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

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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.

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Acknowledgements

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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Authors and Affiliations

  1. Image Processing Laboratory (IPL), Parc Científic, Universitat de València, Paterna, València, 46980, Spain

    Jochem Verrelst, Gustau Camps-Valls & Jose Moreno

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

    Zbyněk Malenovský

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

    Zbyněk Malenovský

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

    Zbyněk Malenovský

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

    Christiaan Van der Tol

  6. Centre d’Etudes Spatiales de la Biosphère - UPS, CNES, CNRS, IRD, Université de Toulouse, 31401, Toulouse Cedex 9, France

    Jean-Philippe Gastellu-Etchegorry

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

    Philip Lewis

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

    Philip Lewis

  9. Department of Geography, Swansea University, Swansea, SA2 8PP, UK

    Peter North

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  1. Jochem Verrelst
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  2. Zbyněk Malenovský
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Correspondence to Jochem Verrelst.

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Verrelst, J., Malenovský, Z., Van der Tol, C. et al. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surv Geophys 40, 589–629 (2019). https://doi.org/10.1007/s10712-018-9478-y

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