Publications related to Geospatial-Information Science and Remote Sensing | Geographical Sciences Department

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
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
Gutman, G., Skakun, S., Gitelson, A. (2021). Revisiting the Use of Red and Near-Infrared Reflectances in Vegetation Studies and Numerical Climate Models. Science of Remote Sensing, 4, art. num. 100025
Letter to the editor on “Nonlinear dynamics of fires in Africa over recent decades controlled by precipitation”
B. Franch, E. Vermote, S. Skakun, A. Santamaria-Artigas, N. Kalecinski, J.-C. Roger, I. Becker-Reshef, B. Barker, C. Justice, J.A. Sobrino, The ARYA crop yield forecasting algorithm: Application to the main wheat exporting countries.International Journal of Applied Earth Observation and Geoinformation, Volume 104, 2021, 102552. https://doi.org/10.1016/j.jag.2021.102552 1-s2.0-S0303243421002592-main.pdf19.27 MB
Li, B., Liang, S., Liu, X., Ma, H., Chen, Y., Liang, T., & He, T. (2021). Estimation of all-sky 1 km land surface temperature over the conterminous United States. Remote Sensing of Environment, 266, 112707.
Resop, J.P.; Lehmann, L.; Hession, W.C. Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning. Drones 2021, 5, 91.https://doi.org/10.3390/drones5030091 resop2021_drones.pdf8.37 MB
Chen, Y., Liang, S., Ma, H., Li, B., He, T., & Wang, Q. (2021). An all-sky 1 km daily surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data. Earth Syst. Sci. Data, 13, 4241-4261
Hall, J.V., Argueta, F., & Giglio, L., (2021). Validation of MCD64A1 and FireCCI51 cropland burned area mapping in Ukraine. International Journal of Applied Earth Observation and Geoinformation, 102. 1-s2.0-S0303243421001501-main.pdf7.32 MB
Ranjbar S., Akhoondzadeh M., Brisco B., Amani M., Hosseini M., 2021, Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.110 JSTAR 2021.pdf9.46 MB
Vadrevu, K.P., Ohara, T. and Justice, C. eds., 2021. Biomass Burning in South and Southeast Asia: Impacts on the Biosphere, Volume Two. CRC Press. https://www.routledge.com/Biomass-Burning-in-South-and-Southeast-Asia-Impacts-on-the-Biosphere-Volume/Vadrevu-Ohara-Justice/p/book/9780367076047
Vadrevu, K.P., Ohara, T. and Justice, C. eds., 2021. Biomass Burning in South and Southeast Asia: Mapping and Monitoring, Volume One. CRC Press. https://www.routledge.com/Biomass-Burning-in-South-and-Southeast-Asia-Mapping-and-Monitoring-Volume/Vadrevu-Ohara-Justice/p/book/9780367076818
Zubkova, M.; Giglio, L.; Humber, M.; Hall, J.H.; Ellicott, E. Conflict and Climate: Drivers of Fire Activity in Syria in the Twenty-First Century. 2021. Earth Interactions, 25(1): 119-135 [10873562 - Earth Interactions] Conflict and Climate_ Drivers of Fire Activity in Syria in the Twenty-First Century (1)_0.pdf2.15 MB
Jia, A., Ma, H., Liang, S., & Wang, D. (2021). Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method. Remote Sensing of Environment, 263, 112566.
Santamaria-Artigas, A., Vermote, E.F., Franch, B., Roger, J.-C., Skakun, S., 2021. Evaluation of the AVHRR surface reflectance long term data record between 1984 and 2011. Int. J. Appl. Earth Obs. Geoinf. 98, 102317. https://doi.org/10.1016/j.jag.2021.102 asantam_JAG_AVHRR.pdf15.57 MB
Hall, J.V., Zibstev, S.V., Giglio, L., Skakun, S., Myroniuk, V., Zhuravel, O., Goldammer, J.G. and Kussul, N., 2021. Environmental and political implications of underestimated cropland burning in Ukraine. Environmental Research Letters. Hall_2021_Environ._Res._Lett._16_064019.pdf1.88 MB
Hosseini, M., McNairn, H., Mitchell, S., Robertson, L. D., Davidson, A., Ahmadian, N., Bhattacharya, A., et al. (2021). A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing, 13(7), 1348. MDP remotesensing-13-01348-v2.pdf0 bytes
Nakalembe, C., Becker-Reshef, I., Bonifacio, R., Hu, G., Humber, M. L., Justice, C. J., … Sanchez, A. (2021). A review of satellite-based global agricultural monitoring systems available for Africa. Global Food Security, 29, 100543. https://doi.org/10.101 1-s2.0-S2211912421000523-main.pdf2.7 MB
Gitelson, A., Arkebauer, T., Viña, A., Skakun, S., & Inoue Y. (2021). Evaluating plant photosynthetic traits via absorption coefficient in the photosynthetically active radiation region. Remote Sensing of Environment, 258, art. num. 112401.
Skakun, S., Vermote, E. F., Artigas, A. E. S., Rountree, W. H., & Roger, J. C. (2021). An experimental sky-image-derived cloud validation dataset for Sentinel-2 and Landsat 8 satellites over NASA GSFC.International Journal of Applied Earth Observation and Geoinformation, 95, 102253.
Huang, X., Reba, M., Coffin, A., Runkle, B. R., Huang, Y., Chapman, B., Ziniti, B., Skakun, S., Kraatz, S., Siqueira, P., & Torbick, N. (2021). Cropland mapping with L-band UAVSAR and development of NISAR products. Remote Sensing of Environment, 253, art. num. 112180.
Skakun, S., Kalecinski, N.I., Brown, M.G.L., Johnson, D.M., Vermote, E.F., Roger, J.-C., & Franch, B. (2021). Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery.Remote Sensing, 13, art. num. 872.
Xia, Z., Stewart, K., Fan, J., 2021. Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area. Computers, Environment and Urban Systems 87, 101599. https://doi.org/10.1016/j.compenvurbsys.2021.101599