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Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra

September 4, 2019

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.

Citation Information

Publication Year 2019
Title Comparison of methods for modeling fractional cover using simulated satellite hyperspectral imager spectra
DOI 10.3390/rs11182072
Authors Philip E. Dennison, Yi Qi, Susan K. Meerdink, Raymond Kokaly, David R. Thompson, Craig S.T. Daughtry, Miguel Quemada, Dar A. Roberts, Paul Gader, Erin Wetherley, Izaya Numata, Keely L. Roth
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing
Series Number
Index ID 70205793
Record Source USGS Publications Warehouse
USGS Organization St. Petersburg Coastal and Marine Science Center