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Regression of in-water radiometric profile data
Abstract:

This study addresses the regression of in-water radiometric profile data with the objective of investigating solutions to minimize uncertainties of derived products like subsurface radiance and irradiance (Lu0 and Ed0) and diffuse attenuation coefficients. Analyses are conducted using radiometric profiles generated through Monte Carlo simulations and field measurements. A nonlinear NL approach is presented as an alternative to the standard linear method LN. Results indicate that the LN method, relying on log-transformed data, tends to underestimate regression results with respect to NL operating on non-transformed data. The log-transformation is thus identified as the source of biases in data products. Observed differences between LN and NL regression results for Lu0 are of the order of 1-2%, that is well below the target uncertainty for data products from in situ measurements (i.e., 5%). For Ed0, instead, differences can easily exceed 5% as a result of more pronounced light focusing and defocusing effects due to wave perturbations. This work also remarks the importance of applying the multi-cast measurement scheme as a mean to increase the precision of data products.


URL: http://dx.doi.org/10.1364/OE.21.027707

Journal: Optics Express ( United States )

Volume: 21

Number: 23

Pages: 27707 to 27733


Date: November, 2013


Authors:
    Davide D\'Alimonte (CENTRIA), Eugeny B. Shybanov (Ukrainian National Academy of Sciences), Giuseppe Zibordi (Joint Research Centre), Tamito Kajiyama
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