Zhang, Hankui

Postdoctoral Fellow

B.S. in Geographic information systems, Zhejiang University, Hangzhou, China. 2007.
M.S. in Remote sensing, Zhejiang University, Hangzhou, China. 2010.
Ph.D. in Geography and resource management, Chinese University of Hong Kong, Hong Kong, China. 2013.

Research Interests: 
Landsat data preprocessing, multi-resolution satellite image fusion, and Bayesian statistics in satellite image processing

Google scholar: https://scholar.google.com/citations?user=e2ziC5IAAAAJ&hl=en

Hankui Zhang is a post-doc fellow with Dr. David Roy working on the WELD project since Feb 2014.



2016-2017 South Dakota State University Professional Staff Excellence in Research & Scholarship


Selected Publications:

Zhang, H. K. and Roy, D. P. Using the 500 m MODIS land cover product to derive consistent 30 m continental scale Landsat land cover products, submitted to Remote Sensing of Environment.

Roy, D.P, Li, J., Zhang, H.K., Yan, L., Huang, H., Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance, submitted to Remote Sensing of Environment.  

Zhang, H. K. and Roy, D.P., 2016​, Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change, Remote Sensing of Environment, 186, 217–233. (http://www.sciencedirect.com/science/article/pii/S003442571630325X

Huang, H., Roy, D.P., Boschetti, B., Zhang, H.K., Yan, L., Kumar, S.S., Gomez-Dans, J., Li, J., Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination, Remote Sensing, 2016, 8(10), 873. (http://www.mdpi.com/2072-4292/8/10/873/html)

Roy, D.P., Zhang, H. K., Ju, J., Gomez-Dans, J. L., Lewis, P.E., Schaaf C.B., Sun, Q., Li, J., Huang, H., Kovalskyy, V., 2016, A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance, Remote Sensing of Environment, 176, 255-271. (http://www.sciencedirect.com/science/article/pii/S0034425716300220)

Yan, L., Roy, D.P., Zhang, H.K., Li, J., Huang, H., 2016, An automated approach for sub-pixel registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery. Remote Sensing, 8(6), 520. (http://www.mdpi.com/2072-4292/8/6/520).  In special issue "First Experiences with European Sentinel-2 Multi-Spectral Imager (MSI)".

Roy, D.P., Li, J., Zhang, H.K., Yan, L., 2016, Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data, Remote Sensing Letters, 7(11), 1023-1032. doi:10.1080/2150704X.2016.1212419 (http://www.tandfonline.com/doi/full/10.1080/2150704X.2016.1212419)

Zhang, H. K., and Roy, D.P., 2016, Computationally inexpensive Landsat 8 Operational Land Imager (OLI) pansharpening, Remote Sensing, 8, 180; doi:10.3390/rs8030180. (http://www.mdpi.com/2072-4292/8/3/180/pdf)

Roy, D.P., Kovalskyy, V., Zhang, H. K., Vermote, E.F., Yan, L., Kumar, S.S, Egorov, A., 2016, Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sensing of Environment. 185, 57–70. (http://www.sciencedirect.com/science/article/pii/S0034425715302455)

Zhang, H. K., Roy, D.P., Kovalskyy, V. 2016. Optimal solar geometry definition for global long term Landsat time series bi-directional reflectance normalization, IEEE Transactions on Geoscience and Remote Sensing, 54 (3), 1410-1418doi: 10.1109/TGRS.2015.2480684. (http://ieeexplore.ieee.org/document/7295567/?reload=true&arnumber=7295567)                             (Note, the right hand side of Equation 2 should be: (1.36292×10^(-9)×α^5 - 3.15403×10^(-8)×α^4 - 3.15819614×10^(-6)×α^3 + 0.0000652685643×α^2 + 0.0120604786763×α + 10.06)

Roy, D.P., Kovalskyy, V., Zhang, H. K., Yan, L., Kommareddy, I., The utility of Landsat data for global long term terrestrial monitoring, chapter inRemote Sensing Time Series  -  Revealing Land Surface Dynamics, Eds. Claudia Kuenzer, C., Dech, S.,  Wagner, W.,  Remote Sensing and Digital Image Processing, Volume 22, 2015, pp 289-305, Springer International, Switzerland.

Zhang, H.K., & Huang, B., 2015, A new look at image fusion methods from a Bayesian perspective. Remote Sensing, 7(6), 6828-6861. (http://www.mdpi.com/2072-4292/7/6/6828?trendmd-shared=0)

Zhang, H.K., Chen J.M., Huang, B., Song H.H., & Li Y.R., 2014, Reconstructing seasonal variation of Landsat vegetation index related to leaf area index by fusing with MODIS data. IEEE Transaction of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3),  950-960. (http://ieeexplore.ieee.org/document/6642144/?reload=true&arnumber=6642144)

Zhang, H.K., & Huang, B., 2013, Support vector regression-based downscaling for intercalibration of multiresolution satellite images. IEEE Transaction on Geoscience and Remote Sensing, 51(3), 1114-1123. (http://ieeexplore.ieee.org/document/6464568/?arnumber=6464568)

Huang, B., Zhang, H.K., Song H.H., Wang, J., & Song, C.Q., 2013, Unified fusion of remote sensing imagery: Generating simultaneously high-resolution synthetic spatial-temporal-spectral earth observations. Remote Sensing Letters, 4(6), 561-569. (http://www.tandfonline.com/doi/full/10.1080/2150704X.2013.769283)



Last modified: 
Mar 09, 2017