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Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods
Citation
Verrelst, J and Malenovsky, Z and Van der Tol, C and Camps-Valls, G and Gastellu-Etchegorry, J-P and Lewis, P and North, P and Moreno, J, Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods, Surveys in Geophysics, 40, (3) pp. 589-629. ISSN 0169-3298 (2018) [Refereed Article]
Copyright Statement
Copyright 2018 Springer Science+Business Media B.V., part of Springer Nature
DOI: doi:10.1007/s10712-018-9478-y
Abstract
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.
Item Details
Item Type: | Refereed Article |
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Keywords: | imaging spectroscopy, retrieval, vegetation properties, parametric and nonparametric regression, machine learning, radiative transfer models, inversion, uncertainties |
Research Division: | Engineering |
Research Group: | Geomatic engineering |
Research Field: | Photogrammetry and remote sensing |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the earth sciences |
UTAS Author: | Malenovsky, Z (Dr Zbynek Malenovsky) |
ID Code: | 127577 |
Year Published: | 2018 |
Funding Support: | Australian Research Council (FT160100477) |
Web of Science® Times Cited: | 51 |
Deposited By: | Geography and Spatial Science |
Deposited On: | 2018-08-03 |
Last Modified: | 2019-12-03 |
Downloads: | 0 |
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