Surveys in Geophysics

, Volume 40, Issue 3, pp 589–629 | Cite as

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

  • Jochem VerrelstEmail author
  • Zbyněk Malenovský
  • Christiaan Van der Tol
  • Gustau Camps-Valls
  • Jean-Philippe Gastellu-Etchegorry
  • Philip Lewis
  • Peter North
  • Jose Moreno


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.


Imaging spectroscopy Retrieval Vegetation properties Parametric and nonparametric regression Machine learning Radiative transfer models Inversion Uncertainties 



Jochem Verrelst was supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617). Contribution of Zbyněk Malenovský was supported by the Australian Research Council Future Fellowship: Bridging scales in remote sensing of vegetation stress (FT160100477). Gustau Camps-Valls was supported by the ERC under the ERC-CoG-2014 SEDAL project (grant agreement 647423). We thank the two reviewers for their valuable suggestions.


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Jochem Verrelst
    • 1
    Email author
  • Zbyněk Malenovský
    • 2
    • 3
    • 4
  • Christiaan Van der Tol
    • 5
  • Gustau Camps-Valls
    • 1
  • Jean-Philippe Gastellu-Etchegorry
    • 6
  • Philip Lewis
    • 7
    • 8
  • Peter North
    • 9
  • Jose Moreno
    • 1
  1. 1.Image Processing Laboratory (IPL), Parc CientíficUniversitat de ValènciaValènciaSpain
  2. 2.Surveying and Spatial Sciences Group, School of Technology, Environments and DesignUniversity of TasmaniaHobartAustralia
  3. 3.Remote Sensing DepartmentGlobal Change Research Institute CASBrnoCzech Republic
  4. 4.USRA/GESTAR, Biospheric Sciences LaboratoryNASA Goddard Space Flight CenterGreenbeltUSA
  5. 5.Department of Water Resources, Faculty ITCUniversity of TwenteEnschedeThe Netherlands
  6. 6.Centre d’Etudes Spatiales de la Biosphère - UPS, CNES, CNRS, IRDUniversité de ToulouseToulouse Cedex 9France
  7. 7.Department of GeographyUniversity College LondonLondonUK
  8. 8.National Centre for Earth Observation, Department of Physics and AstronomyThe University of LeicesterLeicesterUK
  9. 9.Department of GeographySwansea UniversitySwanseaUK

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