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

Remote Sensing

Juan Pablo Rivera¹  Jochem Verrelst¹  Ganna Leonenko² 

[1] Image Processing Laboratory (IPL), Parc Científic, Universitat de València, E-46980 Paterna, Spain; E-Mails:;Department Psychological Med., Cardiff University, Cardiff CF14 4XN, UK; E-Mail:

关键词: biophysical parameters;    LUT-based inversion;    cost functions;    radiative transfer models;    PROSAIL;    Sentinel-2;   

DOI  :  10.3390/rs5073280

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland