Zhang, Hankui

Assistant Research Professor

B.S. in Geographic Information Science, Zhejiang University, Hangzhou, China. 2007.
M.S. in Geological Resources and Geological Engineering, Zhejiang University, Hangzhou, China. 2010.
Ph.D. in Geography and Resource Management, Chinese University of Hong Kong, Hong Kong, China. 2013.

Research Interests: 
Large area (continental to global scale) and long term (30+ years) Landsat data processing and multi-resolution satellite (e.g., Sentinel-2 and Landsat-8) image fusion

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

Hankui Zhang is an Assistant Research Professor in Dr. David Roy group working on the WELD project since Feb 2014.



Roy, D.P., Yan, L., Zhang, H.K., Pathfinding near real time moderate resolution land surface monitoring, looking forward to an operational Landsat 9/10 Sentinel 2A/2B era, This proposal solicits membership to the Landsat Science Team for Dr. Roy and his team who have a proven capability in assessing, processing and developing products from Landsat and Sentinel-2 data. Funded by U.S. Department of Interior, U.S. Geological Survey, Solicitation G17PS00256, Total 5 year budget $1,173,581 for January 2018 – December 2022.

Roy, D.P., Zhang, H.K., Egorov, H., Michaelis, A., Nemani, R., Global Long-Term Multi-Sensor Web-Enabled Landsat Data Record – A Continuation Request, The Landsat satellites provide the longest temporal record of space-based earth observations.  The current (2012-2017) NASA MEaSUREs funded Global Web Enabled Landsat (GWELD) project has demonstrated the capability to generate global scale 30 m Landsat composited mosaics with a monthly and annual reporting frequency for all of the earth’s terrestrial surface except Antarctica. The GWELD products and browses are generated on the NASA Earth Exchange (NEX) and have been reprocessed several times reflecting changes in the input Landsat format and algorithm improvements identified as a result of our intensive product quality assessment. The GWELD products provide consistent 30 m data that are being used to derive land cover as well as geophysical and biophysical products. The most recent Version 3.0 GWELD products are available for the 2010 epoch (3 years of monthly and annual products from 2009 to 2011). A total of 91.7 TB (>387,000 files) have been distributed from the USGS EROS equivalent to 128% of the product volume. We are submitting this continuation request because we do not anticipate finishing the processing in the 5-year proposal period because of the need to take advantage of USGS improvements in the processing of the Landsat Level 1 data (to Collection 1 and a CONUS gridded analysis-ready data (ARD) stream) that have only recently become available and that we helped define through our Landsat Science Team membership. This modest continuation request is to reprocess and continue the product generation of: (i) monthly and annual 30 m global WELD products for six 3-year epochs centered on 1985, 1990, 1995, 2000, 2005, and 2010 (using the new Collection 1 Landsat data as inputs), (ii) weekly, monthly, seasonal, and annual 30 m CONUS WELD products from 1982 to 2012 (using the new Collection 1 CONUS ARD as inputs), (iii) annual 30 m percent land cover and 5-year land cover change products for the CONUS, 1985 to 2010 (using the data from (ii) as inputs). The products will be generated in reverse chronological order to take advantage of the growing U.S. Landsat archive as data continue to be repatriated from international receiving stations. The products will continue to be distributed in HDF (http://globalweld.cr.usgs.gov/collections), in GeoTiff (http://globalweld.cr.usgs.gov), and with native resolution browses via the NASA Global Image Browse Service (http://go.nasa.gov/2kLcKto). Total 3 year budget $570,881 for August 2018  – July 2021.


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


Selected Publications:

Zhang, H. K., Roy, D. P., Yan, L., Li, Z., Huang, H., Vermote E., Skakun S., Roger J., Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences, Remote Sensing of Environment. In review.

Yan, L., Roy, D.P., Li, Z., Zhang, H.K. & Huang, H., Sentinel-2A multi-temporal misregistration characterization and an orbit-based sub-pixel registration methodology. Remote Sensing of Environment. In review.

Egorov, A. E., Roy, D. P., Zhang, H.K., Hansen, M. C. & Kommareddy, A., 2018. Demonstration of Percent Tree Cover Mapping Using Landsat Analysis Ready Data (ARD) and Sensitivity with Respect to Landsat ARD Processing Level. Remote Sensing, 10(2), 209. (http://www.mdpi.com/2072-4292/10/2/209/htm)

Li Z., Zhang, H. K., Roy, D. P., Yan, L., Huang, H., and Li, J., 2017. Landsat 15 m panchromatic assisted downscaling (LPAD) of the 30 m reflective wavelength bands to Sentinel-2 20 m resolution. Remote Sensing, 9(7), 755. (http://www.mdpi.com/2072-4292/9/7/755/htm)

Roy, D.P., Li, Z., Zhang, H.K., 2017. Adjustment of Sentinel-2 multi-spectral instrument (MSI) red-edge band reflectance to nadir BRDF adjusted reflectance (NBAR) and quantification of red-edge band BRDF effects. Remote Sensing, 9(12), 1325. (http://www.mdpi.com/2072-4292/9/12/1325/htm)

Roy, D.P, Li, J., Zhang, H. K., Yan, L., Huang, H., and Li Z., 2017. 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. Remote Sensing of Environment, 199, 25-38. (http://www.sciencedirect.com/science/article/pii/S0034425717302791)

Zhang, H. K. and Roy, D. P., 2017. Using the 500 m MODIS land cover product to derive consistent 30 m continental scale Landsat land cover products. Remote Sensing of Environment, 197, 15-34. (http://www.sciencedirect.com/science/article/pii/S0034425717302249) (The products can be accessed on request)

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

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)

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), 1410-1418(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)
Zhang, H. K., and Roy, D.P., 2016. Computationally inexpensive Landsat 8 Operational Land Imager (OLI) pansharpening. Remote Sensing, 8, 180. (http://www.mdpi.com/2072-4292/8/3/180/htm)


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/htm).  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. (http://www.tandfonline.com/doi/full/10.1080/2150704X.2016.1212419)

Huang, H., Roy, D.P., Boschetti, B., Zhang, H.K., Yan, L., Kumar, S.S., Gomez-Dans, J., Li, J., 2016. Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination. Remote Sensing, 8(10), 873. (http://www.mdpi.com/2072-4292/8/10/873/html)
Roy, D.P., Kovalskyy, V., Zhang, H. K., Yan, L., Kommareddy, I., 2015. 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/htm)

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 19, 2018