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  • About Us
    • Department Overview
    • People
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  • Undergraduate
    • Prospective Students
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    • Courses and Facilities
    • Special Programs
    • Graduation
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  • Graduate
    • Prospective Ph.D. Students
    • Master of Science and Graduate Certificate Programs
    • About Our Ph.D. Students
    • Resources
  • Research
    • Geospatial-Information Science and Remote Sensing
    • Human Dimensions of Global Change - Coupled Human and Natural Systems
    • Land Cover - Land Use Change
    • Carbon, Vegetation Dynamics and Landscape-Scale Processes
  • GIS
    • Center for Geospatial Information Science
    • GIS Undergraduate
    • GIS Summer/Winter Workshops
  • Centers
    • Geographical Sciences Centers
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    • Faculty: A Historic Look
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Publications related to Geospatial-Information Science and Remote Sensing

Fjallsárlón, SE Iceland - May 2018
  • Kerner, H. R., Sahajpal, R., Pai, D. B., Skakun, S., Puricelli, E., Hosseini, M., ... & Becker-Reshef, I. (2022). Phenological normalization can improve in-season classification of maize and soybean: A case study in the central US Corn Belt.Science of Remote Sensing, 6, art. num. 100059.
  • Prudente, V.H.R., Skakun, S., Oldoni, L.V., Xaud, H.A., Xaud, M.R., Adami, M., & Sanches, I.D.A. (2022). Multisensor approach to land use and land cover mapping in Brazilian Amazon. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 95–109.
  • Yao Li, Kathleen Stewart, Kay Thwe Han, Zay Yar Han, Poe P Aung, Zaw W Thein, Thura Htay, Dong Chen, Myaing M Nyunt, Christopher V Plowe, Understanding spatio-temporal human mobility patterns for malaria control using a multi-agent mobility simulation model, Clinical Infectious Diseases, 2022;, ciac568, https://doi.org/10.1093/cid/ciac568
  • Franch, B., Cintas, J., Becker-Reshef, I., Sanchez-Torres, M. J., Roger, J., Skakun, S., Sobrino, J. A., Van Tricht, K., Degerickx, J., Gilliams, S., Koetz, B., Szantoi, Z., & Whitcraft, A. (2022).Global crop calendars of maize and wheat in the framework of the WorldCereal project. GIScience & Remote Sensing, 59(1), 885–913.
  • Gong, W., Huang, C., Houghton, R.A., Nassikas, A., Zhao, F., Tao, X., Lu, J., & Schleeweis, K. (2022). Carbon fluxes from contemporary forest disturbances in North Carolina evaluated using a grid-based carbon accounting model and fine resolution remote sensing products. Science of Remote Sensing, Vol. 5. DOI: 10.1016/j.srs.2022.100042
  • Jia, A., Liang, S., & Wang, D. (2022). Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data. Remote Sensing of Environment. 278. 113105. https://doi.org/10.1016/j.rse.2022.113105
  • Skakun, S., Wevers, J., Brockmann, C., Doxani, G., Aleksandrov, M., Batič, M., Frantz, D., Gascon, F., Gómez-Chova, L., Hagolle, O., López-Puigdollers, D., Louis, J., Lubej, M., Mateo-García, G., Osman, J., Peressutti, D., Pflug, B., Puc, J., Richter, Roger, J.-C., Scaramuzza, P., Vermote, E., Vesel, N., Zupanc, A., Žust, L. (2022). Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2. Remote Sensing of Environment, 274, art. num. 112990.
  • Tian, Z., X. Lyu, H. Zou, H. Yang, L. Sun3, M. S. Pinya, Q. Chao, A. Feng, B. Smith. 2022. Advancing index-based climate risk assessment to facilitate adaptation planning: Application in Shanghai and Shenzhen, China. Advances in Climate Change Research (2020 IF: 4.13). Vol. 13, Issue 3, pp. 432-442. https://doi.org/10.1016/j.accre.2022.02.003. Advancing index-based climate risk assessment to facilitate adaptation planning_AdvanceClimateChangeResearch2022.pdf1.29 MB
  • Zhang, Y., Skakun, S., Adegbenro, M.O., & Ying, Q. (2022). Leveraging the use of labeled benchmark datasets for urban area change mapping and area estimation: a case study of the Washington DC–Baltimore region. International Journal of Digital Earth.https://doi.org/10.1080/17538947.2022.2094001
  • Nakalembe, C., Zubkova, M., Hall, J. V., Argueta, F., & Giglio, L. (2022). Impacts of large-scale refugee resettlement on LCLUC: Bidi Bidi refugee settlement, Uganda case study. Environmental Research Letters, 17(6), 064019. Nakalembe_2022_Environ._Res._Lett._17_064019.pdf9.23 MB
  • Vadrevu, K.P., Le Toan, T., Ray, S.S., and Justice, C. 2022. Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries. Springer Book. https://doi.org/10.1007/978-3-030-92365-5
  • Comment on Otón et al. Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)
  • Huang, X., Fu, Y., Wang, J., Dong, J., Zheng, Y., Pan, B., Skakun, S., & Yuan, W. (2022). High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016–2020. Remote Sensing, 14(9), art. num. 2120.
  • Dubayah, et al. (2021) GEDI L4A Footprint Level Aboveground Biomass Density, V2. ORNL DAAC, Oak Ridge, TNhttps://doi.org/10.3334/ORNLDAAC/1986
  • Duncanson et al. (2022). Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270, 112845. 1-s2.0-S0034425721005654-main (1).pdf3.63 MB
  • Jia, A., Wang, D., ..., & Yu, Y. (2022). Global daily actual and snow-free blue-sky land surface albedo climatology from 20-year MODIS products. Journal of Geophysical Research: Atmospheres, 127, e2021JD035987. https://doi. org/10.1029/2021JD035987
  • Kussul, Nataliia, Klaus Deininger, Leonid Shumilo, Mykola Lavreniuk, Daniel Ayalew Ali, and Oleg Nivievskyi. "Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields." Sustainability 14, no. 7 (2022): 3965.
  • Roger, J.-C., Vermote, E., Skakun, S., Murphy, E., Dubovik, O., Kalecinski, N., Korgo, B., & Holben, B. (2022). Aerosol models from the AERONET database: application to surface reflectance validation. Atmospheric Measurement Techniques, 15, 1123–1144.
  • Alkama, R., Forzieri, G., Duveiller, G., Grassi, G., Liang, S., & Cescatti, A. (2022). Vegetation-based climate mitigation in a warmer and greener World. Nature Communications, 13:606, doi:610.1038/s41467-41022-28305-41469
  • Cao, Y., S. Liang, L. Sun, J. Liu, X. Cheng, D. Wang, Y. Chen, M. Yu, K. Feng. 2022. Trans-Arctic shipping routes expanding faster than the model projections. Global Environmental Change (2020 IF: 9.523). 73, article no. 102488. https://doi.org/10.1016/j.gloenvcha.2022.102488
  • Ding, A., S. Liang, H. Ma, T. He, (2022), Extension of the Hapke model to the spectral domain to characterize soil physical properties, Remote Sensing of Environment, 269, 112843
  • Guo, T., He, T., Liang, S., Roujean, J.-L., Zhou, Y., & Huang, X. (2022). Multi-decadal analysis of high-resolution albedo changes induced by urbanization over contrasted Chinese cities based on Landsat data. Remote Sensing of Environment, 269, 112832
  • Humber, M., Zubkova, M. and Giglio, L., 2022. A remote sensing-based approach to estimating the fire spread rate parameter for individual burn patch extraction. International Journal of Remote Sensing, 43(2), pp.649-673. IJRS_FinalPub.pdf9.74 MB
  • Ma, H., Liang, S., Zhu, Z., & He, T. (2022). Developing a Land continuous Variable Estimator to generate daily land products from Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. doi:10.1109/TGRS.2021.3121272
  • Zhan, C., & Liang, S. (2022). Improved estimation of the global top-of-atmosphere albedo from AVHRR data. Remote Sensing of Environment, 269, 112836

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