TY - JOUR
T1 - Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review
AU - Verrelst, Jochem
AU - Camps-Valls, Gustau
AU - Muñoz-Marí, Jordi
AU - Rivera, Juan Pablo
AU - Veroustraete, Frank
AU - Clevers, J.G.P.W.
AU - Moreno, José
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Bio-geophysical variables
KW - Hybrid
KW - Machine learning
KW - Non-parametric
KW - Operational variable retrieval
KW - Parametric
KW - Physical
U2 - 10.1016/j.isprsjprs.2015.05.005
DO - 10.1016/j.isprsjprs.2015.05.005
M3 - Article
AN - SCOPUS:84942831872
SN - 0924-2716
VL - 108
SP - 273
EP - 290
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
ER -