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Optimizing LUT-based radiative transfer model inversion for retrieval of biophysical parameters using hyperspectral data

Verrelst, J., Rivera, G. P., Leonenko, Ganna M., Alonso, L. and Moreno, J. 2012. Optimizing LUT-based radiative transfer model inversion for retrieval of biophysical parameters using hyperspectral data. Presented at: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Picastaway, NJ: IEEE, pp. 7325-7328. 10.1109/IGARSS.2012.6351969

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Abstract

Inversion of radiative transfer models using a lookup-table (LUT) approach against hyperspectral data streams leads to retrievals of biophysical parameters such as chlorophyll content (Chl), but necessary optimization strategies are not consolidated yet. Here, various regularization options have been evaluated to the benefit of improved Chl retrieval from hyperspectral CHRIS data, being: i) the role of added noise, ii) the role of multiple best solutions, and iii) the role of applied cost functions in LUT-based inversion. By using data from the ESA-led field campaign SPARC (Barrax, Spain), it was found that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast and M-estimates. We found that so-called `Power divergence measure', `Trigonometric' and spectral measure with `Contrast function K(x)=-log(x)+x' outperformed RMSE. The whole inversion approach, including more than 60 different cost functions, has been implemented in the ARTMO (Automated Radiative Transfer Models Operator) GUI toolbox and can easily be applied to other kinds of multispectral or hyperspectral images.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Mathematics
Medicine
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects: Q Science > QA Mathematics
Publisher: IEEE
ISBN: 9781467311601
ISSN: 2153-6996
Last Modified: 05 Jun 2017 04:39
URI: http://orca.cf.ac.uk/id/eprint/64173

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