Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 496766
Title Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review
Author(s) Verrelst, Jochem; Camps-Valls, Gustau; Muñoz-Marí, Jordi; Rivera, Juan Pablo; Veroustraete, Frank; Clevers, J.G.P.W.; Moreno, José
Source ISPRS Journal of Photogrammetry and Remote Sensing 108 (2015). - ISSN 0924-2716 - p. 273 - 290.
DOI http://dx.doi.org/10.1016/j.isprsjprs.2015.05.005
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) Bio-geophysical variables - Hybrid - Machine learning - Non-parametric - Operational variable retrieval - Parametric - Physical
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.

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