smith, L. Costa, T. Giesecke, et al. 2012. “Projecting the Future Distribution of European Potential Natural Vegetation Zones with a Generalized, Tree Species-Based Dynamic Vegetation Model.” Global Ecology and Biogeography 21 (1): 50–63. doi:10.1111/j.1466-8238.2010.00613.x.
   Web of Science ®Google Scholar
• Hill, M. J., G. P. Asner, and A. A. Held. 2006. “The Bio-Geophysical Approach to Remote Sensing of Vegetation in Coupled Human-Environment Systems–Societal Benefits and Global Context.” Journal of Spatial Science 51 (2): 49–66. doi:10.1080/14498596.2006.9635081.
   Google Scholar
• Ito, A., K. Nishina, C. P.O. Reyer, L. François, A J. Henrot, G. Munhoven, I. Jacquemin, et al. 2017. “Photosynthetic Productivity and Its Efficiencies in ISIMIP2a Biome Models: Benchmarking for Impact Assessment Studies.” Environmental Research Letters 12 (8). doi:10.1088/1748-9326/aa7a19.
   Web of Science ®Google Scholar
• Jin, X., L. Kumar, Z. Li, H. Feng, X. Xu, G. Yang, and J. Wang. 2018. “A Review of Data Assimilation of Remote Sensing and Crop Models.” European Journal of Agronomy 92 (October 2017): 141–152. doi:10.1016/j.eja.2017.11.002.
   Google Scholar
• Jönsson, A. M., F. Lagergren, and B. Smith. 2015. “Forest Management Facing Climate Change - an Ecosystem Model Analysis of Adaptation Strategies.” Mitigation and Adaptation Strategies for Global Change 20 (2): 201–220. doi:10.1007/s11027-013-9487-6.
   Web of Science ®Google Scholar
• Kaminski, T., W. Knorr, G. Schürmann, M. Scholze, P. J. Rayner, S. Zaehle, S. Blessing, et al. 2013. “The BETHY/JSBACH Carbon Cycle Data Assimilation System: Experiences and Challenges.” Journal of Geophysical Research: Biogeosciences 118 (4): 1414–1426. doi:10.1002/jgrg.20118.
   Web of Science ®Google Scholar
• Kato, T., W. Knorr, M. Scholze, E. Veenendaal, T. Kaminski, J. Kattge, and N. Gobron. 2013. “Simultaneous Assimilation of Satellite and Eddy Covariance Data for Improving Terrestrial Water and Carbon Simulations at a Semi-Arid Woodland Site in Botswana.” Biogeosciences (online) 10 (2): 789–802. doi:10.5194/bg-10-789-2013.
   Web of Science ®Google Scholar
• Keenan, T. F., Ian Baker, Alan Barr, Philippe Ciais, Ken Davis, Michael Dietze, Danillo Dragoni, et al. 2012. “Terrestrial Biosphere Model Performance for Inter-Annual Variability of Land-Atmosphere CO 2 Exchange.” Global Change Biology 18 (6): 1971–1987. doi:10.1111/j.1365-2486.2012.02678.x.
   Web of Science ®Google Scholar
• Keenan, T. F., M. S. Carbone, M. Reichstein, and A. D. Richardson. 2011. “The Model-Data Fusion Pitfall: Assuming Certainty in an Uncertain World.” Oecologia 167 (3): 587–597. doi:10.1007/s00442-011-2106-x.
   PubMed Web of Science ®Google Scholar
• Kelley, D. I., I. C. Prentice, S. P. Harrison, H. Wang, M. Simard, J. B. Fisher, and K. O. Willis. 2013. “A Comprehensive Benchmarking System for Evaluating Global Vegetation Models.” Biogeosciences (online) 10 (5): 3313–3340. doi:10.5194/bg-10-3313-2013.
   Web of Science ®Google Scholar
• Kissling, W. D., J. A. Ahumada, A. Bowser, M. Fernandez, N. Fernández, E. A. García, R. P. Guralnick, et al. 2018. “Building Essential Biodiversity Variables (EBVs) of Species Distribution and Abundance at a Global Scale.” Biological Reviews 93 (1): 600–625. doi:10.1111/brv.12359.
   PubMed Web of Science ®Google Scholar
• Kissling, W. D., R. Walls, A. Bowser, M.w O. Jones, J. Kattge, D. Agosti, J. Amengual, et al. 2018. “Towards Global Data Products of Essential Biodiversity Variables on Species Traits.” Nature Ecology and Evolution 2 (October). doi:10.1038/s41559-018-0667-3.
   PubMedGoogle Scholar
• Knorr, W., T. Kaminski, A. Arneth, and U. Weber. 2014. “Impact of Human Population Density on Fire Frequency at the Global Scale.” Biogeosciences (online) 11 (4): 1085–1102. doi:10.5194/bg-11-1085-2014.
   Web of Science ®Google Scholar
• Knorr, W., T. Kaminski, M. Scholze, N. Gobron, B. Pinty, R. Giering, and P. P. Mathieu. 2010. “Carbon Cycle Data Assimilation with a Generic Phenology Model.” Journal of Geophysical Research: Biogeosciences 115 (4): 1–16. doi:10.1029/2009JG001119.
   Google Scholar
• Lasslop, G., K. Thonicke, and S. Kloster. 2014. “Spit Fire Within the MPI Earth System Model: Model Development and Evaluation.” Journal of Advances in Modeling Earth Systems 740–755. doi:10.1002/2013MS000284.Received.
   Web of Science ®Google Scholar
• Lehsten, V., A. Arneth, A. Spessa, K. Thonicke, and A. Moustakas. 2016. “The Effect of Fire on Tree-Grass Coexistence in Savannas: A Simulation Study.” International Journal of Wildland Fire 25 (2): 137–146. doi:10.1071/WF14205.
   Web of Science ®Google Scholar
• Li, F., X. D. Zeng, and S. Levis. 2012. “A Process-Based Fire Parameterization of Intermediate Complexity in a Dynamic Global Vegetation Model.” Biogeosciences (online) 9 (7): 2761–2780. doi:10.5194/bg-9-2761-2012.
   Web of Science ®Google Scholar
• Lucht, W., I. C. Prentice, R. B. Myneni, S. Sitch, P. Friedlingstein, W. Cramer, P. Bousquet, W. Buermann, and B. Smith. 2002. “Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect.” Science 296 (5573): 1687–1689. doi:10.1126/science.1071828.
   PubMed Web of Science ®Google Scholar
• MacBean, N., F. Maignan, C. Bacour, P. Lewis, P. Peylin, L. Guanter, P. Köhler, J. Gómez-Dans, and M. Disney. 2018. “Correction: Strong Constraint on Modelled Global Carbon Uptake Using Solar-Induced Chlorophyll Fluorescence Data (Scientific Reports DOI: 10.1038/S41598-018-20024-W).” Scientific Reports 8 (1): 1–12. doi:10.1038/s41598-018-28697-z.
   PubMedGoogle Scholar
• MacBean, N., F. Maignan, P. Peylin, C. Bacour, F. M. Bréon, and P. Ciais. 2015. “Using Satellite Data to Improve the Leaf Phenology of a Global Terrestrial Biosphere Model.” Biogeosciences Discussions 12 (16): 13311–13373. doi:10.5194/bgd-12-13311-2015.
   Google Scholar
• Maignan, F., F.-M. Bréon, F. Chevallier, N. Viovy, P. Ciais, C. Garrec, J. Trules, and M. Mancip. 2011. “Evaluation of a Global Vegetation Model Using Time Series of Satellite Vegetation Indices.” Geoscientific Model Development 4 (4): 1103–1114. doi:10.5194/gmd-4-1103-2011.
   Web of Science ®Google Scholar
• Mangeon, S., A. Voulgarakis, R. Gilham, A. Harper, S. Sitch, and G. Folberth. 2016. “INFERNO: A Fire and Emissions Scheme for the UK Met Office’s Unified Model.” Geoscientific Model Development 9 (8): 2685–2700. doi:10.5194/gmd-9-2685-2016.
   Web of Science ®Google Scholar
• Marchetti, P. G., P. Soille, and L. Bruzzone. 2016. “A Special Issue on Big Data From Space for Geoscience and Remote Sensing [From the Guest Editors].” Ieee Geoscience and Remote Sensing Magazine 4 (3, SI): 7–9. doi:10.1109/MGRS.2016.2586852.
   Web of Science ®Google Scholar
• Medlyn, B. E., S. Zaehle, M. G. De Kauwe, A. P. Walker, M. C. Dietze, P. J. Hanson, T. Hickler, et al. 2015. “Using Ecosystem Experiments to Improve Vegetation Models.” Nature Climate Change 5 (6): 528–534. doi:10.1038/nclimate2621.
   Web of Science ®Google Scholar
• Medvigy, D., S. C. Wofsy, J. W. Munger, D. Y. Hollinger, and P. R. Moorcroft. 2009. “Mechanistic Scaling of Ecosystem Function and Dynamics in Space and Time: Ecosystem Demography Model Version 2.” Journal of Geophysical Research: Biogeosciences 114 (1): 1–21. doi:10.1029/2008JG000812.
   Google Scholar
• Melton, J. R., and V. K. Arora. 2016. “Competition Between Plant Functional Types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0.” Geoscientific Model Development 9 (1): 323–361. doi:10.5194/gmd-9-323-2016.
   Web of Science ®Google Scholar
• Myneni, R. B. 1997. “Estimation of Global Leaf Area Index and Absorbed Par Using Radiative Transfer Models.” IEEE Transactions on Geoscience and Remote Sensing 35 (6): 1380–1393. doi:10.1109/36.649788.
   Web of Science ®Google Scholar
• Newbold, T., L. N Hudson, S. L.L. Hill, S. Contu, I. Lysenko, R. A Senior, L. Börger, et al. 2015. “Global Effects of Land Use on Local Terrestrial Biodiversity.” Nature 520 (7545): 45–50. doi:10.1038/nature14324.
   PubMed Web of Science ®Google Scholar
• Norton, A. J., P. J. Rayner, E. N. Koffi, and M. Scholze. 2018. “Assimilating Solar-Induced Chlorophyll Fluorescence Into the Terrestrial Biosphere Model BETHY-SCOPE v1.0: Model Description and Information Content.” Geoscientific Model Development 11 (4): 1517–1536. doi:10.5194/gmd-11-1517-2018.
   Web of Science ®Google Scholar
• Pachzelt, A., M. Forrest, A. Rammig, S. I. Higgins, and T. Hickler. 2015. “Potential Impact of Large Ungulate Grazers on African Vegetation, Carbon Storage and Fire Regimes.” Global Ecology and Biogeography 24 (9): 991–1002. doi:10.1111/geb.12313.
   Web of Science ®Google Scholar
• Parazoo, N. C., K. Bowman, J. B. Fisher, C. Frankenberg, D. B.A. Jones, A. Cescatti, O. Pérez-Priego, G. Wohlfahrt, and L. Montagnani. 2014. “Terrestrial Gross Primary Production Inferred From Satellite Fluorescence and Vegetation Models.” Global Change Biology 20 (10): 3103–3121. doi:10.1111/gcb.12652.
   PubMed Web of Science ®Google Scholar
• Pavlick, R., D. T. Drewry, K. Bohn, B. Reu, and A. Kleidon. 2013. “The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): A Diverse Approach to Representing Terrestrial Biogeography and Biogeochemistry Based on Plant Functional Trade-Offs.” Biogeosciences (online) 10 (6): 4137–4177. doi:10.5194/bg-10-4137-2013.
   Web of Science ®Google Scholar
• Pereira, H. M., S. Ferrier, M. Walters, G. N. Geller, R. H. G. Jongman, R. J. Scholes, M. W. Bruford, et al. 2013. “Essential Biodiversity Variables.” Science, doi:10.1126/science.1229931.
   Web of Science ®Google Scholar
• Peterson, A. T., M. Papeş, and J. Soberón. 2015. “Mechanistic and Correlative Models of Ecological Niches.” European Journal of Ecology 1 (2). doi:10.1515/eje-2015-0014.
   Google Scholar
• Pettorelli, N. 2015. “Agree on Biodiversity Metrics to Track From Space.” Nature 523: 5–7. doi:10.1038/523403a.
   Web of Science ®Google Scholar
• Pettorelli, N., M. Wegmann, A. Skidmore, S. Mücher, T. P. Dawson, M. Fernandez, R. Lucas, et al. 2016. “Framing the Concept of Satellite Remote Sensing Essential Biodiversity Variables: Challenges and Future Directions.” Remote Sensing in Ecology and Conservation 2 (3): 122–131. doi:10.1002/rse2.15.
   Web of Science ®Google Scholar
• Plummer, S. E. 2000. “Perspectives on Combining Ecological Process Models and Remotely Sensed Data.” Ecological Modelling 129 (2–3): 169–186. doi:10.1016/S0304-3800(00)00233-7.
   Web of Science ®Google Scholar
• Poulter, B., P. Cadule, A. Cheiney, P. Ciais, E. Hodson, P. Peylin, S. Plummer, et al. 2015. “Sensitivity of Global Terrestrial Carbon Cycle Dynamics to Variability in Satellite-Observed Burned Area.” Global Biogeochemical Cycles 29: 207–222. doi:10.1002/2013GB004655.Received.
   Web of Science ®Google Scholar
• Prentice, I. C., A. Bondeau, W. Cramer, S. P. Harrison, T. Hickler, W. Lucht, S. Sitch, B. Smith, and M. T. Sykes. 2004. “Dynamic Global Vegetation Modeling: Quantifying Terrestrial Ecosystem Responses to Large-Scale Environmental Change.” Terrestrial Ecosystems in a Changing World 175–192. doi:10.1007/978-3-540-32730-1_15.
   Google Scholar
• Quillet, A., C. Peng, and M. Garneau. 2009. “Toward Dynamic Global Vegetation Models for Simulating Vegetation – Climate Interactions and Feedbacks : Recent Developments, Limitations, and Future Challenges.” Environmental Reviews 18 (1): 333–353. doi:10.1139/A10-016.
   Google Scholar
• Rabin, S. S., D. S. Ward, S. L. Malyshev, B. I. Magi, E. Shevliakova, and S. W. Pacala. 2018. “A Fire Model with Distinct Crop, Pasture, and Non-Agricultural Burning: Use of New Data and a Model-Fitting Algorithm for FINAL.1.” Geoscientific Model Development 11 (2): 815–842. doi:10.5194/gmd-11-815-2018.
   Web of Science ®Google Scholar
• Rafique, R., F. Zhao, R. De Jong, N. Zeng, and G. R. Asrar. 2016. “Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison.” Remote Sensing 8 (3): 1–16. doi:10.3390/rs8030177.
   Web of Science ®Google Scholar
• Randerson, J. T., F. M. Hoffman, P. E. Thornton, N. M. Mahowald, K. Lindsay, Y. H. Lee, C. D. Nevison, et al. 2009. “Systematic Assessment of Terrestrial Biogeochemistry in Coupled Climate-Carbon Models.” Global Change Biology 15 (10): 2462–2484. doi:10.1111/j.1365-2486.2009.01912.x.
   Web of Science ®Google Scholar
• Saatchi, S., W. Buermann, H. ter Steege, S. Mori, and T. B. Smith. 2008. “Modeling Distribution of Amazonian Tree Species and Diversity Using Remote Sensing Measurements.” Remote Sensing of Environment 112 (5): 2000–2017. doi:10.1016/j.rse.2008.01.008.
   Web of Science ®Google Scholar
• Sakschewski, B., W. von Bloh, A. Boit, L. Poorter, M. Peña-Claros, J. Heinke, J. Joshi, et al. 2014. “Resilience of Amazon Forests Emerges From Plant Trait Diversity.” Edited by Articulación Regional Amazónica (ARA). Nat Clim Change 198: 63–73. doi:10.1038/nclimate3109.
   Google Scholar
• Sato, H., A. Itoh, and T. Kohyama. 2007. “SEIB-DGVM: A New Dynamic Global Vegetation Model Using a Spatially Explicit Individual-Based Approach.” Ecological Modelling 200 (3–4): 279–307. doi:10.1016/j.ecolmodel.2006.09.006.
   Web of Science ®Google Scholar
• Schaphoff, S., M. Forkel, C. Müller, J. Knauer, W. Von Bloh, D. Gerten, J. Jägermeyr, et al. 2018. “LPJmL4 - A Dynamic Global Vegetation Model with Managed Land - Part 2: Model Evaluation.” Geoscientific Model Development 11 (4): 1377–1403. doi:10.5194/gmd-11-1377-2018.
   Web of Science ®Google Scholar
• Scheiter, S., and S. I. Higgins. 2009. “Impacts of Climate Change on the Vegetation of Africa: An Adaptive Dynamic Vegetation Modelling Approach.” Global Change Biology 15 (9): 2224–2246. doi:10.1111/j.1365-2486.2008.01838.x.
   Web of Science ®Google Scholar
• Scheiter, S., L. Langan, and S. I. Higgins. 2013. “Next-Generation Dynamic Global Vegetation Models: Learning From Community Ecology.” New Phytologist 198 (3): 957–969. doi:10.1111/nph.12210.
   PubMed Web of Science ®Google Scholar
• Scholze, M., M. Buchwitz, W. Dorigo, L. Guanter, and S. Quegan. 2017. “Reviews and Syntheses: Systematic Earth Observations for Use in Terrestrial Carbon Cycle Data Assimilation Systems.” Biogeosciences (online) 14 (14): 3401–3429. doi:10.5194/bg-14-3401-2017.
   Web of Science ®Google Scholar
• Schroder, B. E., and W. Lucht. 2003. “Improved Estimates of the Terrestrial Carbon Cycle by Coupling of a Process-Based Global Vegetation Model (LPJ-DGVM) with a 17-Year Time Series of Satellite-Observed FPAR Data (AVHRR).” IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) 2 (C): 7929–7931. doi:10.1109/IGARSS.2003.1293982.
   Google Scholar
• Schürmann, G. J., T. Kaminski, C. Köstler, N. Carvalhais, M. Voßbeck, J. Kattge, R. Giering, C. Rödenbeck, M. Heimann, and S. Zaehle. 2016. “Constraining a Land-Surface Model with Multiple Observations by Application of the MPI-Carbon Cycle Data Assimilation System V1.0.” Geoscientific Model Development 9 (9): 2999–3026. doi:10.5194/gmd-9-2999-2016.
   Web of Science ®Google Scholar
• Seiler, C., R. W. A. Hutjes, B. Kruijt, and T. Hickler. 2015. “The Sensitivity of Wet and Dry Tropical Forests to Climate Change in Bolivia.” Journal of Geophysical Research: Biogeosciences (120): 399–413. doi:10.1002/2014JG002749.Received.
   Google Scholar
• Sellers, P. J., B. W. Meeson, F. G. Hall, G. Asrar, R. E. Murphy, R. A. Schiffer, F. P. Bretherton, et al. 1995. “Remote Sensing of the Land Surface for Studies of Global Change: Models — Algorithms — Experiments.” Remote Sensing of Environment 51 (1): 3–26. doi:10.1016/0034-4257(94)00061-Q.
   Web of Science ®Google Scholar
• Sexton, J. O., X. P. Song, M. Feng, P. Noojipady, A. Anand, C. Huang, D. H. Kim, et al. 2013. “Global, 30-m Resolution Continuous Fields of Tree Cover: Landsat-Based Rescaling of MODIS Vegetation Continuous Fields with Lidar-Based Estimates of Error.” International Journal of Digital Earth 6 (5): 427–448. doi:10.1080/17538947.2013.786146.
   Web of Science ®Google Scholar
• Sitch, S., A. David Mcguire, J. Kimball, N. Gedney, J. Gamon, A. Wolf, Q. Zhuang, J. Clein, and K. C Mcdonald. 2007. “Assessing the Carbon Balance of Circumpolar Arctic Tundra Using Remote Sensing and Process Modeling.” Ecological Applications 17 (1): 213–234.
   PubMed Web of Science ®Google Scholar
• Sitch, S., C. Huntingford, N. Gedney, P. E. Levy, M. Lomas, S. L. Piao, R. Betts, et al. 2008. “Evaluation of the Terrestrial Carbon Cycle, Future Plant Geography and Climate-Carbon Cycle Feedbacks Using Five Dynamic Global Vegetation Models (DGVMs).” Global Change Biology 14 (9): 2015–2039. doi:10.1111/j.1365-2486.2008.01626.x.
   Web of Science ®Google Scholar
• Smith, B., W. Knorr, J. L. Widlowski, B. Pinty, and N. Gobron. 2008. “Combining Remote Sensing Data with Process Modelling to Monitor Boreal Conifer Forest Carbon Balances.” Forest Ecology and Management 255 (12): 3985–3994. doi:10.1016/j.foreco.2008.03.056.
   Web of Science ®Google Scholar
• Smith, B., D. Wärlind, A. Arneth, T. Hickler, P. Leadley, J. Siltberg, and S. Zaehle. 2014. “Implications of Incorporating N Cycling and N Limitations on Primary Production in an Individual-Based Dynamic Vegetation Model.” Biogeosciences (online) 11 (7): 2027–2054. doi:10.5194/bg-11-2027-2014.
   Web of Science ®Google Scholar
• Thum, T., S. Zaehle, P. Köhler, T. Aalto, M. Aurela, L. Guanter, P. Kolari, et al. 2017. “Modelling Sun-Induced Fluorescence and Photosynthesis with a Land Surface Model at Local and Regional Scales in Northern Europe.” Biogeosciences (online) 14 (7): 1969–1984. doi:10.5194/bg-14-1969-2017.
   Web of Science ®Google Scholar
• Thurner, M., C. Beer, P. Ciais, A. D. Friend, A. Ito, A. Kleidon, M. R. Lomas, et al. 2017. “Evaluation of Climate-Related Carbon Turnover Processes in Global Vegetation Models for Boreal and Temperate Forests.” Global Change Biology 23 (8): 3076–3091. doi:10.1111/gcb.13660.
   PubMed Web of Science ®Google Scholar
• Thurner, M., C. Beer, M. Santoro, N. Carvalhais, T. Wutzler, D. Schepaschenko, A. Shvidenko, et al. 2014. “Carbon Stock and Density of Northern Boreal and Temperate Forests.” Global Ecology and Biogeography 23 (3): 297–310. doi:10.1111/geb.12125.
   Web of Science ®Google Scholar
• Torres, R., P. Snoeij, D. Geudtner, D. Bibby, M. Davidson, E. Attema, P. Potin, et al. 2012. “GMES Sentinel-1 Mission.” Remote Sensing of Environment 120: 9–24. doi:10.1016/j.rse.2011.05.028.
   Web of Science ®Google Scholar
• Wang, Y. P., R. M. Law, and B. Pak. 2010. “A Global Model of Carbon, Nitrogen and Phosphorus Cycles for the Terrestrial Biosphere.” Biogeosciences (online) 7 (7): 2261–2282. doi:10.5194/bg-7-2261-2010.
   Web of Science ®Google Scholar
• Wärlind, D., B. Smith, T. Hickler, and A. Arneth. 2014. “Nitrogen Feedbacks Increase Future Terrestrial Ecosystem Carbon Uptake in an Individual-Based Dynamic Vegetation Model.” Biogeosciences (online) 11 (21): 6131–6146. doi:10.5194/bg-11-6131-2014.
   Web of Science ®Google Scholar
• Weigelt, A., W. W. Weisser, N. Buchmann, and M. Scherer-Lorenzen. 2009. “Biodiversity for Multifunctional Grasslands: Equal Productivity in High-Diversity Low-Input and Low-Diversity High-Input Systems.” Biogeosciences (online) 6 (8): 1695–1706. doi:10.5194/bg-6-1695-2009.
   Web of Science ®Google Scholar
• Widdicombe, S., and J. I. Spicer. 2008. “Predicting the Impact of Ocean Acidification on Benthic Biodiversity: What Can Animal Physiology Tell Us?” Journal of Experimental Marine Biology and Ecology 366 (1–2): 187–197. doi:10.1016/j.jembe.2008.07.024.
   Web of Science ®Google Scholar
• Williams, M., A. D. Richardson, M. Reichstein, P. C. Stoy, P. Peylin, H. Verbeeck, N. Carvalhais, and M. Jung. 2009. “Improving Land Surface Models with FLUXNET Data.” Biogeosciences (online) 6: 1341–1359. doi:10.5194/bg-6-1341-2009.
   Web of Science ®Google Scholar
• Yang, C. E., J. Mao, F. M. Hoffman, D. M. Ricciuto, J. S. Fu, C. D. Jones, and M. Thurner. 2018. “Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models Over the Northern Hemisphere.” Scientific Reports 8 (1): 1–14. doi:10.1038/s41598-018-29227-7.
   PubMed Web of Science ®Google Scholar
• Yue, C., P. Ciais, P. Cadule, K. Thonicke, and T. T. Van Leeuwen. 2015. “Modelling the Role of Fires in the Terrestrial Carbon Balance by Incorporating SPITFIRE Into the Global Vegetation Model ORCHIDEE -Part 2: Carbon Emissions and the Role of Fires in the Global Carbon Balance.” Geoscientific Model Development 8 (5): 1321–1338. doi:10.5194/gmd-8-1321-2015.
   Web of Science ®Google Scholar
• Zhu, D., S. S. Peng, P. Ciais, N. Viovy, A. Druel, M. Kageyama, G. Krinner, et al. 2015. “Improving the Dynamics of Northern Hemisphere High-Latitude Vegetation in the ORCHIDEE Ecosystem Model.” Geoscientific Model Development 8 (7): 2263–2283. doi:10.5194/gmd-8-2263-2015.
   Web of Science ®Google Scholar