Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review
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...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 108; pp. 273 - 290 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.10.2015
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| Subjects | |
| Online Access | Get full text |
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| 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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| AbstractList | 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. |
| Author | Verrelst, Jochem Camps-Valls, Gustau Rivera, Juan Pablo Moreno, José Veroustraete, Frank Muñoz-Marí, Jordi Clevers, Jan G.P.W. |
| Author_xml | – sequence: 1 givenname: Jochem orcidid: 0000-0002-6313-2081 surname: Verrelst fullname: Verrelst, Jochem email: jochem.verrelst@uv.es organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain – sequence: 2 givenname: Gustau surname: Camps-Valls fullname: Camps-Valls, Gustau organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain – sequence: 3 givenname: Jordi orcidid: 0000-0002-3014-3921 surname: Muñoz-Marí fullname: Muñoz-Marí, Jordi organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain – sequence: 4 givenname: Juan Pablo surname: Rivera fullname: Rivera, Juan Pablo organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain – sequence: 5 givenname: Frank orcidid: 0000-0002-9019-0498 surname: Veroustraete fullname: Veroustraete, Frank organization: Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, BE2020 Antwerp, Belgium – sequence: 6 givenname: Jan G.P.W. surname: Clevers fullname: Clevers, Jan G.P.W. organization: Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands – sequence: 7 givenname: José surname: Moreno fullname: Moreno, José organization: Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain |
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| SubjectTerms | algorithms Bio-geophysical variables Hybrid image analysis Machine learning monitoring Non-parametric Operational variable retrieval Parametric Physical regression analysis remote sensing satellites spectrometers uncertainty vegetation |
| Title | Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review |
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