Applications of DINEOF to satellite-derived chlorophyll-a from a productive coastal region

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dc.contributor.authorHilborn, Andrea
dc.contributor.supervisorCosta, Maycira
dc.date.accessioned2018-10-10T20:06:10Z
dc.date.available2018-10-10T20:06:10Z
dc.date.copyright2018en_US
dc.date.issued2018-10-10
dc.degree.departmentDepartment of Geographyen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractA major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, dataset reconstructions with this method are sensitive to the characteristics of the input data used. The spatial and temporal coverage of the input imagery can heavily impact the reconstruction outcome, and thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, the DINEOF method was applied to a three-year time series of MODIS-Aqua chlorophyll-a of the Salish Sea, Canada. Spatial reconstructions were performed on an annual and multi-year basis at daily and week- composite time resolutions, and assessed relative to the original, clouded chla datasets and a set of extracted in situ chla measurements. A sensitivity test was performed to assess stability of the results with variation of cross-validation data and simulated scenarios of lower temporal data coverage. Daily input time series showed greater accuracy reconstructing chla (95.08-97.08% explained variance, RMSExval 1.49 - 1.65 mg m-3) than week-composite counterparts (68.99-76.88% explained variance, RMSExval 1.87 – 2.07 mg m-3), with longer time series of both types producing a better relationship to original chla pixel concentrations (R 0.95 over 0.94, RMSE 1.29 over 1.35 mg m-3, slope 0.88 over 0.84). Original daily chla achieved a better relationship to in situ matchups than DINEOF gap-filled chla, with annual DINEOF-processed data performing better than the multi-year. The results of this study are of interest to those who require spatially continuous satellite-derived products, particularly from short time series, and encourage processing consistency in future DINEOF studies to allow unification for global purposes such as climate change studies (Mélin et al., 2017).en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/10158
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectDINEOFen_US
dc.subjectMODIS-Aquaen_US
dc.subjectSalish Seaen_US
dc.subjectMUMM+SWIRen_US
dc.subjectcoastal oceanen_US
dc.subjectocean colour remote sensingen_US
dc.subjectchlorophyll-a concentrationen_US
dc.subjectinterpolationen_US
dc.titleApplications of DINEOF to satellite-derived chlorophyll-a from a productive coastal regionen_US
dc.typeThesisen_US

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