ArticleSurveys in geophysics2019
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods.
Article in Surveys in geophysics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed, 475 citations in OpenAlex.
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- PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.Ecological informatics · 2025Article
- DeepSpecN: A new hybrid method combining PROSPECT-PRO and Conv-Transformer to estimate leaf nitrogen content from leaf reflectance.Plant phenomics (Washington, D.C.) · 2025Article
- Exploring the depth of the maize canopy LAI detected by spectroscopy based on simulations and in situ measurements.Plant phenomics (Washington, D.C.) · 2025Article
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- A hybrid method for water stress evaluation of rice with the radiative transfer model and multidimensional imaging.Plant phenomics (Washington, D.C.) · 2025Article
- Mind the leaf anatomy while taking ground truth with portable chlorophyll meters.Scientific reports · 2025Article
- Driving variables to explain soil organic carbon dynamics: páramo highlands of the Ecuadorian Real mountain range.Journal of soils and sediments · 2025Article
- Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software.Environmental monitoring and assessment · 2024Article
- A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm.Frontiers in plant science · 2024Article
- Nitrogen monitoring and inversion algorithms of fruit trees based on spectral remote sensing: a deep review.Frontiers in plant science · 2024Review
- Water content estimation of conifer needles using leaf-level hyperspectral data.Frontiers in plant science · 2024Article
- Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling.European journal of remote sensing · 2023Article
- Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.Sensors (Basel, Switzerland) · 2023Article
- Remotely Sensed Phenotypic Traits for Heritability Estimates and Grain Yield Prediction of Barley Using Multispectral Imaging from UAVs.Sensors (Basel, Switzerland) · 2023Article
- An Assessment of Relations between Vegetation Green FPAR and Vegetation Indices through a Radiative Transfer Model.Plants (Basel, Switzerland) · 2023Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 6 institutions in 7 countries.
Funding
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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