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Publications Citing BigFoot

The following 35 publications cited the BigFoot project.

YearCitationDataset or Project
2022Ma, H., C. Xiong, S. Liang, Z. Zhu, J. Song, Y. Zhang, and T. He. 2022. Determining the accuracy of the landsat-based land continuous Variable Estimator. Science of Remote Sensing. 5:100054. https://doi.org/10.1016/j.srs.2022.100054
2022Wagner, Y., F. Feng, D. Yakir, T. Klein, and U. Hochberg. 2022. In situ , direct observation of seasonal embolism dynamics in Aleppo pine trees growing on the dry edge of their distribution. New Phytologist. 235(4):1344-1350. https://doi.org/10.1111/nph.18208
2019Zhan, X., Z. Xiao, J. Jiang, and H. Shi. 2019. A Data Assimilation Method for Simultaneously Estimating the Multiscale Leaf Area Index From Time-Series Multi-Resolution Satellite Observations. IEEE Transactions on Geoscience and Remote Sensing. 57(11):9344-9361. https://doi.org/10.1109/TGRS.2019.2926392
2018Engstrom, R. and A. Hope. 2018. Parameter Sensitivity of the Arctic Biome-BGC Model for Estimating Evapotranspiration in the Arctic Coastal Plain. Arctic, Antarctic, and Alpine Research. 43(3):380-388. https://doi.org/10.1657/1938-4246-43.3.380
2018Kim, J.H., T. Hwang, Y. Yang, C.L. Schaaf, E. Boose, and J.W. Munger. 2018. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. Journal of Geophysical Research: Biogeosciences. 123(6):1960-1975. https://doi.org/10.1029/2018JG004438
2018Zhou, Z., S.V. Ollinger, and L. Lepine. 2018. Landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest. Oecologia. 188(2):595-606. https://doi.org/10.1007/s00442-018-4223-2
2017Kim, J., T. Hwang, C.L. Schaaf, D.A. Orwig, E. Boose, and J.W. Munger. 2017. Increased water yield due to the hemlock woolly adelgid infestation in New England. Geophysical Research Letters. 44(5):2327-2335. https://doi.org/10.1002/2016GL072327
2016Kang, Y., M. Ozdogan, S. Zipper, M. Roman, J. Walker, S. Hong, M. Marshall, V. Magliulo, J. Moreno, L. Alonso, A. Miyata, B. Kimball, and S. Loheide. 2016. How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sensing. 8(7):597. https://doi.org/10.3390/rs8070597
2016Tum, M., K. Gunther, M. Bottcher, F. Baret, M. Bittner, C. Brockmann, and M. Weiss. 2016. Global Gap-Free MERIS LAI Time Series (2002-2012). Remote Sensing. 8(1):69. https://doi.org/10.3390/rs8010069
2015Klein, T., C. Randin, and C. Korner. 2015. Water availability predicts forest canopy height at the global scale. Ecology Letters. 18(12):1311-1320. https://doi.org/10.1111/ele.12525
2014Klein, T., D. Yakir, N. Buchmann, and J.M. Grunzweig. 2014. Towards an advanced assessment of the hydrological vulnerability of forests to climate change-induced drought. New Phytologist. 201(3):712-716. https://doi.org/10.1111/nph.12548
2013Ishii, S., H. Sato, and T. Yamazaki. 2013. Geographical variability of relationships among black carbon from wildfires, climate and vegetation in Africa. Climate Research. 57(3):221-231. https://doi.org/10.3354/cr01175
2013Leonenko, G., S.O. Los, and P.R.J. North. 2013. Retrieval of leaf area index from MODIS surface reflectance by model inversion using different minimization criteria. Remote Sensing of Environment. 139:257-270. https://doi.org/10.1016/j.rse.2013.07.012
2013Leonenko, G., S.O. Los, and P.R.J. North. 2013. Retrieval of leaf area index from MODIS surface reflectance by model inversion using different minimization criteria. Remote Sensing of Environment. 139:257-270. https://doi.org/10.1016/j.rse.2013.07.012
2013Southworth, J., L. Rigg, C. Gibbes, P. Waylen, L. Zhu, S. McCarragher, and L. Cassidy. 2013. Integrating Dendrochronology, Climate and Satellite Remote Sensing to Better Understand Savanna Landscape Dynamics in the Okavango Delta, Botswana. Land. 2(4):637-655. https://doi.org/10.3390/land2040637
2012Chai, L., Y. Qu, L. Zhang, S. Liang, and J. Wang. 2012. Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs. International Journal of Remote Sensing. 33(18):5712-5731. https://doi.org/10.1080/01431161.2012.671553
2012Chai, L., Y. Qu, L. Zhang, S. Liang, and J. Wang. 2012. Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs. International Journal of Remote Sensing. 33(18):5712-5731. https://doi.org/10.1080/01431161.2012.671553
2012Kim, Y., J.S. Kimball, K. Zhang, and K.C. McDonald. 2012. Satellite detection of increasing Northern Hemisphere non-frozen seasons from 1979 to 2008: Implications for regional vegetation growth. Remote Sensing of Environment. 121:472-487. https://doi.org/10.1016/j.rse.2012.02.014
2012Knyazikhin, Y., M.A. Schull, P. Stenberg, M. Mõttus, M. Rautiainen, Y. Yang, A. Marshak, P. Latorre Carmona, R.K. Kaufmann, P. Lewis, M.I. Disney, V. Vanderbilt, A.B. Davis, F. Baret, S. Jacquemoud, A. Lyapustin, and R.B. Myneni. 2012. Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences. 110(3). https://doi.org/10.1073/pnas.1210196109
2012Li, A., Y. Bo, and L. Chen. 2012. Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI. International Journal of Remote Sensing. 34(1):227-246. https://doi.org/10.1080/01431161.2012.712234
2011Yuan, H., Y. Dai, Z. Xiao, D. Ji, and W. Shangguan. 2011. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sensing of Environment. 115(5):1171-1187. https://doi.org/10.1016/j.rse.2011.01.001
2010Pisek, J., J.M. Chen, K. Alikas, and F. Deng. 2010. Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America. Journal of Geophysical Research. 115(G3):. https://doi.org/10.1029/2009jg001138
2009Peckham, S.D., D.E. Ahl, and S.T. Gower. 2009. Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors. Remote Sensing of Environment. 113(6):1127-1132. https://doi.org/10.1016/j.rse.2009.02.008
2008Qu, Y., J. Wang, H. Wan, X. Li, and G. Zhou. 2008. A Bayesian network algorithm for retrieving the characterization of land surface vegetation. Remote Sensing of Environment. 112(3):613-622. https://doi.org/10.1016/j.rse.2007.03.031
2008Qu, Y., J. Wang, H. Wan, X. Li, and G. Zhou. 2008. A Bayesian network algorithm for retrieving the characterization of land surface vegetation. Remote Sensing of Environment. 112(3):613-622. https://doi.org/10.1016/j.rse.2007.03.031
2007Pisek, J. and J.M. Chen. 2007. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sensing of Environment. 109(1):81-94. https://doi.org/10.1016/j.rse.2006.12.004
2007Weiss, M., F. Baret, S. Garrigues, and R. Lacaze. 2007. LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sensing of Environment. 110(3):317-331. https://doi.org/10.1016/j.rse.2007.03.001
2006Salomon, J.G., C.B. Schaaf, A.H. Strahler, Feng Gao, and Yufang Jin. 2006. Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms. IEEE Transactions on Geoscience and Remote Sensing. 44(6):1555-1565. https://doi.org/10.1109/TGRS.2006.871564
2003Turner, D.P., W.D. Ritts, W.B. Cohen, S.T. Gower, M. Zhao, S.W. Running, S.C. Wofsy, S. Urbanski, A.L. Dunn, and J.W. Munger. 2003. Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sensing of Environment. 88(3):256-270. https://doi.org/10.1016/j.rse.2003.06.005
2018Zhou, P., J. Huang, and H. Hong. 2018. Modeling nutrient sources, transport and management strategies in a coastal watershed, Southeast China. Science of The Total Environment. 610-611:1298-1309. https://doi.org/10.1016/j.scitotenv.2017.08.113
2017Mu, X., R. Hu, Y. Zeng, T.R. McVicar, H. Ren, W. Song, Y. Wang, R. Casa, J. Qi, D. Xie, and G. Yan. 2017. Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology. 246:162-177. https://doi.org/10.1016/j.agrformet.2017.06.009
2016Zhu, G., X. Li, K. Zhang, Z. Ding, T. Han, J. Ma, C. Huang, J. He, and T. Ma. 2016. Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging. Hydrological Processes. 30(16):2861-2879. https://doi.org/10.1002/hyp.10832
2015Ji, L., G.B. Senay, and J.P. Verdin. 2015. Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products. Journal of Hydrometeorology. 16(6):2463-2480. https://doi.org/10.1175/JHM-D-14-0230.1
2008Verstraete, M.M., N. Gobron, O. Aussedat, M. Robustelli, B. Pinty, J.L. Widlowski, and M. Taberner. 2008. An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products. Advances in Space Research. 41(11):1773-1783. https://doi.org/10.1016/j.asr.2007.05.066
2003Turner, D.P., W.D. Ritts, W.B. Cohen, S.T. Gower, M. Zhao, S.W. Running, S.C. Wofsy, S. Urbanski, A.L. Dunn, and J.W. Munger. 2003. Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sensing of Environment. 88(3):256-270. https://doi.org/10.1016/j.rse.2003.06.005