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Planetary Greenness Research with MODIS, AVHRR and Other Sensors

  • Xu et al., 2013 Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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.

  • 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

  • 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, (2012)

  • 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, 2012

  • 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

  • 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

  • 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

  • Xu and Samanta et al., 2011. Widespread decline in greenness of Amazonian vegetation due to the 2010 drought, Geophys. Res. Lett., VOL. 38, L07402, doi:10.1029/2011GL046824, 2011.

  • 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.

  • 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

  • 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

  • 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

  • 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

  • Robinson et al., 2008. An empirical approach to retrieve monthly evapotranspiration over Amazonia, Int. J. Remote Sens., Vol. 29:7045–7063, 2008.

  • 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.

  • 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.

  • 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.

  • 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 et al., 2007. Large seasonal changes in leaf area of amazon rainforests. Proc. Natl. Acad. Sci., 104: 4820-4823, doi:10.1073/pnas.0611338104.

  • Huang et al., 2007. Canopy spectral invariants for remote sensing and model applications, Remote Sens. Environ., 106: 106–122.

  • 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.

  • 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.

  • Ahl et al., 2006. Monitoring Spring Canopy Phenology of a Deciduous Broadleaf Forest Using MODIS, Remote Sens. Environ., 104: 88–95.

  • 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.

  • 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.

  • Yang et al., 2006. MODIS Leaf Area Index Products: From Validation to Algorithm Improvement. IEEE Trans. Geosci. Remote Sens., 44: 1885-1898.

  • 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.

  • 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.

  • Zhang et al., 2006. Monitoring of the 2005 U.S. Corn-belt Yield using Satellite Data, Eos, s and A. Marshak [Eds], "Three-Dimensional Radiative Transfer in the Cloudy Atmosphere," Springer-Verlag, (book chapter to appear).

  • Potter et al., 2003. Satellite data help predict terrestrial carbon sinks. EOS, 84(46): pages 502 & 508.

  • 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

  • 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.

  • 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.

  • 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.

  • Tian et al., 2002. Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different resolutions. Remote Sens. Environ., 84:143-159.

  • 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.

  • Tian et al., 2002. Multiscale Analysis and Validation of the MODIS LAI Product. I. Uncertainty Assessment. Remote Sens. Environ., 83:414-430.

  • Tian et al., 2002. Multiscale Analysis and Validation of the MODIS LAI Product. II. Sampling Strategy. Remote Sens. Environ., 83:431-441.

  • Privette et al., 2002. Early spatial and temporal validation of MODIS LAI product in Africa. Remote Sens. Environ., 83: 232-243.

  • 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.

  • 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.

  • Panferov 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.

  • 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.

  • Privette et al., 1998. Global validation of EOS LAI and FPAR products. The Earth Observer, 10(6):39-42.

  • Knyazikhin 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.

  • Justice, et al., 1998. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Trans. Geosc. Remote Sens., 36:1228-1249.




    • Climate and Vegetation Research Group
    Dept. of Geography,Boston University. Mar-21-2013