  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) non-parametric regression,
including linear and non-linear 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 the operational
production of biophysical variables are given.
