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Related Experiment Video

Updated: Aug 29, 2025

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
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Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods.

Jochem Verrelst1, Zbyněk Malenovský2,3,4, Christiaan Van der Tol5

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

Surveys in Geophysics
|September 9, 2022
PubMed
Summary

New satellite missions will provide unprecedented spectroscopic data, enabling detailed vegetation property mapping. This review categorizes retrieval methods for analyzing this data, focusing on accuracy and processing speed for Earth observation.

Keywords:
Imaging spectroscopyInversionMachine learningParametric and nonparametric regressionRadiative transfer modelsRetrievalUncertaintiesVegetation properties

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Area of Science:

  • Earth Observation
  • Spectroscopy
  • Vegetation Science

Background:

  • Forthcoming Earth-observing satellite missions will generate vast imaging spectroscopy data streams.
  • This data offers significant opportunities to quantify diverse biochemical and structural vegetation properties.
  • Reliable retrieval techniques are crucial for processing large datasets and quantifying biophysical variables.

Purpose of the Study:

  • To review and categorize state-of-the-art retrieval methods for vegetation biophysical variables using experimental imaging spectroscopy data.
  • To prepare for the operational processing of new-generation spectroscopy data from upcoming satellite missions.
  • To provide recommendations for future spectroscopy-based processing chains.

Main Methods:

  • Categorization of retrieval methods into parametric regression (vegetation indices, shape indices, spectral transformations), nonparametric regression (machine learning), physically based (radiative transfer models - RTMs), and hybrid approaches.
  • Overview of widely applied methods within each category for mapping vegetation properties.
  • Discussion of challenges including spectral multicollinearity, robust estimation, uncertainty quantification, and processing speed.

Main Results:

  • Identified and categorized four main types of retrieval methods: parametric, nonparametric, physically based, and hybrid.
  • Highlighted the importance of spectral multicollinearity, robust estimates, retrieval uncertainties, and processing speed for operational applications.
  • Provided a comprehensive overview of current techniques for inferring vegetation biophysical variables from imaging spectroscopy.

Conclusions:

  • The review provides a framework for understanding and applying various retrieval methods to large-scale imaging spectroscopy data.
  • Addressing challenges like spectral multicollinearity and ensuring processing efficiency are key for operational biophysical variable production.
  • Recommendations are made for developing next-generation processing chains to fully leverage upcoming satellite data for vegetation monitoring.