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Remote Sens. 2013, 5(7), 3280-3304; https://doi.org/10.3390/rs5073280

Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model

1
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, E-46980 Paterna, Spain
2
Department Psychological Med., Cardiff University, Cardiff CF14 4XN, UK
*
Author to whom correspondence should be addressed.
Received: 27 April 2013 / Revised: 25 June 2013 / Accepted: 26 June 2013 / Published: 9 July 2013
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Abstract

Abstract: Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized “L1 -estimate” function with a relative error of 17.6% (r2 : 0.73), while best LAI retrievals were obtained through non-normalized “least-squares estimator” (LSE) with a relative error of 15.3% (r2 : 0.74). View Full-Text
Keywords: biophysical parameters; LUT-based inversion; cost functions; radiative transfer models; PROSAIL; Sentinel-2 biophysical parameters; LUT-based inversion; cost functions; radiative transfer models; PROSAIL; Sentinel-2
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Rivera, J.P.; Verrelst, J.; Leonenko, G.; Moreno, J. Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model. Remote Sens. 2013, 5, 3280-3304.

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