Publications

The list below provides links to electronic versions of recent papers published by group members and collaborators. Clicking on any of the links will take you to webpages where each publication can be downloaded from the publisher.

2024

• Radeloff, V.C., Roy, D.P., Wulder, M.A., Anderson, M., Cook, B., Crawford, C.J., Friedl, M., Gao, F., Gorelick, G., Hansen, M., Healey, S., Hostert, P., Hulley, G.,  Huntington, J.L.,  Johnson, D.M, Neigh, C., Lyapustin, A., Lymburner, L., Pahlevan, N., Pekel, J.F., Scambos, T.A., Schaaf, C., Strobl, P., Woodcock, C.E, Zhang, H.K. and Z. Zhu (2024).  Need and vision for global medium-resolution Landsat and Sentinel-2 data products, Remote Sensing of Environment, 300 (2024) 113918, https://doi.org/10.1016/j.rse.2023.113918
• Roman, M.O., Justice, C., Paynter I., Boucher, P.B., Devadiga, S., Endsley, A., Erb, A., Friedl, M., Gao, H., Giglio, L., Gray, J.M., Hall, D., Hulley, G., Kimball, J., Knyazikhin, Y., Lyapustin, A., Myneni, R.B., Noojipady, P., Pu, J., Riggs, G., Sarkar, S., Schaaf, C., Shah, D., Tran, K.H., Vermote, E., Wang, D., Wang, Z., Wu, A., Ye, Y., Shen, Y., Zhang, S., Zhang, S., Zhang, X., Zhao, M., Davidson, C. and Robert Wolfe (2024). Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products. Remote Sensing of Environment, (302) 113963. https://doi.org/10.1016/j.rse.2023.113963

2023

• Stanimirova, R. , Tarrio, K., Turlej , K., Mcavoy, K., Stonebrook, S., Hu, KT. , Arévalo, P, Bullock, E.L., Zhang, Y.,Woodcock, C.E., Olofsson. P., Zhu, Z,  Barber, C.P., Souza Jr, C.M., Chen, S.,Wang, J.A., Mensah, F., Calderoń -Loor, M., Hadjikakou, M., Bryan, B.A., Graesser, J., Beyene, D.L.,,Mutasha, B., Siame, S., Siampale, A and M.A. Friedl. (2023). A global land cover training dataset from 1984 to 2020, Scientific Data, (2023) 10:879, https://doi.org/10.1038/s41597-023-02798-5
• Beamesderfer, E., Biraud, S.C., Brunsell, N.A, Friedl, M.A., Helbig, M., Hollinger, D.M., Milliman, T. Rahn, D.A., Scott, R.L. Stoy, P.C., Diehl, J.L. and A.D. Richardson (2023). The role of surface energy fluxes in determining mixing layer heights, Agricultural and Forest Meteorology, 342 (15), https://doi.org/10.1016/j.agrformet.2023.109687.
• Lee, L.X., Whitby, T.G., Munger, J.W., Stonebrook, S.J. and M.A. Friedl (2023). Remote sensing of seasonal variation of LAI and fAPAR in deciduous broadleaf forest, Agricultural and Forest Meteorology, 333, https://doi.org/10.1016/j.agrformet.2023.109389.
• Gao, X., McGregor, I.R., Gray, J.M. Friedl, M.A. and M. Moon (2023). Observations of satellite land surface phenology indicate that maximum greenness is more associated with global vegetation productivity than growing season length. Global Biogeochemical Cycles, 37, e2022GB007462. https://doi. org/10.1029/2022GB007462.

2022

• Stanimirova, R., Graesser, J., Olofsson, P. and M.A. Friedl (2022). Widespread changes in 21^st Century vegetation cover in Argentina, Paraguay, and Uraguay. Remote Sensing of Environment, 282, https://doi.org/10.1016/j.rse.2022.113277.
• Graesser, J., R. Stanimirova, K. Tarrio, E.J. Copati, J.N. Volante, S.R. Verón, S. Banchero, H. Elena, D. de Abelleyra and Mark A. Friedl (2022). Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America, Remote Sensing 14, no. 16: 4005. https://doi.org/10.3390/rs14164005
• Young, A.M., Friedl, M.A.,Novick,K., Scott, R.L., Moon, M., Frolking, S., Li, X., Carrillo, C.M. and  D. Richardson (2022). Disentangling the relative drivers of seasonal evapotranspiration across a continental-scale aridity gradient, Journal of Geophysical Research, Biogeosciences, https://doi.org/10.1029/2022JG006916
• Moon, M., Richardson, A.D., Milliman and A. Friedl (2022). A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites. Sci Data 9, 448. https://doi.org/10.1038/s41597-022-01570-5
• Li, X., Melaas, E., Carrillo, C.M., Ault, T., Richardson, A.D., Lawrence, P., Friedl, M.A., Seyednasrollah, B., Lawrence, D.M. and A.M. Young (2022). A Comparison of Land Surface Phenology in the Northern Hemisphere Derived from Satellite Remote Sensing and the Community Land Model. Journal of Hydrometeorology, 23, 859–873. https://doi.org/10.1175/JHM-D-21-0169.1
• Friedl M.A., Woodcock C.E., Olofsson P., Zhu Z., Loveland T., Stanimirova R., Arevalo P., Bullock E., Hu K.T., Zhang Y., Turlej K., Tarrio K., McAvoy K., Gorelick N., Wang J.A., Barber C.P., and C. Souza (2022). Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat, Frontiers in Remote Sensing, 3, https://doi.org/10.3389/frsen.2022.894571.
• Zhang Y., Woodcock, C.E., Arévalo P., Olofsson P., Tang X., Stanimirova R., Bullock E., Tarrio K.R., Zhu Z., Friedl M.A. (2022). A Global Analysis of the Spatial and Temporal Variability of Usable Landsat Observations at the Pixel Scale, Frontiers in Remote Sensing, 3, https://doi.org/10.3389/frsen.2022.894618.
• Moon, M., Richardson, A.D., O’Keefe, J. and A. Friedl (2022). Senescence in temperate broadleaf trees exhibits species-specific dependence on photoperiod versus thermal forcing, Agricultural and Forest Meteorology, 322 (2022), 109026, https://doi.org/10.1016/j.agrformet.2022.109026
• Graesser, J., Stanimirova, R. and M. A. Friedl (2022) “Reconstruction of satellite time series with a dynamic smoother,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 1803-1813, http://doi.org/10.1109/JSTARS.2022.3146081.
• Zhang, Y., Woodcock, C.E., Chen.S., Wang, J.A., Sulla-Menashe, D., Zuo, Z., Olofsson, P. Wang, Y and M.A. Friedl (2022). Mapping causal agents of disturbance in boreal and Arctic ecosystems of North America using time series of Landsat data, Remote Sensing of Environment, 272, https://doi.org/10.1016/j.rse.2022.112935.

2021

• Moon, M, Richardson, A.D., and M.A. Friedl (2021). Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery, Remote Sensing of Environment, 266 (2021) 112716, https://doi.org/10.1016/j.rse.2021.112716.
• Young, A.M., Friedl, M.A., Seyednasrollah, B., Beamesderfer, E., Carrillo, C.M., Li, X., Moon, M.,  Arain, M.A., Baldocchi, D.D., Blanken, P.D., Bohrer, G., Burns, S.P., Chu, H., Desai, A.R., Griffis, T.J., Hollinger, D.Y., Litvak, M.E., Novick, K., Scott, R.L., Suyker, A.E., Verfaillie, J., Wood, J.D. and A.D. Richardson (2021), Seasonality in aerodynamic resistance across a range of North American ecosystems, Agricultural and Forest Meteorology, Volume 310, https://doi.org/10.1016/j.agrformet.2021.108613.
• Wang, J.A., Baccini, A., Farina, M. R.,Randerson, J.T. and M.a. Friedl (2021). Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Chang. https://doi.org/10.1038/s41558-021-01027-4.
• Moon, M., Seyednasrollah, B., Richardson, A.D. and M.A. Friedl (2021). Using time series of MODIS land surface phenology to model temperature and photoperiod controls on spring greenup in North American deciduous forests, Remote Sensing of Environment, 260 (2021), https://doi.org/10.1016/j.rse.2021.112466
• Hu, Q., Yin, H., Friedl, M.A., You, L., Li, Z., Tang, H. and W. Wu (2021). Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2021.112365.

2020

• Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, R.J., Fishgold, L., Friedl, M.A., Jain, M., Kohli, D., Bayas, J.C.L., Lunga, D., McCarty, J.L., Pontius, R.G., Reinman, A.B., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z.F. and L. Estes (2020), Accounting for training data errors in machine learning applied to Earth observations, Remote Sensing, 12(6), 1034: https://doi.org/10.3390/rs12061034
• Seyednasrollah, B., Young, A.M., Li, X., Milliman, T., Ault, T., Frolking, S., Friedl, M., and A.D. Richardson, Sensitivity of deciduous forest phenology to environmental drivers for climate change impacts across North America, Geophysical Research Letters, 47, e2019GL086788. https://doi.org/10.1029/2019GL086788.
• Bolton, D, K., Gray, J.M, Melaas, E.K., Moon, M., Eklundh, L. and M.A. Friedl (2020). Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sensing of Environment, 240, https://doi.org/10.1016/j.rse.2020.111685.
• Moon, M., Li, D., Liao, W., Rigden, A.J. andA. Friedl (2020). Modification of Surface Energy Balance During Springtime: The Relative Importance of Biophysical and Meteorological Changes, Agricultural and Forest Meteorology, 284, https://doi.org/10.1016/j.agrformet.2020.107905

2019

• Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A., and Schaaf, C.B., 2018. Generation and evaluation of the VIIRS land surface phenology product. Remote Sensing of Environment, 216, pp. 212–229.
• Zhang, X., Jayavelu, S., Liu, L., Friedl, M.A., Henebry, G.M., Liu, Y., Schaaf, C.B., Richardson, A.D. and Gray, J., 2018. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. Agricultural and Forest Meteorology, 256, pp.137-149.
• Sulla-Menashe, D., Woodcock, C.E. and Friedl, M.A., 2018. Canadian boreal forest greening and browning trends: An analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers. Environmental Research Letters, 13(1), p.014007.
• Hufkens, K., Basler, D., Milliman, T., Melaas, E.K. and Richardson, A.D., 2018. An integrated phenology modelling framework in R. Methods in Ecology and Evolution, 9(5), pp.1276-1285.
• Jönsson, P., Cai, Z., Melaas, E., Friedl, M.A. and Eklundh, L., 2018. A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data. Remote Sensing, 10(4), p.635.
• Melaas, E.K., Sulla‐Menashe, D. and Friedl, M.A., 2018. Multidecadal changes and interannual variation in springtime phenology of North American temperate and boreal deciduous forests. Geophysical Research Letters, 45(6), pp.2679-2687
• Richardson, A.D., Hufkens, K., Milliman, T., Aubrecht, D.M., Chen, M., Gray, J.M., Johnston, M.R., Keenan, T.F., Klosterman, S.T., Kosmala, M. and Melaas, E.K., 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data, 5, p.180028.
• Klosterman, S., Melaas, E., Wang, J., Martinez, A., Frederick, S., O’Keefe, J., Orwig, D.A., Wang, Z., Sun, Q., Schaaf, C. and Friedl, M., 2018. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agricultural and Forest Meteorology, 248, pp.397-407.
• Gallinat, A.S., Russo, L., Melaas, E.K., Willis, C.G. and Primack, R.B., 2018. Herbarium specimens show patterns of fruiting phenology in native and invasive plant species across New England. American Journal of Botany, 105(1), pp.31-41.

2017

• Hardiman, B.S., J.A. Wang, L.R. Hutyra, C.K. Gately, J.M. Getson, and M.A. Friedl, 2017. Accounting for urban biogenic fluxes in regional carbon budgets, Science of the Total Environment, vol 592, pp. 366-373. DOI: 10.1016/j.scitotenv.2017.03.028
• Wang, J. A., Hutyra, L. R., Li, D., and M.A. Friedl, 2017. Gradients of atmospheric temperature and humidity controlled by local urban land use intensity in Boston. Journal of Applied Meteorology and Climatology, vol 56, pp. 817-831.
• Zhang, X, Wang, J., Gao F., Liu, Y., Schaaf, C., Friedl, M., Yu, Y. Jayavelu, S., Gray, J., Liu, L., Yan, D., and G. M. Henebry(2017).  Exploration of scaling effects on coarse resolution land surface phenology, Remote Sensing of Environment, 190(1), pp. 318-330.

2016

• Melaas, E.K., Sulla-Menashe, D., Gray, J.M., Black, A., Morin, T.H., Richardson A.D. and M.A. Friedl 2016. Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat, Remote Sensing of Environment, 186(1), pp. 452-464.
• Melaas, E.K., Wang, J.A., Miller, D.L. and M.A. Friedl, 2016. Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan regions, Environmental Research Letters, 11(5), DOI: 10.1088/1748-9326/11/5/054020.
• Sulla-Menashe, D, Friedl, M.A. and C.E. Woodcock, 2016. Sources of bias and variability in long-term Landsat time series of Canadian boreal forests, Remote Sensing of Environment, 177, pp. 206-219.
• Abercrombie, S.P. and M.A. Friedl, 2016. Improving the consistency of multitemporal land cover maps using a hidden Markov model, IEEE Transactions on Geoscience and Remote Sensing, 54(2), pp. 703-713.
• Melaas, E.K., M.A. Friedl and A.D. Richardson, 2016. Multiscale modeling of spring phenology across deciduous forests in the Eastern United States, Global Change Biology, 22(2), pp. 792-805.
• Huang, X., A. Schneider and Friedl, M.A. 2016. Mapping sub-pixel urban expansion in China using MODIS and DMSP nighttime lights, Remote Sensing of Environment, 175, pp. 92-108.
• Verma, M., M.A. Friedl, A. Finzi and N. Phillips, 2016. Multi-criteria evaluation of the suitability of growth functions for modeling remotely sensed phenology, Ecological Modeling, 323, pp. 123-132.

2015

• Verma, M., M.A. Friedl, B.E. Law, D. Bonal, G. Kiely, T.A. Black, G. Wohlfahrt, E.J. Moors, L. Montagnani, B. Marcolla, P. Toscano, a. Varlagin, O. Roupsard, A. Cecatti, M.A. Arain, P. D’Odorico, 2015. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data, Agricultural and Forest Meteorology, 214-215, pp. 416-429.
• Martellozzo, F, N. Ramankutty, R.J. Hall, D.T. Price, B. Purdy and M.A. Friedl, 2014.  Urbanization and the loss of prime farmland: a case study in the Calgary-Edmonton corridor of Alberta, Regional Environmental Change, DOI 10.1007/s10113-014-0658-0
• Salmon, J.M. M.A. Friedl, S. Frolking, D. Wisser and E. M. Douglas, 2015.  Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories, and climate data.  International Journal of Applied Earth Observation and Geoinformation, 38, pp. 321-334; doi:10.1016/j.jag.2015.01.014.
• Michael Toomey, Mark A. Friedl, Steve Frolking, Koen Hufkens, Stephen Klosterman, Oliver Sonnentag, Dennis D. Baldocchi, Carl J. Bernacchi, Gil Bohrer, Edward Brzostek, Sean P. Burns, Carole Coursolle, David Y. Hollinger, Hank A. Margolis, Harry McCaughey, Russell K. Monson, J. William Munger, Stephen Pallardy, Richard P. Phillips, Margaret Torn, Sonia Wharton, Marcelo Zeri, Andrew D. Richardson, 2015. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis, Ecological Applications, 25(1), pp.99-115.

2014

• Gray, J.M, S. Frolking, E.A. Kort, D.K. Ray, C.J. Kucharik, N. Ramankutty^, & M.A. Friedl 2014, Direct human influence on atmospheric CO₂ seasonality from increased cropland productivity, Nature, 515, pp 398-401, doi:10.1038/nature13957.
• Gray, J.M., M.A. Friedl, S. Frolking, N. Ramankutty, A.Nelson and M. Gumma, 2014. Mapping Asian Cropping Intensity with MODIS.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), pp. 3373-3379. DOI:10.1109/JSTARS.2014.2344630
  
• T.F. Keenan, B. Darby, E. Felts, O. Sonnentag, M. Friedl, K. Hufkens, J. O’Keefe, S. Klosterman, J.W. Munger, M. Toomey, A.D. Richardson, 2014. Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment, Ecological Applications, 24:1478–1489. DOI: 10.1890/13-0652.
• Glanz, H., L. Carvalho, D. Sulla-Menashe and M.A. Friedl, 2014. A parametric model for classifying land cover and evaluating training data based on mulit-temporal remote sensing data, ISPRS Journal of Photogrammetry and Remote Sensing, 97, pp. 219-228; doi: 0.1016/j.isprsjprs.2014.09.004
• Sulla-Menashe, D., R. Kennedy, Z. Yang, J. Braaten, O.N. Krankina and M.A. Friedl 2013, Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation, Remote Sensing of Environment, 151, pp 114-123; DOI: 10.1016/j.rse.2013.07.042
• Klosterman, S.T., K. Hufkens, J.M. Gray, E. Melaas, O. Sonnetag, I. Lavine, L. Mitchell, R. Norman, M.A. Friedl, and A.D. Richardson, 2014.  Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences, 11, 4305-4320. Doi: 10.5194/bg-11-4305-2014.
• Friedl, M.A., J.M. Gray, E.K. Melaas, A.D. Richardson, K. Hufkens, T.F. Keenan, A. Bailey and J. O’Keefe. 2014. A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research. Letters. 9 054006 doi:10.1088/1748-9326/9/5/054006
• Verma,M., M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, B. Gielen, O. Roupsard, E. J. Moors, P. Toscano, F. P. Vaccari, D. Gianelle, G. Bohrer, A. Varlagin, N. Buchmann, E. van Gorsel, L. Montagnani, and P. Propastin, 2014. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set, Biogeosciences, 11, 2185-2200.
• Li, L.; Friedl, M.A.; Xin, Q.; Gray, J.; Pan, Y.; Frolking, S, 2014. Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sensing, 6, 2473-2493.
• Huang, X and M.A. Friedl, 2014.  Distance metric-based forest cover change detection using MODIS time series, International Journal of Applied Remote Sensing and Geoinformation, 29:78-92
• Keenan, T.F., J. Gray, M.A. Friedl, M. Toomey, G. Bohrer, D. Y. Hollinger, J.W. Munger, J.O’Keefe, H.P. Schmid, I. Sue Wing, B. Yang and A.D. Richardson, 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nature Climate Change, doi:10.1038/nclimate2253
• Cai, S. Liu, D. Sulla-Menashe, D and M.A. Friedl 2014.  Enhancing MODIS land cover product with a spatial-temporal modeling algorithm.  Remote Sensing of Environment, 147, pp. 243-255.

2013

• Justice, C.O., M.O, Roman, I. Csiszar, E.F. Vermote, R.E. Wolfe, S.J. Hook, M. Friedl, Z.S. Wang, C.B. Schaaf, T. Miura, M. Tschudi, G. Riggs, D.K. Hall, A. Lyapustin,S. Sadashiva, C. Davidson, E.J. Masuoka, 2013 Land and cryosphere products from Suomi NPP VIIRS: Overview and Status, Journal of Geophysical Research-Atmospheres, 118(17), pp. 9753-9765, DOI: 10.1002/jgrd.50771.
• Frolking, S., T. Milliman, K. Seto and M.A. Friedl (2013).  A global fingerprint of macro-scale changes in urban structure from 1999-2009, Environmental Research Letters, (8) 2013, 10 pp.
• Bolton, D.K. and M.A. Friedl (2013).  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics, Agricultural and Forest Meteorology, 173, 74-84.
• Melaas, E.K., M.A. Friedl and Z. Zhe (2013).  Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETN+ data, Remote Sensing of Environment, 132, 176-185.
• Melaas, E.K., A.D. Richardson, M.A. Friedl, D. Dragoni, C.M. Gough, M. Herbst, L. Montagnani, and E. Moors (2013).  Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems, Agricultural and Forest Meteorology, 171-172, 46-56.

2012

• Chong, L., Ray, S., Hooker, G, and M.A. Friedl (2012). Functional factor analysis for periodic remote sensing data. The Annals of Applied Statistics, 6(2), 610-624.
• Hufkens, K., Friedl, M.A., Keenan, T.F., Sonnentag, O., Bailey, A., O’Keefe, J. and A.D. Richardson (2012). Ecological impacts of a widespread frost event following early spring leaf-out. Global Change Biology, 18(7), 2365-2367.
• Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S. and R.A. Houghton (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2(3), 182-185.
• Olofsson, P., Stehman, S.V., Woodcock, C.E., Sulla-Menashe, D., Sibley, A.M., Newell, J.D., Friedl, M.A. and M. Herold (2012). A global land-cover validation data set, part I: Fundamental design principles. International Journal of Remote Sensing, 33(18), 5768-5788.
• Stehman, S.V., Olofsson, P., Woodcock, C.E., Herold, M. and M.A. Friedl (2012). A global land cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class, in press, International Journal of Remote Sensing, 33(22), 6975-6993.
• Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A.M., Friedl, M.A., Braswell, B.H., Milliman, T., O’Keefe, J. and A.D. Richardson (2012). Digital repeat photography for phenological research in forest ecosystems, Agricultural and Forest Meteorology, 152, 159-177.
• Avitable, V., Baccini, A., Friedl, M.A. and C. Schmullius (2012). Capabilities and limitations of Landsat and land cover data for aboveground biomass estimation in Uganda, Remote Sensing of Environment, 117(15), 366-380.
• Hufkens, K, Friedl, M.A., Sonnentag, O., Braswell, B.H., Millman, T. and A.D. Richardson (2012). Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology, Remote Sensing of Environment, 117(15), 366-380.

2011

• van der Molen, M.K., Dolman, A.J., Ciais, P., Eglin, T., Gobron, N., Law, B.E., Meir, P., Peters, W., Phillips, O.L., Reichstein, M., Chen, T., Dekker, S.C., Doubbková, M., Friedl, M.A., Jung, M., van den Hurk, B.J.J.M., de Jeu, R.A.M., Kruijt, B., Ohta, T., Rebel, K.T., Plummer, S., Seneviratne, S.I., Sitch, S., Teuling, A.J., van der Werf, G.R. and G. Wang (2011). Drought and ecosystem carbon cycling, Agricultural and Forest Meteorology, 151(7), 765-773.
• Pflugmacher, D., Krankina, O.N., Cohen, W.B., Friedl, M.A., Sulla-Menashe, D., Kennedy, R.E., Nelson, P., Loboda, T.V., Kuemmerle, T., Dyukarev, E., Elsakov, V. and V.I. Kharuk (2011). Comparison and assessment of coarse resolution land cover maps for Northern Eurasia. Remote Sensing of Environment, 115(12), 3539-3553.
• Sulla-Menashe, D., Friedl, M.A., Krankina, O.N., Baccini, A., Woodcock, C.E., Sibley, A., Sun, G.Q., Kharuk, V. and V. Elsakov (2011). Hierarchical mapping of Northern Eurasian land cover using MODIS data. Remote Sensing of Environment, 115(2), 392-403.

2010

• Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N., Hollinger, D.Y., Kutsch, W.L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Munger, J.W., Moors, E., Piao, S.L., Rebmann, C., Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R. and A. Varlagin (2010). Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B-Biological Sciences, 365(1555), 3227-3246.
• Schneider, A., Friedl, M.A. and D. Potere (2010). Mapping global urban areas using MODIS 500-m data: New methods and datasets based on ‘urban ecoregions’. Remote Sensing of Environment, 114(8), 1733-1746.
• Ganguly, S., Friedl, M.A., Tan, B., Zhang, X.Y. and M. Verma (2010). Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment, 114(8), 1805-1816.
• Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. and X.M. Huang (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168-182.
• Zhang, X., Goldberg, M., Tarpley, D., Friedl, M.A., Morisette, J., Kogan, F. and Y. Yu (2010). Drought-induced vegetation stress in southwestern North America, Environmental Research Letters, 5(2), 024008.

2009

• Schneider, A., Friedl, M.A. and D. Potere (2009). A new map of global urban extent from MODIS satellite data. Environmental Research Letters, 4(4), 044003.
• Zhang, X.Y., Friedl, M.A. and C.B. Schaaf 2009. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data, International Journal of Remote Sensing, 30(8), 2061-2074.

2008

• Ordoyne, C. and M.A. Friedl (2008). Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades. Remote Sensing of Environment, 112(11), 4107-4119.
• Krankina, O.N., Pflugmacher D., Friedl, M.A., Cohen, W.B., Nelson, P., and A. Baccinni (2008). Meeting the challenge of mapping peatlands with remotely sensed data. Biogeosciences, 5(6), 1809-1820.

Other significant papers:

• Santanello, J.A., Friedl, M.A. and M.B. Ek (2007). Convective planetary boundary layer interactions with the land surface at diurnal time scales: Diagnostics and feedbacks. Journal of Hydrometeorology, 8(5), 1082-1097.
• Baccini, A., Friedl, M.A., Woodcock, C.E. and Z. Zhu. (2007). Scaling field data to calibrate and validate moderate spatial resolution remote sensing models, Photogrammetric Engineering and Remote Sensing 73(8), 945-954.
• Zhang X., Friedl, M.A. and C.B. Schaaf (2006). Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research, 111, G04017.
• Zhang X., Friedl, M.A., Schaaf, C.B. and A.H. Strahler (2005). Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research, 110(D12), D12103.
• Lotsch A., Friedl M.A., Anderson B.T. and C.J. Tucker (2005). Response of terrestrial ecosystems to recent Northern Hemispheric drought, Geophysical Research Letters, 32(6), L06705.
• Santanello, J.A., Friedl, M.A. and W.P. Kustas (2005). An empirical investigation of convective planetary layer evolution and its relationship with the land surface, Journal of Applied Meteorology, 44, 917-932.
• Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H. and A. Schneider (2004). The footprint of urban climates on vegetation phenology. Geophysical Research Letters, 31, L12209.
• Baccini, A., Friedl, M.A., Woodcock, C.E. and R. Warbington (2004). Forest biomass estimation over regional scales using multisource data. Geophysical Research Letters, 31, L10501.
• Zhang, X., Friedl, M.A., Schaaf, C.B. and A.H. Strahler (2004). Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10, 1133-1145.
• Schneider, A., Friedl, M.A., McIver, D.K. and C.E. Woodcock (2003). Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69(12), 1377-1386.
• Lotsch, A., Tian, Y., Friedl, M.A. and R.B. Myneni (2003). Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors. International Journal of Remote Sensing, 24(10), 1997-2016.
• Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F. and F. Gao (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84, 471-575.
• Santanello, J.A. and M.A. Friedl (2003). Diurnal covariation in soil heat flux and net radiation, Journal of Applied Meteorology, 42, 851-862.

• Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler, A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F. and C. Schaaf (2002). Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1-2), 287-302.
• Mciver, D.K. and M.A. Friedl (2002). Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sensing of Environment, 81, 253-261.
• McIver, D.K. and M.A. Friedl (2001). Estimating pixel-scale land cover classification confidence using non-parametric machine learning methods. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1959-1968.
• Friedl, M.A., Woodcock, C., Gopal, S., Muchoney, D., Strahler, A.H., and C. Barker-Schaaf (2000). A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data. International Journal of Remote Sensing, 21, 1073-1077.
• Friedl, M.A., Brodley, C.E. and A.H. Strahler (1999). Maximizing land cover classification accuracies at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing, 37, 969-977.
• Friedl, M.A. and C.E. Brodley (1997). Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, 399-409.
• M.A. Friedl (1996). Relationships among remotely sensed data, surface energy balance, and area-averaged fluxes over partially vegetated land surfaces, Journal of Applied Meteorology, 35(11), 2091-2103.
• M.A. Friedl (1995). Modeling land surface fluxes using a sparse canopy model and radiometric surface temperature measurements, Journal of Geophysical Research, 100(D12), 25,435-25,446.
• Friedl, M.A., Davis, F.W., Michaelsen, J. and M.A. Moritz (1995). Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE, Remote Sensing of Environment, 54, 233-246.
• Michaelsen, J.C., Schimel, D.S., Friedl, M.A., Davis, F.W. and R.C. Dubayah (1994). Regression tree analysis of satellite and terrain data to guide vegetation sampling surveys, Journal of Vegetation Science, 5, 673-686.
• Friedl, M.A. and F.W. Davis (1994). Sources of variation in radiometric surface temperature over a tallgrass prairie, Remote Sensing of Environment, 48, 1-17.
• Friedl, M.A., Michaelsen, J., Davis, F.W., Walker, H. and D.S. Schimel (1994). Estimating grassland biomass and leaf area index using ground and satellite data, International Journal of Remote Sensing, 15(7), 1401-1420.