Editorial Type:
Article
Article Type:
Research Article

Robust Identification of Global Greening Phase Patterns from Remote Sensing Vegetation Products

Carola Dahlke Max Planck Institute for Meteorology, Hamburg, Germany

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Alexander Loew Max Planck Institute for Meteorology, Hamburg, Germany

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Christian Reick Max Planck Institute for Meteorology, Hamburg, Germany

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Print Publication:
01 Dec 2012
DOI:
https://doi.org/10.1175/JCLI-D-11-00319.1
Page(s):
8289–8307
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Abstract

The fraction of absorbed photosynthetically active radiation (fAPAR) is an essential diagnostic variable to investigate the temporal and spatial dynamics of the terrestrial biosphere. The present study provides a new method to assess global vegetation greening phase dynamics, derived from fAPAR time series from four different remote sensing products. A robust algorithm is developed to detect intra-annual greening phase patterns and derive seasonality patterns of vegetation dynamics at the global scale. The comparison of four independent remote sensing datasets shows significantly consistent global spatiotemporal patterns at the 95% confidence level. Regions where the remote sensing datasets show consistent results, as well as regions where at least one of the used remote sensing datasets deviates, can be identified. The derived global greening phase dataset and analysis method provides a solid framework for the evaluation of global vegetation models.

Corresponding author address: Alexander Loew, Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany. E-mail: alexander.loew@zmaw.de

Abstract

The fraction of absorbed photosynthetically active radiation (fAPAR) is an essential diagnostic variable to investigate the temporal and spatial dynamics of the terrestrial biosphere. The present study provides a new method to assess global vegetation greening phase dynamics, derived from fAPAR time series from four different remote sensing products. A robust algorithm is developed to detect intra-annual greening phase patterns and derive seasonality patterns of vegetation dynamics at the global scale. The comparison of four independent remote sensing datasets shows significantly consistent global spatiotemporal patterns at the 95% confidence level. Regions where the remote sensing datasets show consistent results, as well as regions where at least one of the used remote sensing datasets deviates, can be identified. The derived global greening phase dataset and analysis method provides a solid framework for the evaluation of global vegetation models.

Corresponding author address: Alexander Loew, Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany. E-mail: alexander.loew@zmaw.de
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  • Alessandri, A., and A. Navarra, 2008: On the coupling between vegetation and rainfall inter-annual anomalies: Possible contributions to seasonal rainfall predictability over land areas. Geophys. Res. Lett., 35, L02718, doi:10.1029/2007GL032415.

    • Search Google Scholar
    • Export Citation

  • Baret, F., and Coauthors, 2007: LAI, fAPAR, and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ., 110, 275–286.

    • Search Google Scholar
    • Export Citation

  • Beck, P., C. Atzberger, K. Hgda, B. Johansen, and A. Skidmore, 2006: Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens. Environ., 100, 321–334.

    • Search Google Scholar
    • Export Citation

  • Botta, A., N. Viovy, P. Ciais, P. Friedlingsstein, and P. Monfrey, 2000: A global prognostic scheme of leaf onset using satellite data. Global Change Biol., 6, 709–725.

    • Search Google Scholar
    • Export Citation

  • Bradley, B., R. Jacob, J. Hermance, and J. Mustard, 2007: A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens. Environ., 106, 137–145.

    • Search Google Scholar
    • Export Citation

  • Chen, X., B. Hu, and R. Yu, 2005: Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Global Change Biol., 11, 1118–1130.

    • Search Google Scholar
    • Export Citation

  • D’Arrigo, R., G. Jacoby, and I. Fung, 1987: Boreal forest and atmosphere–biosphere exchange of carbon dioxide. Nature, 329, 321–323.

    • Search Google Scholar
    • Export Citation

  • Delbart, N., L. Kergoat, T. Le Toan, J. Lhermitte, and G. Picar, 2005: Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens. Environ., 97, 26–38.

    • Search Google Scholar
    • Export Citation

  • Delbart, N., T. Le Toan, L. Kergoat, and V. Fedotova, 2006: Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004). Remote Sens. Environ., 101, 52–62.

    • Search Google Scholar
    • Export Citation

  • de Wit, A., and B. Su, 2005: Deriving phenological indicators from SPOT-VGT data using the HANTS algorithm. Proc. Second Int. SPOT-VEGETATION Users Conf., Antwerp, Belgium, VITO, 195–201.

  • Friedl, M., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182.

    • Search Google Scholar
    • Export Citation

  • Garrigues, S., and Coauthors, 2008: Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J. Geophys. Res., 113, G02028, doi:10.1029/2007JG000635.

    • Search Google Scholar
    • Export Citation

  • Gobron, N., B. Pinty, F. Melin, M. Taberner, and M. Verstraete, 2002: Sea Wide Field-of-View Sensor (SeaWiFS): An optimized FAPAR algorithm. Tech. Rep. I21020, Institute for Environment and Sustainability Joint Research Centre Theoretical Basis Doc., 40 pp.

  • Gobron, N., B. Pinty, M. Taberner, F. Mélin, M. Verstraete, and J. Widlowski, 2006a: Monitoring the photosynthetic activity of vegetation from remote sensing data. Adv. Space Res., 38, 2196–2202.

    • Search Google Scholar
    • Export Citation

  • Gobron, N., and Coauthors, 2006b: Evaluation of fraction of absorbed photosynthetically active radiation products for different canopy radiation transfer regimes: Methodology and results using Joint Research Center products derived from SeaWiFS against ground-based estimations. J. Geophys. Res., 111, D13110, doi:10.1029/2005JD006511.

    • Search Google Scholar
    • Export Citation

  • Gobron, N., and Coauthors, 2008: Uncertainty estimates for the FAPAR operational products derived from MERIS: Impact of top-of-atmosphere radiance uncertainties and validation with field data. Remote Sens. Environ., 112, 1871–1883.

    • Search Google Scholar
    • Export Citation

  • Gobron, N., A. Belward, B. Pinty, and W. Knorr, 2010: Monitoring biosphere vegetation 1998–2009. Geophys. Res. Lett., 37, L15402, doi:10.1029/2010GL043870.

    • Search Google Scholar
    • Export Citation

  • Heumann, B., J. Seaquist, L. Eklundh, and P. Joensson, 2007: AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens. Environ., 108, 385–392.

    • Search Google Scholar
    • Export Citation

  • Justice, C., J. Townshend, B. Holben, and C. Tucker, 1985: Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens., 6, 1271–1318.

    • Search Google Scholar
    • Export Citation

  • Kaduk, J., and M. Heimann, 1997: A prognostic phenology scheme for global terrestrial carbon cycle models. Climate Res., 6, 1–19.

    • Search Google Scholar
    • Export Citation

  • Kang, S., S. Running, J.-H. Lim, M. Zhao, C. Park, and R. Loehman, 2003: A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: An application of MODIS leaf area index. Remote Sens. Environ., 86, 232–242.

    • Search Google Scholar
    • Export Citation

  • Keeling, C., J. Chin, and T. Whorf, 1996: Increased activity of northern vegetation inferred from atmospheric CO2 measurements. Nature, 382, 146–149.

    • Search Google Scholar
    • Export Citation

  • 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. J. Geophys. Res., 115, G04017, doi:10.1029/2009JG001119.

    • Search Google Scholar
    • Export Citation

  • Knyazikhin, Y., J. Martonchik, D. Diner, R. Myneni, M. Verstraete, B. Pinty, and N. Gobron, 1998: Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data. J. Geophys. Res., 103 (D24), 32 239–32 256.

    • Search Google Scholar
    • Export Citation

  • Knyazikhin, Y., and Coauthors, 1999: MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15). Institute for Environment and Sustainability Joint Research Centre Algorithm Theoretical Basis Doc., 126 pp. [Available online at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod15.pdf.]

  • Linderholm, H., 2006: Growing season changes in the last century. Agric. For. Meteor., 137, 1–14.

  • Liu, Z., M. Notaro, J. Kutzbach, and N. Liu, 2006: Assessing global vegetation–climate feedbacks from observations. J. Climate, 19, 787–814.

    • Search Google Scholar
    • Export Citation

  • Lüdeke, M., P. Ramge, and G. H. Kohlmaier, 1996: The use of satellite NDVI data for the validation of global vegetation phenology models: Application to the Frankfurt Biosphere Model. Ecol. Modell., 91, 255–270.

    • Search Google Scholar
    • Export Citation

  • Mahecha, M., L. Fürst, N. Gobron, and H. Lange, 2010: Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity. Pattern Recognit. Lett., 31, 2309–2317.

    • Search Google Scholar
    • Export Citation

  • Markon, C., M. Fleming, and E. Binnian, 1995: Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data. Polar Rec., 31, 179–190.

    • Search Google Scholar
    • Export Citation

  • Massey, F., 1951: The Kolmogorov–Smirnov test for goodness of fit. J. Amer. Stat. Assoc., 46, 68–78.

  • McCallum, I., W. Wagner, C. Schmullius, A. Shvidenko, M. Obersteiner, S. Fritz, and S. Nilsson, 2010: Comparison of four global FAPAR datasets over northern Eurasia for the year 2000. Remote Sens. Environ., 114, 941–949.

    • Search Google Scholar
    • Export Citation

  • McCloy, K., and W. Lucht, 2001: Comparison of the fraction absorbed photosynthetically active radiation (FPAR) data derived from AVHRR NDVI data with that estimated by the LPJ global dynamic vegetation model, for the globe over the period July 1981–June 1998. Proc. Int. Geoscience Remote Sensing Symp. ‘01, Vol. 3, Sydney, Australia, IEEE, 1335–1337.

  • Menzel, A., 2002: Phenology, its importance to the global change community. Climate Change, 54, 379–385.

  • Moulin, S., L. Kergoat, N. Viovy, and G. Dedieu, 1997: Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Climate, 10, 1154–1170.

    • Search Google Scholar
    • Export Citation

  • Myneni, R., C. Keeling, C. Tucker, G. Asrar, and R. Nemani, 1997a: Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702.

    • Search Google Scholar
    • Export Citation

  • Myneni, R., R. Nemani, and S. Running, 1997b: Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Remote Sens., 35, 1380–1393.

    • Search Google Scholar
    • Export Citation

  • Nekovar, J., E. Koch, E. Kubin, P. Nejedlik, T. Sparks, and F. E. Wielgolaski, 2008: The History and Current Status of Plant Phenology in Europe. COST, 182 pp.

  • Penuelas, J., and I. Filella, 2001: Responses to a warming world. Science, 294, 793–795.

  • Pinty, B., N. Gobron, F. Melin, and M. Verstraete, 2002: A time composite algorithm theoretical basis document. Institute for Environment and Sustainability, European Commission, Tech. Rep. EUR 20150 EN, 8 pp.

  • Potter, C., P.-N. Tan, M. Steinbach, S. Klooster, V. Kumar, and R. Myneni, 2003: Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biol., 9, 1005–1021.

    • Search Google Scholar
    • Export Citation

  • Randerson, J., and Coauthors, 2009: Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models. Global Change Biol., 15, 2462–2484.

    • Search Google Scholar
    • Export Citation

  • Rayner, P., M. Scholze, W. Knorr, T. Kaminski, R. Giering, and H. Widmann, 2005: Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS). Global Biogeochem. Cycles, 19, GB2026, doi:10.1029/2004GB002254.

    • Search Google Scholar
    • Export Citation

  • Reed, B., J. Brown, D. VanderZee, T. Loveland, J. Merchant, and D. Ohlen, 1994: Measuring phenological variability from satellite imagery. J. Veg. Sci., 5, 703–714.

    • Search Google Scholar
    • Export Citation

  • Schwartz, M., 1994: Monitoring global change with phenology: The case of the spring green wave. Int. J. Biometeor., 38, 18–22.

  • Schwartz, M., 1998: Green-wave phenology. Nature, 394, 839–840.

  • Schwartz, M., B. Reed, and M. White, 2002: Assessing satellite-derived start-of-season measures in the conterminous USA. Int. J. Climatol., 22, 1793–1805.

    • Search Google Scholar
    • Export Citation

  • Shabanov, N., A. Samanta, R. Myneni, Y. Knyazikhin, P. Votava, and R. Nemani, 2007: Collection 5 MODIS LAI and FPAR products. Proc. MODIS Land Collection 5/LTDR Workshop, Adelphi, MD, NASA. [Available online at http://modis.gsfc.nasa.gov/sci_team/meetings/c5meeting/pres/day1/shabanov.pdf.]

  • Stöckli, R., and P. Vidale, 2004: European plant phenology and climate as seen in a 20- year AVHRR land-surface parameter dataset. Int. J. Remote Sens., 25, 3303–3330.

    • Search Google Scholar
    • Export Citation

  • Stöckli, R., T. Rutishauser, I. Baker, M. Liniger, and A. Denning, 2011: A global reanalysis of vegetation phenology. J. Geophys. Res., 116, G03020, doi:10.1029/2010JG001545.

    • Search Google Scholar
    • Export Citation

  • Studer, S., R. Stöckli, C. Appenzeller, and P. Vidale, 2007: A comparative study of satellite and ground-based phenology. Int. J. Biometeor., 51, 405–414.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Coauthors, 2004: Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. J. Geophys. Res., 109, D01103, doi:10.1029/2003JD003777.

    • Search Google Scholar
    • Export Citation

  • Turner, B., E. Lambin, and A. Reenberg, 2007: The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci. USA, 104, 20 666–20 671; Corrigendum, 105, 2751.

    • Search Google Scholar
    • Export Citation

  • Verstraete, M., N. Gobron, O. Aussedat, M. Robustelli, B. Pinty, J.-L. Widlowski, and M. Taberner, 2008: An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products. Adv. Space Res., 41, 1773–1783.

    • Search Google Scholar
    • Export Citation

  • Wang, Q., J. Tenhunen, N. Dinh, M. Reichstein, T. Vesala, and P. Keronen, 2004: Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland. Remote Sens. Environ., 93, 225–237.

    • Search Google Scholar
    • Export Citation

  • Weiss, M., F. Baret, S. Garrigues, and R. Lacaze, 2007: LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products. Remote Sens. Environ., 110, 317–331.

    • Search Google Scholar
    • Export Citation

  • White, M., P. Running, and P. Thornton, 1999: The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern U.S. deciduous forest. Int. J. Biometeor., 42, 139–145.

    • Search Google Scholar
    • Export Citation

  • White, M., R. Nemani, P. Thornton, and S. Running, 2002: Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems, 5, 260–277.

    • Search Google Scholar
    • Export Citation

  • White, M., and Coauthors, 2009: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biol., 15, 2335–2359.

    • Search Google Scholar
    • Export Citation

  • Whittaker, R. H., 1975: Communities and Ecosystems. 2nd ed. McMillan, 385 pp.

  • Yang, W., H. Dong, T. Bin, J. Stroeve, N. Shabanov, Y. Knyazikhin, R. Nemani, and R. Myneni, 2006: Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the Terra MODIS sensor: 2000–2005. IEEE Trans. Geosci. Remote Sens., 44, 1829–1842.

    • Search Google Scholar
    • Export Citation

  • Zhang, X., M. Friedl, C. Schaaf, A. Strahler, J. Hodges, F. Gao, B. Reed, and A. Huete, 2003: Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 84, 471–475.

    • Search Google Scholar
    • Export Citation

  • Zhou, L., C. Tucker, R. Kaufmann, D. Slayback, N. Shabanov, and R. Myneni, 2001: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res., 106 (D17), 20 069–20 083.

    • Search Google Scholar
    • Export Citation

  • Zhou, L., R. Kaufmann, Y. Tian, R. Myneni, and C. Tucker, 2003: Relation between interannual variations in satellite measures northern forest greenness and climate between 1982 and 1999. J. Geophys. Res., 108, 4004, doi:10.1029/2002JD002510.

    • Search Google Scholar
    • Export Citation
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