An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Centre d'Etudes Spatiales de la Biosphère UPS CNES CNRS IRD Université de Toulouse 31401 Toulouse Cedex 9 France

Department of Geography Swansea University Swansea SA2 8PP UK

Department of Geography University College London Pearson Building Gower Street WC1E 6BT London UK

Department of Water Resources Faculty ITC University of Twente P O Box 217 7500 AE Enschede The Netherlands

Image Processing Laboratory Parc Científic Universitat de València Paterna València 46980 Spain

National Centre for Earth Observation Department of Physics and Astronomy The University of Leicester Michael Atiyah Building LE1 7RH Leicester UK

Remote Sensing Department Global Change Research Institute CAS Bělidla 986 4a 60300 Brno Czech Republic

Surveying and Spatial Sciences Group School of Technology Environments and Design University of Tasmania Private Bag 76 Hobart TAS 7001 Australia

USRA GESTAR Biospheric Sciences Laboratory NASA Goddard Space Flight Center 8800 Greenbelt Rd Greenbelt MD 20771 USA

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