Myneni’s Publications 2024 (344 – )

  1. Winkler et al., 2024. Carbon system state determines warming potential of emissions, PLoS ONE 19(8): e0306128. doi: 10.1371/journal.pone.0306128
  2. Li et al., 2024. Vegetation greenness in 2023, Nature Reviews Earth & Environment, doi: 10.1038/s43017-024-00543-z
  3. Yan et al., 2024. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening, Nature Ecology and Evolution, doi: 10.1038/s41559-024-02372-1
  4. Pu et al., 2024. Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022, Earth Syst. Sci. Data, 16, 15–34, 2024, https://doi.org/10.5194/essd-16-15-2024
  5. Zhang et al., 2024. An Insight Into the Internal Consistency of MODIS Global Leaf Area Index Products, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 62, 2024, doi: 10.1109/TGRS.2024.3434366
  6. Peng et al., 2024. Revisiting the consistency of MODIS LAI products from a new perspective of spatiotemporal variability, Intl. J. Digital Earth, doi:10.1080/17538947.2024.2407045
  7. Zhang et al., 2024. An Insight into The Internal Consistency of MODIS Global Leaf Area Index Products, IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2024.3434366
  8. Zhao et al., 2024. A global dataset of the fraction of absorbed photosynthetically active radiation for 1982–2022, Scientific Data, doi: 10.1038/s41597-024-03561-0
  9. Yan et al., 2024. HiQ LAI: A high-quality reprocessed MODIS leaf area index dataset with better spatiotemporal consistency from 2000 to 2002, Earth Syst. Sci. Data, 16, 1601-1622, 2024, doi: 10.5194/essd-16-1601-2024
  10. Chen et al., 2024. The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades, Environ. Res. Lett., 19 (2024) 014052, https://doi.org/10.1088/1748-9326/ad107f
  11. Roman et al., 2024. Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products, Remote Sens. Environ., 302 (2024) 113963
  12. She et al., 2024. Varied responses of Amazon forests to the 2005, 2010, and 2015/2016 droughts inferred from multi-source satellite data, Remote Sens. Environ., 3353 (2024) 110051

Myneni’s Publications 2023 (331 – 343)

  1. Zuo et al., 2023. Simulating Potential Tree Height for Beech–Maple–Birch Forests in Northeastern United States on Google Earth Engine, J. Remote Sens., 2023;3:Article 0084. https://doi.org/10.34133/remotesensing.0084
  2. Cao et al., 2023. Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, 2023, https://doi.org/10.5194/essd-15-4877-2023
  3. Pan et al., 2023. Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes, Env. Res. Lett., 18 044045, https://doi.org/10.1088/1748-9326/acca34.
  4. Li et al., 2023. Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022, Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, 2023.
  5. Zeng et al., 2023. Structural complexity biases vegetation greenness measures. Nat Ecol Evol (2023). https://doi.org/10.1038/s41559-023-02187-6
  6. Gao et al., 2023. Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations. Remote Sens. Environ., doi: 10.1016/j.rse.2023.113665
  7. Zhang et al., 2023. Autumn canopy senescence has slowed down with global warming since the 1980s in the Northern Hemisphere. Comm. Earth and Environ., doi: 10.1038/s43247-023-00835-0
  8. Wang et al., 2023. Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal Correlation Information. IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2023.3264280
  9. Meng et al., 2023. Climate change increases carbon allocation to leaves in early leaf green-up. Ecol. Lett., doi: 10.1111/ele.14205
  10. Tucker et al., 2023. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature, doi: 10.110.1038/s41586-022-05653-6
  11. Pu et al., 2023. Improving the MODIS LAI compositing using prior time-series information. Remote Sens.Env.,doi: 10.1016/j.rse.2023.113493
  12. Dong et al., 2023. A method for retrieving coarse-resolution leaf area index for mixed biomes using a mixed-pixel correction factor. IEEE Trans. Geosci. Remote Sens.,doi: 10.1109/TGRS.2023.3235949
  13. Li et al., 2023.A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels. J. Remote Sens., doi: 10.34133/remotesensing.0038

Myneni’s Publications 2022 (325 – 330)

  1. Zhao et al., 2022. Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems. Nature Plants,doi: 10.1038/s41477-022-01278-9
  2. Zou et al., 2022. Revisit the Performance of MODIS and VIIRS Leaf Area Index Products from the Perspective of Time-Series Stability. IEEE J. Selected Topics in Applied Earth Observations and Remote Sens.,doi: 10.1109/JSTARS.2022.3214224
  3. Jiang et al., 2022. Warming does not delay the start of autumnal leaf coloration but slows its progress rate. Global Ecol. Biogeography, doi: 10.1111/geb.13581
  4. Erlandsson et al., 2022. An artificial intelligence approach to remotely assess pale lichen biomass. Remote Sens. Environment, 280 (2022) 113201, doi: 10.1016/j.rse.2022.113201
  5. Li et al., 2022. Regional asymmetry in the response of global vegetation growth to springtime compound climate events. Communications Earth & Environment, doi: 10.1038/s43247-022-00455-0
  6. Sun et al., 2022. Seasonal and long-term variations in leaf area of Congolese rainforest. Remote Sens. Environ., doi: 10.1016/j.rse.2021.112762

Myneni’s Publications 2021 (313 – 324)

  1. Ni et al., 2021. Vegetation Angular Signatures of Equatorial Forests From DSCOVR EPIC and Terra MISR Observations. Frontiers in Remote Sens., doi: 10.3389/frsen.2021.766805
  2. Yan et al., 2021. Modeling the radiation regime of a discontinuous canopy based on the stochastic radiative transport theory: Modification, evaluation and validation. Remote Sens. Environ., doi: 10.1016/j.rse.2021.112728
  3. Zhu et al., 2021. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science, doi: 10.1126/science.abg5673
  4. Winkler et al., 2021. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2. Biogeosciences, 18, 4985–5010, Publisher Site
  5. Hashimoto et al., 2021. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nature Communications, https://doi.org/10.1038/s41467-021-20994-y
  6. Xu et al., 2021. Seasonal biological carryover dominates northern vegetation growth. Nature Communications, https://doi.org/10.1038/s41467-021-21223-2
  7. Cortes et al., 2021. Where are Global Vegetation Greening and Browning Trends Significant? Geophys. Res. Lett., doi: 10.1029/2020GL091496
  8. Yan et al., 2021. Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2021.112438
  9. Chen et al., 2021. Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sens., DOI: 10.1109/JSTARS.2021.3094647
  10. Yan et al., 2021. A Bibliometric Visualization Review of the MODIS LAI/FPAR Products from 1995 to 2020. Journal of Remote Sensing, doi: 10.1109/TGRS.2021.3064018
  11. Gorkavy et al., 2021. Earth Imaging From the Surface of the Moon With a DSCOVR/EPIC-Type Camera. Frontiers in Remote Sensing, doi: 10.3389/frsen.2021.724074
  12. Yan et al., 2021. Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain. IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2021.3064018

Myneni’s Publications 2020 (303 – 312)

  1. Chi et al., 2020. Biophysical impacts of Earth greening largely controlled by aerodynamic resistance. Sci. Adv., 6 : eabb1981
  2. Huang et al., 2020. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv., 6 : eabb8508
  3. Chen et al., 2020. Attribution of Land‐Use/Land‐Cover Change Induced Surface Temperature Anomaly: How Accurate Is the First‐Order Taylor Series Expansion? JGR Biogeosciences doi: 10.1029/2020JG005787
  4. Zhao et al., 2020. Future greening of the Earth may not be as large as previously predicted. Agric. For. Meteorol., doi: 10.1016/j.agrformet.2020.108111
  5. Lian et al., 2020. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Science Advances, 6, eaax0255
  6. Yang et al., 2020. Interannual Variability of Carbon Uptake of Secondary Forests in the Brazilian Amazon (2004‐2014). Global Biogeochem. Cycles, doi:10.1029/2019GB006396
  7. Pu et al., 2020. Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland. Remote Sens., doi:10.3390/rs12203391
  8. Xu et al., 2020. Improving leaf area index retrieval over heterogeneous surface mixed with water. Remote Sens. Environ., doi:10.1016/j.rse.2020.111700
  9. Yan et al., 2020. Recent wetting trend in China from 1982 to 2016 and the impacts of extreme El Niño events. Int. J. Clim., doi:10.1002/joc.6530
  10. Dunn et al., 2020. Global Climate [in “State of the Climate in 2019″]. Bull. Amer. Meteor., 101 (8), S9–S127, https://doi.org/10.1175/BAMSD-20-0104.1

Myneni’s Publications 2019 (290 – 302)

  1. Piao et al., 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth and Environment, doi: 10.1038/s43017-019-0001-x
  2. Chen et al., 2019. China and India lead in greening of the world through land-use management. Nature Sustainability, doi:10.1038/s41893-019-0220-7
  3. Winkler et al., 2019. Earth system models underestimate carbon fixation by plants in the high latitudes. Nature Communications, doi:10.1038/s41467-019-08633-z
  4. Huang et al., 2019. Air temperature optima of vegetation productivity across global biomes. Nature Ecol. Evolution, doi:10.1038/s41559-019-0838-x
  5. Fan et al., 2019. Satellite-observed pantropical carbon dynamics. Nature Plants, doi:10.1038/s41477-019-0478-9
  6. Winkler et al., 2019. Investigating the applicability of emergent constraints. Earth System Dynamics, doi:10.5194/esd-10-501-2019
  7. Park et al., 2019. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Global Change Biology, doi:10.1111/gcb.14638
  8. Tømmervik et al., 2019. Legacies of Historical Exploitation of Natural Resources Are More Important Than Summer Warming for Recent Biomass Increases in a Boreal–Arctic Transition Region. Ecosystems, doi:10.1007/s10021-019-00352-2
  9. Hashimoto et al., 2019. Constraints to Vegetation Growth Reduced by Region-Specific Changes in Seasonal Climate. Climate, doi:10.3390/cli7020027
  10. Zhang et al., 2019. Mapping Maximum Tree Height of the Great Khingan Mountain, Inner Mongolia Using the Allometric Scaling and Resource Limitations Model. Forests, doi:10.3390/f10050380
  11. Chen et al., 2019. Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data. Remote Sens., doi:10.3390/rs11131517
  12. Niu et al., 2019. Ecological engineering projects increased vegetation cover, production, and biomass in semiarid and subhumid Northern China. Land Degrad Dev., doi:10.1002/ldr.3351

Myneni’s Publications 2018 (276 – 289)

  1. Yang et al., 2018. Post-drought decline of the Amazon carbon sink. Nature Communications, doi:10.1038/s41467-018-05668-6
  2. Piao et al., 2018. Lower land-use emissions responsible for increased net land carbon sink during the slow warming period. Nature Geoscience, doi:10.1038/s41561-018-0204-7
  3. Wu et al., 2018. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nature Climate Change, https://doi.org/10.1038/s41558-018-0346-z
  4. Tian et al., 2018. Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. Nature Ecology and Evolution, doi:10.1038/s41559-018-0630-3
  5. Liu et al., 2018. Extension of the growing season increases vegetation exposure to frost. Nature Communications, doi:10.1038/s41467-017-02690-y
  6. Tong et al., 2018. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nature Sustainability, https://doi.org/10.1038/s41893-017-0004-x
  7. Bastos et al., 2018. Impact of the 2015/2016 El Nino on the terrestrial carbon cycle constrained by bottom-up and top-down approaches, Phil. Trans. R. Soc. B 373: 20170304.http://dx.doi.org/10.1098/rstb.2017.0304
  8. Xu et al., 2018. Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016. Forests, doi:10.3390/f9020073
  9. Xu et al., 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sens. Environ., doi:10.1016/j.rse.2018.02.049
  10. Wang et al., 2018. An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes. Remote Sens. 2018, 10, 1805; doi:10.3390/rs10111805
  11. Song et al., 2018. Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations, Remote Sens. 2018, 10, 1594; doi : 10.3390/rs10101594
  12. Li et al., 2018. Recent Changes in Global Photosynthesis and Terrestrial Ecosystem Respiration Constrained From Multiple Observations. Geophys. Res. Lett., doi:10.1002/2017GL076622
  13. Zeng et al., 2018. Impact of Earth Greening on the Terrestrial Water Cycle. J. Climate, doi: 10.1175/JCLI-D-17-0236.1
  14. Liu et al., 2018. Factors controlling changes in evapotranspiration, runoff, and soil moisture over the conterminous U.S.: accounting for vegetation dynamics. J. Hydrology, doi: 10.1016/j.jhydrol.2018.07.068

Myneni’s Publications 2017 (266 – 275)

  1. Fauchald et al., 2017. Arctic greening from warming promotes declines in caribou populations. Science Advances, 3, e1601365 (2017)
  2. Zeng et al., 2017. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nature Climate Change, doi: 10.1038/NCLIMATE3299
  3. Piao et al., 2017. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE3277
  4. Huang et al., 2017. Velocity of change in vegetation productivity over northern high latitudes. Nature Ecology and Evolution, doi: 10.1038/s41559-017-0328-y
  5. Zhu et al., 2017. Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models. Global Change Biol., doi: 10.1111/gcb.13723
  6. Bastos et al., 2017. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ. Res. Lett., doi.org/10.1088/1748-9326/aa67b5
  7. Yan et al., 2017. Generating Global Products of LAI and FPAR From SNPP-VIIRS Data: Theoretical Background and Implementation. IEEE Trans. Geosci. Remote Sens., doi:10.1109/TGRS.2017.2775247
  8. Chen et al., 2017. Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data. Remote Sensing, doi:10.3390/rs9040370
  9. Yang et al., 2017. Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: Theoretical basis. Remote Sens. Environ., http://dx.doi.org/10.1016/j.rse.2017.05.033
  10. Jiang et al., 2017. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Global Change Biology, doi: 10.1111/gcb.13787

Myneni’s Publications 2016 (252 – 265)

  1. Zhu et al., 2016. Greening of the Earth and its Drivers. Nature Climate Change, doi:10.1038/nclimate3004
  2. Mao et al., 2016. Human-induced Greening of the Northern Extratropical Land Surface. Nature Climate Change, doi: 10.1038/nclimate3056
  3. Li et al., 2016. Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets. PNAS, doi: 10.1073/pnas.1603956113
  4. Choi et al., 2016. Application of the metabolic scaling theory and water–energy balance equation to model large-scale patterns of maximum forest canopy height. Global Ecol. Biogeography, doi:10.1111/geb.12503
  5. Park et al., 2016. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Env. Res. Lett., doi:10.1088/1748-9326/11/8/084001
  6. Yan et al., 2016. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements, Remote Sensing, doi:10.3390/rs8050359
  7. Yan et al., 2016. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison, Remote Sensing, doi:10.3390/rs8060460
  8. Bi et al., 2016. Amazon Forests’ Response to Droughts: A Perspective from the MAIAC Product, Remote Sensing, doi:10.3390/rs8040356
  9. Yang et al., 2016. Abiotic Controls on Macroscale Variations of Humid Tropical Forest Height, Remote Sensing, doi:10.3390/rs8060494
  10. Yang et al., 2016. Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples, Remote Sensing, doi:10.3390/rs8070563
  11. Chen et al., 2016. Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011. Int. J. Digital Earth (doi:10.1080/17538947.2016.1180549)
  12. Yan et al., 2016. Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982-2011 by using PDSI indices and agriculture drought survey data. J. Geophys. Res., (Atmos.), (doi:10.1002/2015JD024285)
  13. Yin et al., 2016. Nonlinear variations of forest leaf area index over China during 1982–2010 based on EEMD method. Int J Biometeorol., DOI 10.1007/s00484-016-1277-x
  14. Catalano et al., 2016. Observationally based analysis of land–atmosphere coupling. Earth Syst. Dynam. Discuss., Earth Syst. Dynam., 7, 251–266, 2016 (doi:10.5194/esd-7-251-2016)

Myneni’s Publications 2015 (236 – 251)

  1. Ukkola et al., 2015. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nature Climate Change, 2015 (DOI: 10.1038/NCLIMATE2831)
  2. Piao et al., 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 2015 (doi: 10.1038/ncomms7911)
  3. Anderegg et al., 2015. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl. Acad. Sci. USA, 2015 (www.pnas.org/cgi/doi/10.1073/pnas.1521479112)
  4. Shen et al., 2015. Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proc. Natl. Acad. Sci. USA, 2015 (www.pnas.org/cgi/doi/10.1073/pnas.1504418112)
  5. Bi et al., 2015. Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests. Environ. Res. Lett., 2015 (doi: 10.1088/1748-9326/10/6/064014)
  6. Xu et al., 2015. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett., 2015 (doi: 10.1088/1748-9326/10/8/084005)
  7. Reid et al., 2015. Global impacts of the 1980s regime shift. Global Change Biology, 2015 (doi: 10.1111/gcb.13106)
  8. Ni et al., 2015. Mapping forest canopy height over continental China using multi-source remote sensing data. Remote Sensing, 2015 (doi: 10.3390/rs70708436)
  9. Hilker et al., 2015. On the measurability of change in Amazon vegetation from MODIS. Remote Sens. Environ., 2015 (http://dx.doi.org/10.1016/j.rse.2015.05.020)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. Wu et al., 2015. A comparative study of predicting DBH and stem volume of individual trees in a temperate forest using airborne waveform LiDAR, IEEE Geoscience and Remote Sensing, 2015 (doi: 10.1109/LGRS.2015.2466464)

Myneni’s Publications 2014 (215 – 235)

  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, Proc. Natl. Acad. Sci. USA (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.

Myneni’s Publications 2013 (190 – 214)

  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, Nature, 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

Myneni’s Publications 2012 (177 – 189)

  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

Myneni’s Publications 2011 (172 – 176)

  1. Myneni et al., 2011. Leaf area index and fraction of absorbed PAR products from Terra and Aqua MODIS sensors: Analysis, Validation, and Refinement. In “Land remote sensing and global environmental change” Eds. B. Ramachandran et al., Springer, New York, ISBN: 978-1-4419-6748-0. Info
  2. Peng et al., 2011. Recent change of vegetation growth trend in China, Environ. Res. Lett., Vol. 6, (2011) 044027 (13pp), doi:10.1088/1748-9326/6/4/044027
  3. Samanta et al., 2011. Comment on “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009”, Science, Vol. 333, p. 1093, DOI: 10.1126/science.1199048, 2011.
    Supplementary Online Material
  4. Xu and Samanta et al., 2011. Widespread decline in greenness of Amazonian vegetation due to the 2010 drought, Geophys. Res. Lett., Vol. 38, LXXXXX, doi:10.1029/2011GL046824, 2011.
    Auxiliary Material
  5. Zhousen et al., 2011. Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data, Remote Sens. Environ., doi:10.1016/j.rse.2011.02.010, 2011.

Myneni’s Publications 2010 (164 – 171)

  1. Samanta et al., 2010. MODIS Enhanced Vegetation Index data do not show greening of Amazon forests during the 2005 drought, New Phytologist, doi: 10.1111/j.1469-8137.2010.03516.x, 2010
  2. Yu et al., 2010. Regional distribution of forest height and biomass from multisensor data fusion, J. Geophys. Res., Vol. 115, G00E12, doi:10.1029/2009JG000995, 2010
  3. Knyazikhin et al., 2010. Canopy spectral invariants. Part 1: A new concept in remote sensing of vegetation. J. Quant. Spectroscp. Radiat. Trans., (2010), doi:10.1016/j.jqsrt.2010.06.014
  4. Schull et al., 2010. Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data. J. Quant. Spectroscp. Radiat. Trans., (2010), doi:10.1016/j.jqsrt.2010.06.004
  5. Samanta et al., 2010. Physical climate response to a reduction of anthropogenic climate forcing, Earth Interactions, Vol. 14, Paper No. 7, DOI: 10.1175/2010EI325.1, 2010.
  6. Zhang et al., 2010. Application of a satellite-based climate-variability impact index for crop yield forecasting in drought-stricken regions, African J. Plant Sci., Vol 4(4), 82-94, 2010
  7. Milesi et al., 2010. Decadal variations in NDVI and food production in India, Remote Sens. Vol. 2, 758-776, doi:10.3390/rs2030758, 2010
  8. Samanta et al., 2010. Amazon forests did not green-up during the 2005 drought, Geophys. Res. Lett., Vol. 37, L05401, doi:10.1029/2009GL042154, 2010,
    Supplemental Information

Myneni’s Publications 2008 (154 – 163)

  1. Hu et al., 2008. A rank-based algorithm for aggregating land cover maps, Chapter 1 in “Landscape Ecology Research Trends,” Eds. A. Dupont and H. Jacobs, Nova Science Publishers, Inc., ISBN 978-1-60456-672-7
  2. Ganguly et al., 2008. Generating vegetation leaf area index earth system data records from multiple sensors. Part 1: Theory. Remote Sens. Environ., Vol. 112(2008)4333–4343, doi:10.1016/j.rse.2008.07.014
  3. Ganguly et al., 2008. Generating vegetation leaf area index earth system data records from multiple sensors. Part 2: Implementation, Analysis and Validation. Remote Sens. Environ., 112(2008)4318–4332, doi:10.1016/j.rse.2008.07.013
  4. Kaufmann et al., 2008. Identifying climatic controls on ring width: The timing of correlations between tree rings and NDVI. Earth Interactions, Vol. 12, DOI: 10.1175/2008EI263.1. (2008).
  5. Kaufmann et al., 2008. The Power of Monitoring Stations and a CO2 Fertilization Effect: Evidence from Causal Relationships Between NDVI and Carbon Dioxide. Earth Interactions, Vol: 12, DOI: 10.1175/2007EI240.1 (2008).
  6. Robinson et al., 2008. An empirical approach to retrieve monthly evapotranspiration over Amazonia, Int. J. Remote Sens., Vol. 29:7045–7063, 2008.
  7. Garrigues et al., 2008. Validation and Intercomparison of Global Leaf Area Index Products Derived from Remote Sensing Data, J. Geophys. Res., VOL. 113, G02028, doi:10.1029/2007JG000635, 2008.
  8. Garrigues et al., 2008. Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands, Agric. For. Meteorol., doi:10.1016/j.agrformet.2008.02.014.
  9. Gao et al., 2008. An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series. Geophys. Res. Lett., doi: 10.1109/LGRS.2007.907971.
  10. Huang et al., 2008. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurement, Remote Sensing of Environ., 112:35–50, 2008.

Myneni’s Publications 2007 (145 – 153)

  1. Wang et al., 2007. Intraseasonal interactions between temperature and vegetation over the Boreal forests, Earth Interactions, Vol. 11 (18), DOI: 10.1175/EI219.1, 2007.
  2. Schull et al., 2007. Physical interpretation of the correlation between multi-angle spectral data and canopy height. Geophys. Res. Lett., VOL. 34, L18405, doi:10.1029/2007GL031143, 2007.
  3. Shabanov et al., 2007. Stochastic radiative transfer model for mixture of discontinuous vegetation canopies, J. Quant. Spectroscp. Radiat. Trans., 107: 236-262.
  4. Myneni et al., 2007. Large seasonal changes in leaf area of amazon rainforests. Proc. Natl. Acad. Sci., 104: 4820-4823, doi:10.1073/pnas.0611338104.
  5. Richmond et al., 2007. Valuing ecosystem services: A shadow price for net primary production, Ecological Economics, 64: 454–462.
  6. Ichii et al., 2007. Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality, Global Change Biology, 13: 67-77, doi: 10.1111/j.1365-2486.2006.01277.x
  7. Hu et al., 2007. Analysis of the MISR LAI/FPARproduct for spatial and temporal coverage, accuracy and consistency, Remote Sens. Environ., 107: 334–347.
  8. Sundareshwar et al., 2007. Environmental monitoring network for Inda, Science, 316:204-205.
  9. Huang et al., 2007. Canopy spectral invariants for remote sensing and model applications, Remote Sens. Environ., 106: 106–122.

Myneni’s Publications 2006 (132 – 144)

  1. Tan et al., 2006. The impact of geolocation offsets on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration, Remote Sens. Environ., 105: 98–114.
  2. Yang et al., 2006. Analysis of prototype collection 5 products of leaf area index from Terra and Aqua MODIS sensors, Remote Sens. Environ., 104: 297–312.
  3. Wang et al., 2006. Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands: 2. A Coupled Stochastic Model, Earth Interactions, 10, Available online at Earth Interactions
  4. Wang et al., 2006. Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands: 1. Statistical Analysis, Earth Interactions, 10, Available online at Earth Interactions
  5. Ahl et al., 2006. Monitoring Spring Canopy Phenology of a Deciduous Broadleaf Forest Using MODIS, Remote Sens. Environ., 104: 88–95.
  6. Huang et al., 2006. The Importance of Measurement Error for Deriving Accurate Reference Leaf Area Index Maps for Validation of the MODIS LAI Product. IEEE Trans. Geosci. Remote Sens., 44:1866-1871.
  7. Yang et al., 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.
  8. Yang et al., 2006. MODIS Leaf Area Index Products: From Validation to Algorithm Improvement. IEEE Trans. Geosci. Remote Sens., 44: 1885-1898.
  9. Baret et al., 2006. Evaluation of the representativeness of networks of sites for the validation and inter-comparison of global land biophysical products. Proposition of the CEOS-BELMANIP. IEEE Trans. Geosci. Remote Sens., 44: 1794-1803.
  10. Morisette et al., 2006. Validation of global moderate resolution LAI Products: a framework proposed within the CEOS Land Product Validation subgroup IEEE Trans. Geosci. Remote Sens. 44: 1804-1817.
  11. Tan et al., 2006. Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument, Agric. For. Meteorol.,135: 124-134.
  12. Zhang et al., 2006. Monitoring of the 2005 U.S. Corn-belt Yield using Satellite Data, Eos, Vol. 87, No. 15, pg 150, 11 April 2006.
  13. Huete et al., 2006. Amazon rainforests green-up with sunlight in dry season, Geophys. Res. Lett., VOL. 33, L06405, doi:10.1029/2005GL025583.

Myneni’s Publications 2005 (124 – 131)

  1. Fang et al., 2005. Precipitation patterns alter growth of temperate vegetation, Geophys. Res. Lett., 32, L21411, doi:10.1029/2005GL024231.
  2. Zhang et al., 2005. Potential Monitoring of Crop Production Using a Satellite-Based Climate-Variability Impact Index, Agric. For. Meteorol., 132: 344-358.
  3. Huemmerich et al., 2005. Time-Series Validation of MODIS Land Biophysical Products in a Kalahari Woodland, Africa. Intl. J. Remote Sens., 26: 4381-4398.
  4. Knyazikhin et al., 2005. Influence of Small-Scale Drop Size Variability on the Estimation of Cloud Optical Properties. J. Atmos. Sci., 62:2555-2567.
  5. Knyazikhin, et al., 2005. Three-Dimensional Radiative Transfer in Vegetation Canopies. In: A. Davis and A. Marshak [Eds], “Three-Dimensional Radiative Transfer in the Cloudy Atmosphere,” Springer-Verlag, ISBN-10 (3-540-23958-8), pages 617-651.
  6. Shabanov et al., 2005. Optimization of the MODIS LAI and FPAR algorithm performance over broadleaf forests. IEEE Trans. Geosci. Remote Sens., 43:1855-1865.
  7. Tan et al., 2005. Validation of Moderate Resolution Imaging Spectroradiometer leaf area index product in croplands of Alpilles, France. J. Geophys. Res., VOL. 110, D01107, doi:10.1029/2004JD004860, 2005.
  8. Shabanov et al., 2005. Sub-pixel burn detection in MODIS 500-m data with ARTMAP neural networks. J. Geophys. Res., VOL. 110, D03111, doi:10.1029/2004JD005257, 2005.

Myneni’s Publications in 2004 (111 – 123)

  1. Hashimoto et al., 2004. El Nino Southern Oscillation induced variability in terrestrial carbon cycling. J. Geophys. Res., VOL. 109, D23110, doi:10.1029/2004JD004959, 2004.
  2. Wang et al., 2004. On the relation between the North Atlantic Oscillation and SSTs in the North Atlantic basin. J. Clim., 17(24), 4752-4759.
  3. McGuire et al., 2004. Land Cover Disturbances and Feedbacks to the Climate System in Canada and Alaska. In “Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface” by Gutman, G.; Janetos, A.C.; Justice, C.O.; Moran, E.F.; Mustard, J.F.; Rindfuss, R.R.; Skole, D.; Turner II, B.L.; Cochrane, M.A. (Eds.), Springer Verlag, ISBN: 1-4020-2561-0.
  4. Zhang et al., 2004. Climate related vegetation characteristics derived from MODIS LAI and NDVI. J. Geophys. Res., VOL. 109, D20105, doi:10.1029/2004JD004720, 2004
  5. D’Arrigo et al., 2004. Thresholds for warming induced growth decline at elevational treeline in Yukon territory, Canada. Global Biogeochemical Cycles, VOL. 18, GB3021, doi:10.1029/2004GB002249, 2004.
  6. Robinson et al., 2004. Climate Data Records from Environmental Satellites, The National Academies Press, Washington, DC, USA.
  7. Kotchenova et al., 2004. Lidar remote sensing for modelling net primary productivity of deciduous forests. Remote Sens. Environ., 92: 158-172.
  8. Wang et al., 2004. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sens. Environ., 91:114-127.
  9. Zhou et al., 2004. Evidence for a significant urbanization effect on climate in China. Proc. Natl. Acad. Sci., Vol. 101, No. 26,9540-9544.
  10. Kaufmann et al., 2004. The effect of growing season and summer greenness on northern forests. Geophys. Res. Lett., Vol. 31, No. 9, L09205, 10.1029/2004GL019608.
  11. Tian et al., 2004. Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophys. Res. Lett., Vol. 31, No. 5, L05504, 10.1029/2003GL019104.
  12. Tian et al., 2004. Comparison of seasonal and spatial variations of LAI/FPAR from MODIS and Common Land Model. J. Geophys. Res., Vol. 109, No. D1, D01103, 10.1029/2003JD003777.
  13. Stow et al., 2004. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems, Remote Sens. Environ., 89: 281-308.

Myneni’s Publications in 2003 (92 – 110)

  1. Kaufmann et al., 2003. The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Geophys. Res. Lett., VOL. 30, NO. 22, 2147,ndoi:10.1029/2003GL018251.
  2. Potter et al., 2003. Satellite data help predict terrestrial carbon sinks. EOS, 84(46): pages 502 & 508.
  3. Hu et al., 2003. Performance of thenMISR LAI and FPAR Algorithm: A Case Study In Africa, Remote Sens. Environ., 88:324-340
  4. Potter et al., 2003. Continental scalecomparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982-98. Global and Planetary Change, 39:201-213.
  5. Potter et al., 2003. Global teleconnections of climate to terrestrial carbon flux. J. Geophys. Res., Vol. 108, No. D17, 4556, 10.1029/2002JD002979
  6. Kotchenova et al., 2003. Modeling lidar waveforms with time-dependent stochastic radiative transfer theory for remote estimations of forest biomass. J. Geophys. Res., Vol. 108, No. D15, 4484, 10.1029/2002JD003288
  7. Zhou et al., 2003. Comparison of seasonal and spatial variations of albedos from MODIS and the Common Land Model. J. Geophys. Res., 108(D15), 4488, doi:10.1029/2002JD003326, 2003
  8. Buermann et al., 2003. Circulation anomalies explain interannual covariability in northern hemisphere temperatures and greenness. J. Geophys. Res., 108(D13), 4396, doi:10.1029/2002JD002630, 2003.
  9. Potter et al., 2003. Modeling Terrestrial Biogenic Sources of Oxygenated Organic Emissions. Earth Interactions, Vol. 7, Paper 7.
  10. Potter et al., 2003. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, 9(7): 1005-1021.
  11. Nemani et al., 2003. Climate driven increases in global net primary production from 1981 to 1991. Science, 300:1560-1563 (June-06-2003)
  12. Kaufmann et al., 2003. Reply to the comment by R. Lanfredi et al. to “Variations in Northern Vegetation Activity Inferred from Satellite Data of Vegetation Index During 1981 to 1999” by Zhou et al. J. Geophys. Res. Vol. 108 No. D12, 10.1029/2002JD003287.
  13. Zhuang et al., 2003. Carbon cycling in extratropical terrestrial ecosystems of the northern hemisphere during the 20th century: A modeling analysis of the influences of soil thermal dynamics. Tellus, 55B: 751-776.
  14. Lotsch et al., 2003. Land cover mapping in support of LAI/FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors, Int. J. Remote Sesns. 24, 1997-2016.
  15. Shabanov et al., 2003. The effect of spatial heterogeneity in validation of the MODIS LAI and FPAR algorithm over broadleaf forests, Remote Sens. Environ.,85: 410-423.
  16. Wang et al., 2003. A new parameterization of canopy spectral response to incident solar radiation: case study with hyperspectral data from pine dominant forest, Remote Sens. Environ., 85:304-315.
  17. Dong et al., 2003. Remote sensing of boreal and temperate forest woody biomass: Carbon pools, Sources and Sinks, Remote Sens. Environ., 84:393-410.
  18. Zhou et al., 2003. Relation between interannual variations in satellite measures of vegetation greenness and climate between 1982 and 1999. J. Geophys. Res. 108(D1), doi:10.1029/2002JD002510.
  19. Stow et al., 2003. Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s. Int. J. Remote Sens., 24, 1111-1117, 2003.

Myneni’s Publications in 2002 (75 – 91)

  1. Knyazikhin et al., 2002. A missing solution to the transport equation and its effect on estimation of cloud absorptive properties. J. Atmos. Sci., 59:3572-3585.
  2. Buermann et al., 2002. Analysis of a multi-year global vegetation leaf area index data set. J. Geophys. Res., 10.1029/2001JD000975.
  3. Tian et al., 2002. Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different resolutions. Remote Sens. Environ., 84:143-159.
  4. Combal et al., 2002. Retrieval of Canopy Biophysical Variables from Bidirectional Refectance: Using Prior Information to solve the Ill-posed Inverse Problem, Remote Sens. Environ., 84:1-15.
  5. Tian et al., 2002. Multiscale Analysis and Validation of the MODIS LAI Product. I. Uncertainty Assessment. Remote Sens. Environ., 83:414-430.
  6. Tian et al., 2002. Multiscale Analysis and Validation of the MODIS LAI Product. II. Sampling Strategy. Remote Sens. Environ., 83:431-441.
  7. Privette et al., 2002. Early spatial and temporal validation of MODIS LAI product in Africa. Remote Sens. Environ., 83: 232-243.
  8. Myneni et al., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ., 83: 214-231.
  9. Zeng et al., 2002. Coupling of the common land model to the NCAR community climate model. J. Clim., 15:1832-1854.
  10. Kaufmann et al., 2002. Reply to Comment on “Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999” by J. R. Ahlbeck. J. Geophys. Res., Vol. 107(D11), 10.1029/2001JD001516.
  11. Bogaert and Zhou et al., 2002. Evidence for a persistent and extensive greening trend in Eurasia inferred from satellite vegetation index data. J. Geophys. Res., Vol. 107(D11), 10.1029/2001JD001075.
  12. Lucht et al., 2002. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296:1687-1689 (May-31-2002).
  13. Bogaert et al., 2002. A mathematical comment on the formulae for the aggregation index and the shape index. Landscape Ecol., 17: 87-90.
  14. Zhang et al., 2002. Assessing the Information Content of Multiangle Satellite Data for Mapping Biomes. I: Statistical Analysis, Remote Sens. Environ, 80: 418-434 (2002).
  15. Zhang et al., 2002. Assessing the Information Content of Multiangle Satellite Data for Mapping Biomes. II: Theory, Remote Sens. Environ., 80: 435-446 (2002).
  16. Dickinson et al., 2002. Nitrogen Controls on Climate Model Evapotranspiration. J. Clim., 15(3): 278-295.
  17. Shabanov et al., 2002. Analysis of interannual changes in northern vegetation activity observed in AVHRR data during 1981 to 1994. IEEE Trans. Geosci. Remote Sens., 40:115-130.

Myneni’s Publications in 2001 (68 – 74)

  1. Myneni and Dong et al., 2001. A large carbon sink in the woody biomass of northern forests. Proc. Natl. Acad. Sci. USA., 98(26): 14784-14789. Supplemental information
  2. Tucker et al., 2001. Higher northern latitude NDVI and growing season trends from 1982 to 1999. Int. J. Biometeorol., 45:184-190.
  3. Wang et al., 2001. Investigation of product accuracy as a function of input and model uncertainities: Case study with SeaWiFS and MODIS LAI/FPAR Algorithm. Remote Sens. Environ. 78:296-311.
  4. Zhou et al., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res., 106(D17): 20069-20083.
  5. Buermann et al., 2001. Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Climate, 14(17): 3536-3550.
  6. Dong et al., 2001. Improving numerical precision of simulated soil water fluxes in land surface models. J. Geophys. Res., 106(D13): 14,357-14,368.
  7. Panferov, O. et al., 2001. The role of canopy structure in the spectral variation of transmission and absorption of solar radiation in vegetation canopies. IEEE Trans. Geosci. Remote Sens., 39:241-253.

Myneni’s Publications in 2000 (63 – 67)

  1. Kaufmann et al., 2000. Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens., 38: 2584-2597.
  2. Zhang et al., 2000. Prototyping of MISR LAI and FPAR algorithm with POLDER data over Africa. IEEE Trans. Geosci. Remote Sens., 38(5): 2402-2418.
  3. Tian et al., 2000. Prototyping of MODIS LAI and FPAR algorithm with LASUR and LANDSAT data. IEEE Trans. Geosci. Remote Sens., 38(5): 2387-2401.
  4. Shabanov, N. V. et al., 2000. Stochastic modeling of radiation regime in discontinuous vegetation canopy, Remote Sens. Environ., 74:125-144.
  5. Weiss, M., et al., 2000. Investigation of a model inversion technique for the estimation of crop characteristics from spectral and directional reflectance data. Agronomie, 20: 3-22.

Myneni’s Publications in 1998 (56 – 62)

  1. Knyazikhin, Y., et al., 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J. Geophys. Res., 103:32,257-32,276.
  2. Knyazikhin, Y., et al., 1998. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MISR data. J. Geophys. Res., 103:32,239-32,256.
  3. Diner, D. J., et al., 1998. Multi-angle imaging spectroradiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosc. Remote Sens., 36:1072-1087.
  4. Justice, C. O., et al., 1998. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosc. Remote Sens., 36:1228-1249.
  5. Martonchik, J. V., et al., 1998. Determination of land and ocean reflective, radiative, and biophysical properties using multi-angle imaging. IEEE Trans. Geosc. Remote Sens.,36:1266-1281.
  6. Knyazikhin, Y., et al., 1998. Influence of small-scale structure on radiative transfer and photosynthesis in vegetation canopies. J. Geophys. Res., 103 (D6): 6133-6144.
  7. Myneni, R. B., et al., 1998. Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res., 103 (D6): 6145-6160.

Myneni’s Publications in 1997 (54 – 55)

  1. Myneni, R. B., et al., 1997. Algorithm for the estimation of global land cover, LAI and FPAR based on radiative transfer models. IEEE Trans. Geosc. Remote Sens., 35: 1380-1393.
  2. Myneni, R. B., et al., 1997. Increased plant growth in the northern high latitudes from 1981-1991. Nature, 386:698-701.

Myneni’s Publications in 1996 (49 – 53)

  1. Verstraete et al., 1996. Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing. Remote Sens. Environ., 58:201-214.
  2. Veroustraete et al., 1996. Estimating net ecosystem exchange of carbon using the normalized difference vegetation index and an ecosystem model. Remote Sens. Environ., 58: 115-130.
  3. Begue, A and Myneni, R. B., 1996. Operational relationships between NOAA Advanced Very High Resolution Radiometer vegetation indices and daily fAPAR established for Sahelian vegetation canopies. J. Geophys. Res., 101: 21275-21283.
  4. Myneni et al., 1996. Satellite-based identification of linked vegetation index and sea surface temperature anomaly areas from 1982-1990 for Africa, Asutralia and South America. Geophysical Res. Letters, 23: 729-732.
  5. Privette et al., 1996. Preferred sampling schemes for the estimation of canopy LAI, albedo and APAR through vegetation BRDF model inversions. IEEE Trans. Geosc. Remote Sens. 34: 272-284.

Myneni’s Publications in 1995 (45 – 48)

  1. Privette et al., 1995. Inversion of a soil bidirectional reflectance model for use with vegetation reflectance models. J. Geophy. Res. 100: 25497-25508.
  2. Myneni et al., 1995. Potential gross primary productivity of vegetation from 1982-1990. Geophysical Res. Letters, 22: 2617:2620.
  3. Myneni et al., 1995. The meaning of spectral vegetation indices. IEEE Trans. Geosc. Remote Sens. 33: 481-486.
  4. Myneni et al., 1995. Optical remote sensing of vegetation: modelling, caveats and algorithms. Remote Sens. Environ., 51: 169-188.

Myneni’s Publications in 1994 (40 – 44)

  1. Myneni, R. B. and Williams, D. L., 1994. On the relationship between FAPAR and NDVI. Remote Sens. Environ., 49:200-211.
  2. Knyazikhin et al., 1994. Optimization of solar radiation input in forest canopy as a tool for planting pattern of trees. Trans. Theory and Stat. Phys., 23: 671-700.
  3. Privette et al., 1994. Invertibility of a 1D discrete ordinates canopy reflectance model. Remote Sens. Environ., 48: 89-105.
  4. Myneni, R. B. and Asrar, G., 1994. Atmospheric effects and spectral vegetation indices. Remote Sens. Environ., 47: 390-402.
  5. Veroustraete, F., Patyn, J. and Myneni, R.B., 1994. Forcing of a Simple Ecosystem Model with FAPAR and Climatic Data to Estimate Regional Scale Photosynthetic Assimilation. In: Veroustraete et al. [Ed.], “Vegetation: Modeling and Climatic Change Effects.” SFB Academic Publishing bv., The Hague, The Netherlands.

Myneni’s Publications in 1993 (36 – 39)

  1. Asrar, G. and Myneni, R.B., 1993. Atmospheric effects in the remote sensing of surface albedo and radiation absorption by vegetation canopies. Remote Sens. Revs., 7: 197-222.
  2. Myneni, R.B. and Asrar, G., 1993. Radiative transfer in three-dimensional atmosphere/vegetation media. J. Quant. Spectroscp. Radiat. Transfer, 49: 585-598.
  3. Myneni, R.B. and Choudhury, B. J., 1993. Synergistic use of optical and microwave data in agrometeorological applications. Adv. Space Res., 13: 239-248.
  4. Myneni, R.B., Impens, I. and Asrar, G., 1993. Simulation of space measurements of vegetation canopy bidirectional reflectance factors. Remote Sens. Rev., 7 : 19-41.

Myneni’s Publications in 1992 (29 – 35)

  1. Myneni, R.B., Ganapol, B. D. and Asrar, G., 1992. Remote sensing of vegetation canopy photosynthetic and stomatal conductance efficiencies. Remote Sens. Environ., 42 : 217-238.
  2. Ganapol, B. D. and Myneni, R.B., 1992. The application of the principles of invariance to the radiative transfer equation in plant canopies. J. Quant. Spectroscp. Radiat. Transfer, 48: 321-339.
  3. Myneni, R.B., Asrar, G. and Hall, F. G., 1992. A three dimensional radiative transfer method for optical remote sensing of vegetated land surfaces. Remote Sens. Environ., 41: 105-121.
  4. Asrar, G., Myneni, R.B. and Choudhury, B. J., 1992. Spatial heterogeneity in vegetation canopies and absorbed photosynthetically active radiation: A modeling study. Remote Sens. Environ., 41: 85-103.
  5. Myneni, R.B., Asrar, G., Tanre, D. and Choudhury, B. J., 1992. Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces. IEEE Trans. Geosc. Remote Sens. 30:302-314.
  6. Ganapol, B. D. and Myneni, R.B., 1992. The F_N method for the one-angle radiative transfer equation applied to plant canopies. Remote Sens. Environ., 39: 213-231.
  7. Knyazikhin, Y. V., Marshak, A. L. and Myneni, R.B., 1992. Interaction of photons in a canopy of finite dimensional leaves. Remote Sens. Environ., 39: 61-74.

Myneni’s Publications in 1991 (23 – 28)

  1. Myneni, R.B. and Asrar, G., 1991. Photon interaction cross sections for aggregations of finite dimensional leaves. Remote Sens. Environ., 37: 219-224.
  2. Myneni, R.B. and Ganapol, B. D., 1991. A simplified formulation of photon transport in leaf canopies with finite dimensional scatterers. J. Quant. Spectroscp. Radiat. Transfer, 46: 135-140.
  3. Myneni, R.B., Marshak, A.L. and Knyazikhin, Yu., 1991. Transport theory for leaf canopies with finite dimensional scattering centers. J. Quant. Spectroscp. Radiat. Transfer, 46: 259-280.
  4. Myneni, R.B., 1991. Modelling radiative transfer and photosynthesis in three dimensional vegetation canopies. Agric. Forest Meteorol., 55:323-344.
  5. Asrar, G. and Myneni, R.B., 1991. Applications of Radiative Transfer Models for Remote Sensing of Vegetation Conditions and States. In: R.B. Myneni and J. Ross [Eds.], “Photon-Vegetation Interactions: Applications in Optical Remote Sensing and Plant Physiology”, Springer-Verlag, pp. 539-557.
  6. Myneni, R.B., Marshak, A. and Knyazihin, M. and Asrar, G., 1991. Discrete Ordinates Method for Photon Transport in Leaf Canopies. In: R.B. Myneni and J. Ross [Eds.], “Photon-Vegetation Interactions: Applications in Optical Remote Sensing and Plant Physiology”, Springer-Verlag, pp. 45-109.

Myneni’s Publications in 1990 (22)

  1. Myneni, R.B., Asrar, G. and Gerstl, S.A.W., 1990. Radiative transfer in three dimensional leaf canopies. Transport Theory and Statistical Physics, 19:205-250.

Myneni’s Publications in 1989 (18 – 21)

  1. Myneni, R.B., Ross, J. and Asrar, G., 1989. A review on the theory of photon transport in leaf canopies in slab geometry. Agric. For. Meteorol., 45:1-153.
  2. Asrar, G., Myneni, R.B., Li., Y. and Kanemasu, E. T., 1989. Measuring and Modeling Spectral Characteristics of a Tallgrass Prairie. Remote Sens. Environ., 27:143-155.
  3. Asrar, G., Myneni, R.B. and Kanemasu, E.T., 1989. Estimation of plant canopy attributes from spectral reflectance measurements. In: G. Asrar [Ed.], “Theory and Applications of Optical Remote Sensing”, John Wiley & Sons, pp. 252-297.
  4. Myneni, R.B., Asrar, G. and Kanemasu, E.T., 1989. The theory of photon transport in leaf canopies. In: G. Asrar [Ed.], “Theory and Applications of Optical Remote Sensing”, John Wiley & Sons, pp. 142-205.

Myneni’s Publications in 1988 (10 – 17)

  1. Myneni, R.B., Asrar, G. and Kanemasu, E.T., 1988. Finite element discrete ordinates method for radiative transfer in non-rotationally invariant scattering media: Application to the leaf canopy problem. J. Quant. Spectroscp. Radiat. Transfer, 40:147-155.
  2. Myneni, R.B., Asrar, G. and Kanemasu, E.T., 1988. Solution of an integral equation encountered in studies on radiative transfer in completely absorbing leaf canopies. J. Quant. Spectroscp. Radiat. Transfer, 40:157-164.
  3. Myneni, R.B. and Kanemasu, E.T., 1988. The hot spot of vegetation canopies. J. Quant. Spectroscp. Radiat. Transfer, 40:165-168.
  4. Shultis, J.K. and Myneni, R.B., 1988. Radiative transfer in vegetation canopies with anisotropic scattering. J. Quant. Spectroscp. Radiat. Transfer, 39:115-129.
  5. Myneni, R.B., Gutschick, V.P., Asrar, G. and Kanemasu, E.T., 1988. Photon transport in vegetation canopies with anisotropic scattering: Part IV. Discrete-ordinates finite-difference exact-kernel technique for photon transport in slab geometry for the two-angle problem. Agric. For. Meteorol., 42:101-120.
  6. Myneni, R.B., Gutschick, V.P., Asrar, G. and Kanemasu, E.T., 1988. Part III. The scattering phase functions in the two-angle problem. Agric. For. Meteorol., 42:87-99.
  7. Myneni, R.B., Gutschick, V.P., Asrar, G. and Kanemasu, E.T., 1988. Part II. Discrete-ordinates finite-difference exact-kernel technique for photon transport in slab geometry for the one-angle problem. Agric. For. Meteorol., 42:17-40.
  8. Myneni, R.B., Gutschick, V.P., Asrar, G. and Kanemasu, E.T., 1988. Part I. The scattering phase functions in the one-angle problem. Agric. For. Meteorol., 42:1-16.

Myneni’s Publications in 1987 (07 – 09)

  1. Myneni, R.B., Asrar, G., Burnett, R.B. and Kanemasu, E.T., 1987. Radiative transfer in an anisotropically scattering vegetative medium. Agric. For. Meteorol., 41:97-121.
  2. Myneni, R.B., Asrar, G. and Kanemasu, E.T., 1987. Reflectance of a soybean canopy using the method of Successive Orders of Scattering Approximations [SOSA]. Agric. For. Meteorol., 40:71-87.
  3. Myneni, R.B., Asrar, G. and Kanemasu, E.T., 1987. Light scattering in plant canopies: The method of Successive Orders of Scattering Approximations [SOSA]. Agric. For. Meteorol., 39:1-12.

Myneni’s Publications in 1986 (04 – 06)

  1. Myneni, R.B., Burnett, R.B., Asrar, G. and Kanemasu, E.T., 1986. Single scattering of parallel direct and axially symmetric diffuse solar radiation in vegetative canopies. Remote Sens. Environ., 20:165-182.
  2. Myneni, R.B., Asrar, G., Wall, G.W., Kanemasu, E.T. and Impens, I., 1986. Canopy architecture, irradiance distribution on leaf surfaces and consequent photosynthetic efficiencies in heterogeneous plant canopies. Part II. Results and discussion. Agric. For. Meteorol., 37:205-218.
  3. Myneni, R.B., Asrar, G., Wall, G.W., Kanemasu, E.T. and Impens, I., 1986. Part I. Theoretical considerations. Agric. For. Meteorol., 37:189-204.

Myneni’s Publications in 1985 (01 – 03)

  1. Myneni, R.B. and Impens, I., 1985. A procedural approach for studying the radiation regime of infinite and truncated foliage spaces. III. Effect of leaf size and inclination distribution on non-parallel beam radiation penetration and canopy photosynthesis. Agric. For. Meteorol., 34:183-194.
  2. Myneni, R.B. and Impens, I., 1985. Part II. Experimental results and discussion. Agric. For. Meteorol., 34:3-16.
  3. Myneni, R.B. and Impens, I., 1985. Part I. Theoretical considerations. Agric. For. Meteorol., 33:323-337.