Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Jochem Verrelst (Corresponding Author), Zbynek Malenovsky, Christiaan van der Tol, Gustau Camps-Valls, Jean-Philippe Gastellu-Etchegorry, Philip Lewis, Peter North, Jose Moreno

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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.
Original languageEnglish
Pages (from-to)589-629
Number of pages41
JournalSurveys in Geophysics
Volume40
Issue number3
Early online date1 Jun 2018
DOIs
Publication statusPublished - 1 May 2019

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vegetation
retrieval
regression analysis
spectroscopy
Spectroscopy
Imaging techniques
Processing
machine learning
Radiative transfer
radiative transfer
Learning systems
Earth (planet)
vegetation mapping
spectroradiometers
satellite mission
vegetation index
Linear regression
recommendations
method
Satellites

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • UT-Hybrid-D

Cite this

Verrelst, J., Malenovsky, Z., van der Tol, C., Camps-Valls, G., Gastellu-Etchegorry, J-P., Lewis, P., ... Moreno, J. (2019). Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics, 40(3), 589-629. https://doi.org/10.1007/s10712-018-9478-y
Verrelst, Jochem ; Malenovsky, Zbynek ; van der Tol, Christiaan ; Camps-Valls, Gustau ; Gastellu-Etchegorry, Jean-Philippe ; Lewis, Philip ; North, Peter ; Moreno, Jose. / Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data : A Review on Retrieval Methods. In: Surveys in Geophysics. 2019 ; Vol. 40, No. 3. pp. 589-629.
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Verrelst, J, Malenovsky, Z, van der Tol, C, Camps-Valls, G, Gastellu-Etchegorry, J-P, Lewis, P, North, P & Moreno, J 2019, 'Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods' Surveys in Geophysics, vol. 40, no. 3, pp. 589-629. https://doi.org/10.1007/s10712-018-9478-y

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data : A Review on Retrieval Methods. / Verrelst, Jochem (Corresponding Author); Malenovsky, Zbynek; van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose.

In: Surveys in Geophysics, Vol. 40, No. 3, 01.05.2019, p. 589-629.

Research output: Contribution to journalArticleAcademicpeer-review

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