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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F19%3A00505838" target="_blank" >RIV/86652079:_____/19:00505838 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007%2Fs10712-018-9478-y.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2Fs10712-018-9478-y.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10712-018-9478-y" target="_blank" >10.1007/s10712-018-9478-y</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20705 - Remote sensing

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Surveys in Geophysics

ISSN

0169-3298

e-ISSN

—

Volume of the periodical

40

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

41

Pages from-to

589-629

UT code for WoS article

000468286300011

EID of the result in the Scopus database

2-s2.0-85047942796