Ranga Myneni Publications

Publications on this site range from 2012 to present. For a complete list of Professor Myneni’s publications click here.

2015

  1. Piao et al., 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 2015 (doi: 10.1038/ncomms7911)
  2. Shi et al., 2015. Mapping annual precipitation across mainland China in the period 2001-2010 from TRMMM3B43 product using spatial downscaling approach. Remote Sensing (doi: 10.3390/rs70505849)
  3. Tian et al., 2015. Response of vegetation activity to climatic change and ecological programs in Inner Mongolia from 2000 to 2012. Ecol. Eng. (http://dx.doi.org/10.1016/j.ecoleng.2015.04.098)
  4. Piao et al., 2015. Detection and attribution of vegetation greening trend in China over the last 30 years, Global Change Biology, 2015 (doi: 10.1111/gcb.12795)
  5. Hilker et al., 2015. Reply to Gonsamo et al.: Effect of the Eastern Atlantic-West Russia pattern on Amazon vegetation has not been demonstrated, Proc. Nat. Acad. Sci. USA, 2015 (www.pnas.org/cgi/doi/10.1073/pnas.1423471112)
  6. Sitch et al., 2015. Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 2015 (doi: 10.5194/bg-12-653-2015)
  7. Wang et al., 2015. Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the last three decades? Global Ecol. Biogeography, 2015 (doi: 10.1111/geb.12289)

2014

  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. Piao et al., 2014. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity, Nature Communications, 2014 (doi:10.1038/ncomms6018)
  5. Hilker et al., 2014. Vegetation dynamics and rainfall sensitivity of the Amazon, Proc. Natnl. Acad. Sci. USA (www.pnas.org/cgi/doi/10.1073/pnas.1404870111)
  6. Peng et al., 2014. Afforestation in China cools local land surface temperature, PNAS (www.pnas.org/cgi/doi/10.1073/pnas.1315126111)
  7. 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.
  8. 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
  9. 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)
  10. 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
  11. 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
  12. 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).
  13. 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).
  14. Van Oijen et al., 2014. Impact of droughts on the C-cycle in European Vegetation: a probabilistic risk analysis using six vegetation models, Biogeosciences, 11: 6357–6375, 2014, doi:10.5194/bg-11-6357-2014
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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.
  20. 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.
  21. 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.

2013

  1. Xu et al., 2013 Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information
    Prof. Snyder’s Commentary
  2. Peng et al., 2013 Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation, 2013, doi:10.1038/nature12434
    Prof. Still’s “News and Views” item
  3. Knyazikhin et al., 2013 Reply to Ollinger et al.: Remote Sensing of Leaf Nitrogen and Emergent Ecosystem Properties, Proc. Natl. Acad. Sci. USA (www.pnas.org/cgi/doi/10.1073/pnas.1305930110)
  4. Knyazikhin et al., 2013 Reply to Townsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry. Proc. Natl. Acad. Sci. USA (www.pnas.org/cgi/doi/10.1073/pnas.1301247110)
  5. Fu et al., 2013 Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection, Proc. Natl. Acad. Sci. USA, doi: 10.1073/pnas.1302584110
  6. Wang et al., 2013 Variations in atmospheric CO2 growth rates coupled with tropical temperature, Proc. Natl. Acad. Sci. USA, doi: 10.1073/pnas.1219683110
  7. Ciais et al., 2013. Carbon and Other Biogeochemical Cycles, IPCC AR5 Chapter 6, 2013.
  8. Ichii et al., 2013 Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011, Remote Sens., doi: 10.3390/rs5116043
  9. Xin et al., 2013 A production efficiency model-based method for satellite estimates of corn and soybean yields in the midwestern US, Remote Sens., doi: 10.3390/rs5115926
  10. Tan et al., 2013 Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies, International J. Remote Sens. doi: 10.1080/01431161.2013.853143, 2013
  11. Yan et al., 2013 Diagnostic analysis of interannual variation of global land evapotranspiration over 1982–2011: Assessing the impact of ENSO, J. Geophys. Res., doi: 10.1002/jgrd.50693, 2013
  12. Barichivich et al., 2013 Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011, Global Change Biol., 2013, doi: 10.1111/gcb.12283
  13. Wang et al., 2013 Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets, Remote Sens. 2013, 5, 2857-2882; doi:10.3390/rs5062857
  14. Bi et al., 2013 Divergent Arctic-Boreal Vegetation Changes Between North America and Eurasia Over the Past 30 Years, Remote Sens., doi:10.3390/rs5052093
  15. Piao et al., 2013 Evaluation of Terrestrial Carbon Cycle Models for their Response to Climate Variability and to CO2 Trends, Global Change Biology, doi: 10.1111/gcb.12187
  16. Anav et al., 2013 Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models, J. Climate, doi:10.1175/JCLI-D-12-00417.1
  17. Fang et al., 2013 Characterization and Intercomparison of Global Moderate Resolution Leaf Area Index (LAI) Products: Analysis of Climatologies and Theoretical Uncertainties, J. Geophys. Res.Biogeosci., doi:10.1002/jgrg.20051
  18. Mohammat et al., 2013 Drought and Spring Cooling Induced Recent Decrease in Vegetation Growth in Inner Asia, Agric. For. Meteorol., http://dx.doi.org/10.1016/j.agrformet.2012.09.014
  19. Poulter et al., 2013 Recent Trends in Inner Asian Forest Dynamics to Temperature and Precipitation Indicate High Sensitivity to Climate Change, Agric. For. Meteorol., http://dx.doi.org/10.1016/j.agrformet.2012.12.006
  20. Mao et al., 2013 Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982-2009, Remote Sens. 2013, 5, 1484-1497; doi:10.3390/rs5031484
  21. Zhu et al., 2013 Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011, Remote Sens. 2013, 5, 927-948; doi:10.3390/rs5020927
    Supplementary Information
  22. Luo et al., 2013 Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China, Remote Sens. 2013, 5, 845-861; doi:10.3390/rs5020845
  23. Fang et al., 2013 The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective, Remote Sens. 2013, 5, 830-844; doi:10.3390/rs5020830
  24. Shi & Choi et al., 2013 Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA, Remote Sens. 2013, 5, 284-306; doi:10.3390/rs5010284
  25. Choi & Ni et al., 2013 Allometric Scaling and Resource Limitations Model of Tree Heights: Part 2. Site Based Testing of the Model, Remote Sens. 2013, 5, 202-223; doi:1

2012

  1. Saatchi et al., 2012 Persistent Effects of a Severe Drought on Amazonian Forest Canopy, Proc. Natl. Acad. Sci. USA, www.pnas.org/cgi/doi/10.1073/pnas.1204651110
  2. Knyazikhin et al., 2012 Hyperspectral remote sensing of foliar nitrogen content,” Proc. Natl. Acad. Sci. USA, www.pnas.org/cgi/doi/10.1073/pnas.1210196109
    Commentary by Susan L. Ustin, “Remote Sensing of Canopy Chemistry” Proc. Natl. Acad. Sci. USA (2013).
  3. Cong et al., 2012 Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multi-method analysis, Global Change Biol., doi: 10.1111/gcb.12077
  4. Xu et al., 2012 Spatio-temporal patterns of the area experiencing negative vegetation growth anomalies in China over the last three decades, Environ. Res. Lett., 7, doi:10.1088/1748-9326/7/3/035701
  5. Peng et al., 2012 Response to Comment on “Surface Urban Heat Island Across 419 Global Big Cities,” Environ. Sci. Technol., 2012, 46, pp 6889-6890, DOI:10.1021/es301811b
  6. Samanta et al., 2012 Why is remote sensing of Amazon forest greenness so challenging? Earth Int., Vol. 16(2), Paper 7, doi:10.1175/2012EI440.1
  7. W. Yang and R.B. Myneni, 2012, Analysis, Improvement and Application of the MODIS LAI Products, LAP Lambert Academic Publishing GmbH and Co., Saarbruecken, Germany, ISBN: 978-3-659-00068-3.
  8. Samanta et al., 2012 Interpretation of variations in MODIS-measured greenness levels of Amazon forests during 2000 to 2009, Environ. Res. Lett., doi:10.1088/1748-9326/7/2/024018
  9. Zeng et al., 2012 Global evapotranspiration over the past three decades: estimation based on the water balance equation combined with empirical models, Environ. Res. Lett., doi:10.1088/1748-9326/7/1/014026
  10. Ganguly et al., 2012 Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration, Remote Sens. Environ. doi:10.1016/j.rse.2011.10.032
  11. Samanta et al., 2012 Seasonal changes in leaf area of Amazon forests from leaf flushing and abscission, J. Geophys. Res. VOL. 117, G01015, doi:10.1029/2011JG001818
  12. Peng et al., 2012 Surface Urban Heat Island Across 419 Global Big Cities, Environ. Sci. & Tech., Environ. Sci. Technol., 2012, 46 (2), pp 696-703, DOI:10.1021/es2030438
  13. Hashimoto et al., 2012 Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data, Remote Sensing, 4, 303-326; doi:10.3390/rs4010303