Yan, Lin

Assistant Research Professor
Email: 
lin.yan@sdstate.edu

Lin Yan is an assistant research professor working on agriculture monitoring, land-cover and land-use change (LCLUC), and satelliet time series algorithms development using Landsat and Sentinel-2 data.

Lin Yan received his Ph. D. in Geodetic Science and Surveying Engineering from The Ohio State University (OSU) (2011), and he received his M.S. in Photogrammetry and Remote Sensing (2005) and his B.S in Surveying Engineering (2002) from Tongji University.   His Ph.D. research focused on spectral-spatial classification and nonlinear dimensionality reduction of hyperspectral remote sensing images. Lin also served as software engineer in the Mapping and GIS Lab at OSU supporting NASA's Mars Exploration Rover (MER) and Lunar Reconnaissance Orbiter (LRO) missions.

 

Grants

• Yan, L. (Principal Investigator), Roy, D.P., Sentinel-2/Landsat-8 automated registration software tool development to support the terrestrial moderate spatial resolution user community. This proposal seeks to develop open-source software for precise registration of the Landsat-8 and Sentinel-2 Level 1 products. The proposed work will be undertaken over two years with a phased software delivery.  The software will be released to the NASA MuSLI Science Team and refined as needed based on their informed feedback.  By the end of the second year, the software will also be released to the general public. Funded by NASA Land Cover/Land Use Change (LCLUC) Multi-Source Land Imaging (MuSLI) program grant NNX17AB34G by an unsolicited proposal to NASA, total 2-year budget $100,012 for November 2016 – November 2018.

• Roy, D.P. (Principal Investigator), 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.  They will: (1) develop Landsat and Sentinel-2 processing, involving assessment and development of algorithms and development of Sentinel-2 ARD comparable to the Landsat ARD, and reflectance correction to nadir BRDF-adjusted reflectance (NBAR); (2) undertake quality assessment and characterization of the consistency of the Landsat and Sentinel-2 ARD; (3) investigate the utility of the data to develop annual land cover products using the state of the art non-parametric supervised classification approaches; (4) investigate the utility of the data to develop timely land cover mapping within the growing season and near-real time (NRT) change detection; (5) assess the expansion of tasks #1-4 to global scale, including making recommendations concerning the development of a global ARD, (6) through these integrative activities make informed recommendations for and provide feedback on planned Landsat-10 observational capabilities. The proposed research is directly responsive to the Data Characterization, Landsat Science Data Products and Data Applications solicitation topics.  It will involve collaboration with the Landsat Science Team, engineers, data distribution personnel, and USGS and NASA management, and will lead to recommendations for refinements to the Landsat product format, content, and distribution for Collection 1 and 2, CONUS ARD, and the global ARD, and will inform the evolution of Landsat-10 and the Sustainable Land Imaging Program. The research will be developed using all Landsat-8 and -9 ARD and Sentinel-2A, -2B and –2C L1C data available over North Dakota, South Dakota, Nebraska, Minnesota and Iowa. These five states are contiguous and contain a variety of land covers and land uses and a diversity of agricultural uses including short growing seasons where temporal data availability is important and so challenging for classification and change detection algorithms. 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.

 

Select Publications

Yan, L. and Roy, D.P. (2018). Robust large-area gap filling of Landsat reflectance time series by spectral-angle-mapper based spatio-temporal similarity (SAMSTS). Remote Sensing. 10(4), 609; doi:10.3390/rs10040609 (http://www.mdpi.com/2072-4292/10/4/609). In special issue "Science of Landsat Analysis Ready Data".

SAMSTS v1.0 source codes download

Yan, L., Roy, D.P., Li, J., Zhang, H.K., Huang, H. (2018). Sentinel-2A multi-temporal misregistration characterization and an orbit-based sub-pixel registration methodology. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.04.021. In special issue "Science and Applications with Sentinel-2".

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; doi:10.3390/rs8060520 (http://www.mdpi.com/2072-4292/8/6/520).  In special issue "First Experiences with European Sentinel-2 Multi-Spectral Imager (MSI)".

Open-source software (Linux) download (version 2.0 for Landsat-8 and Sentinel-2 registration)

Depth-first least-squares matching C source codes (Windows) download v1.1.1

Yan, L. and Roy, D.P. (2016). Conterminous United States crop field size quantification from multi-temporal Landsat data. Remote Sensing of Environment, 172, 67-86. (http://dx.doi.org/10.1016/j.rse.2015.10.034).
Conterminous United States field extraction data download

Yan, L. and Roy, D.P. (2015). Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction. Remote Sensing of Environment, 158, 478-491. (http://dx.doi.org/10.1016/j.rse.2014.11.024)

Yan, L. and Roy, D.P. (2014) Automated crop field extraction from multi-temporal Web Enabled Landsat Data, Remote Sensing of Environment. 144, 42-64. (http://dx.doi.org/10.1016/j.rse.2014.01.006)

Yan, L. and Niu, X. (2014). Spectral-angle-based Laplacian Eigenmaps for nonlinear dmensionality reduction of hyperspectral imagery. Photogrammetric Engineering & Remote Sensing, 80, 849-861.

• Li, R., Yan, L., Di, K., and Wu, B. (2008). A new ground-based stereo panoramic scanning system. The XXIth ISPRS Congress, Beijing, China, July 3-11, 2008, 6 p.

 

Select Presentations

Yan, L., Roy, D.P., Field Size Estimation, Past and Future Opportunities, in Emerging Technologies and Methods in Earth Observation for Agricultural Monitoring Workshop, Beltsville, Maryland, February 13-15, 2018.

Yan, L., Roy, D.P., Huang, H., Li, Z., Zhang, H.K., Operational Sentinel-2A L1C and Landsat-8 Collection-1 Time-series Registration, in Landsat/Sentinel Cross-Calibration session, PECROA 20 conference, Sioux Falls, South Dakota, November 14-16, 2017.

Yan, L., Roy, D.P, Sentinel-2/Landsat-8 automated registration software tool development to support the terrestrial moderate spatial resolution user community. NASA LCLUC Science Team Meeting, Rockville, MD, April 12-14, 2017.

Yan, L., Roy, D.P., Robust gap filling of Landsat reflectance time series by spectral-angle-mapper based spatio-temporal similarity – demonstration over dynamic U.S. agricultural landscapes. Landsat Science Team Meeting, Brookings, SD, July 26-28, 2016.

Yan, L., Roy, D.P., Conterminous United States crop field extraction from multi-temporal Landsat data. Fall Meeting, AGU, San Francisco, CA, Dec. 14-18, 2015.

Yan, L., Roy, D.P., Mapping and monitoring field sizes from satellite time series. AAG Annual Meeting, Chicago, IL, April 21-25, 2015.

Yan, L., Roy, D.P., Automated agricultural field extraction from multi-temporal Web Enabled Landsat Data. Fall Meeting, AGU, San Francisco, CA, Dec. 3-7, 2012.

• Li., R., Yan, L., He, S., Hwangbo, J., Chen, Y., Tang, M., Wang, W., Tang, P., Lawver, J., Thomas, P.C., Robinson, M., Photogrammetric techniques for terrain model generation from LROC NAC images. ASPRS 2010 Fall Conference, Orlando, FL, Nov. 15-19, 2010.

• Li, R., Yan, L., Di, K., A new ground-based stereo panoramic scanning system, the XXIth ISPRS Congress, Beijing, China, July 3-11, 2008.

• Li, R., Yan, L., Di, K., Initial results of a new ground-based stereo panoramic scanning system. ASPRS 2007 Annual Conference, Tampa, FL, May 7-11, 2007.

Last modified: 
May 22, 2018