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Open AccessFeature PaperArticle

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Department of Geography, University of Utah, Salt Lake City, UT 84112, USA
School of Natural Resources, University of Nebraska Lincoln, Lincoln, NE 68583, USA
Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL 32611, USA
Geophysics and Geochemistry Science Center, US Geological Survey, Denver, CO 80225, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Hydrology and Remote Sensing Laboratory, US Department of Agriculture Agricultural Research Service, Beltsville, MD 20705, USA
School of Agricultural Engineering, CEIGRAM, Universidad Politecnica de Madrid, 28040 Madrid, Spain
Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Science, The Climate Corporation, San Francisco, CA 94103, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2072;
Received: 16 July 2019 / Revised: 1 September 2019 / Accepted: 2 September 2019 / Published: 4 September 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions. View Full-Text
Keywords: fractional cover mapping; field spectroscopy; PRISMA; HISUI; EnMAP; HyspIRI; Surface Biology and Geology (SBG) fractional cover mapping; field spectroscopy; PRISMA; HISUI; EnMAP; HyspIRI; Surface Biology and Geology (SBG)
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MDPI and ACS Style

Dennison, P.E.; Qi, Y.; Meerdink, S.K.; Kokaly, R.F.; Thompson, D.R.; Daughtry, C.S.T.; Quemada, M.; Roberts, D.A.; Gader, P.D.; Wetherley, E.B.; Numata, I.; Roth, K.L. Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra. Remote Sens. 2019, 11, 2072.

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