Date of Award
8-2004
Level of Access Assigned by Author
Campus-Only Thesis
Degree Name
Master of Science (MS)
Department
Oceanography
Advisor
Emmanuel Boss
Second Committee Member
Mary Jane Perry
Third Committee Member
Andrew C. Thomas
Abstract
A method to invert in-water total absorption and backscattering coefficients, as well as absorption coefficients by phytoplankton and by colored dissolved organic matter (CDOM) and non-algal particles (NAP) and backscattering coefficients by particulates, from remote-sensing reflectance analytically and to quantify their uncertainties associated with the inversion products is presented. These uncertainties are computed based on finding all the possible solutions that are within an acceptable level of tolerance around the observed remote-sensing reflectance (rrs). The statistics of these solutions are calculated from the distribution of all possible parameters associated with the shapes of the IOP. We demonstrate the uncertainty calculation algorithm with both a dataset of IP and rrs collected during the HyCODE field campaign and a simulated dataset developed by ZP Lee. Good agreement is achieved between the measured and inverted IOP values in particulate when the associated uncertainties are taken into account. The results are as follows: at 440nm the median relative difference of total absorption coefficients is 7.75% for simulated dataset and 11.7% for HyCODE field dataset; at 550 nm, the median relative difference of particulate backscattering coefficient is 7.55% for simulated dataset and 22.8% for in situ dataset. The total absorption at 440 nm is found with more than 80% of the point inside the 90% error bounds in both datasets. The results indicate that the particular inversion method presented here works well and that the method to quantify the uncertainty in the inversion parameters is both informative and useful.
Recommended Citation
Wang, Peng, "A Method to Quantify the Uncertainties Associated with Semi-Analytic Algorithm for Inversion of Ocean Color" (2004). Electronic Theses and Dissertations. 1560.
https://digitalcommons.library.umaine.edu/etd/1560