Title
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties : a review Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties : a review
Author
Faculty/Department
Faculty of Sciences. Bioscience Engineering
Publication type
article
Publication
Amsterdam :Elsevier science bv ,
Subject
Economics
Physics
Engineering sciences. Technology
Source (journal)
ISPRS journal of photogrammetry and remote sensing / International Society for Photogrammetry and Remote Sensing. - Amsterdam
Volume/pages
108(2015) , p. 273-290
ISSN
0924-2716
ISI
000363075300022
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery. We can categorize these methods into (1) parametric regression, (2) non-parametric regression, (3) physically-based and (4) hybrid methods. Hybrid methods combine generic capabilities of physically-based methods with flexible and computationally efficient methods, typically non-parametric regression methods. A review of the theoretical basis of all these methods is given first and followed by published applications. This paper focusses on: (1) retrievability of bio-geophysical variables, (2) ability to generate multiple outputs, (3) possibilities for model transparency description, (4) mapping speed, and (5) possibilities for uncertainty retrieval. Finally, the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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