Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

Journal Article · Wed May 15 00:00:00 EDT 2013 · Remote Sensing of Environment, 132:102–119

DOI:https://doi.org/10.1016/j.rse.2012.12.026· OSTI ID:1079128

Zhao, Kaiguang; Valle, Denis; Popescu, Sorin; Zhang, Xuesong; Malick, Bani

Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 species across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1079128
• Report Number(s): PNNL-SA-93571; 400409900
• Journal Information: Remote Sensing of Environment, 132:102–119, Journal Name: Remote Sensing of Environment, 132:102–119
• Country of Publication: United States
• Language: English