New

    1. Poulter et al., 2014. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 2014 (doi:10.1038/nature13376)
    2. Zhou et al., 2014. Widespread decline of Congo rainforest greenness in the past decade, Nature, 2014 (doi: 10.1038/nature13265)
    3. Wang et al., 2014. A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 2014 (doi: 10.1038/nature12915)
    4. Peng et al., 2014. Afforestation in China cools local land surface temperature, PNAS (www.pnas.org/cgi/doi/10.1073/pnas.1315126111)
    5. Traore et al., 2014. Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements, J. Geophys. Res. Biogeosci., 119, 1554–1575, doi:10.1002/2014JG002638.
    6. Yan et al., 2014. Development of a remotely sensing seasonal vegetation-based Palmer Drought Severity Index and its application of global drought monitoring over 1982-2011, J. Geophys. Res. Atmos.,
      119, 9419–9440, doi:10.1002/2014JD021673
    7. Tan et al., 2014. Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere, Global Change Biology, (doi:
      10.1111/gcb.12724)
    8. Traore et al., 2014. 1982-2010 trends of light use efficiency and inherent water use efficiency in African vegetation: Sensitivity to climate and atmospheric CO2 concentrations, Remote Sensing, 6, 8923-8944; doi:10.3390/rs6098923
    9. Zhao et al., 2014. Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau, Landscape Ecol., 6, doi:10.1007/s10980-014-0095-y
    10. Park et al., 2014. Application of physically-based slope correction for maximum forest canopy height estimation using waveform lidar across different footprint sizes and locations: Tests on LVIS and GLAS, Remote Sensing, 6: 6566-6586 (doi:10.3390/rs6076566).
    11. Ciais et al., 2014. Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system, Biogeosciences, 11: 3547-3602 (doi:10.5194/bg-11-3547-2014).
    12. Van Oijen et al., 2014. Impact of droughts on the C-cycle in European Vegetation: a probabilistic risk analysis using six vegetation models, Biogeosciences Discussion, doi:10.5194/bgd-11-8325-2014.
    13. Weiss et al., 2014. On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products, Remote Sensing, 2014 (doi: 10.3390/rs6054190)
    14. Zhang et al., 2014. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data, Remote Sens. Environ, 2014 (http://dx.doi.org/10.1016/j.rse.2014.01.025)
    15. Xu et al., 2014. Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011, Remote Sens. 2014 (doi: 10.3390/rs6043263)
    16. Ni and Park et al., 2014. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China, Remote Sens. 2014 (doi: 10.3390/rs6053533)
    17. Chen et al., 2014. Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades, Remote Sens. Environ. 144: 28-41.
    18. Barichivitch et al., 2014. Temperature and snow-mediated controls of summer photosynthetic activity in northern terrestrial ecosystems between 1982 and 2011, Remote Sens., 6: 1390-1431.
    19. Ganguly et al., 2014. Green leaf area and fraction of photosynthetically active radiation absorbed by vegetation, In: J. M. Hanes (ed.), Biophysical Applications of Satellite Remote Sensing, Springer Remote Sensing/Photogrammetry, DOI: 10.1007/978-3-642-25047-7_2, 2014.

      Research Highlights
      Complete List of Research Articles