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Remote Sens. 2018, 10(9), 1449; https://doi.org/10.3390/rs10091449

Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

SPECTRAL Remote Sensing Laboratory, University of Victoria, 3800 Finnerty Rd., Victoria, BC V8P 5C2, Canada
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Received: 25 July 2018 / Revised: 6 September 2018 / Accepted: 8 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Remote Sensing of Ocean Colour)
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Abstract

A 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, the spatial and temporal coverage of the input image dataset can heavily impact the outcomes of using this method and, thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, we used a three-year time series of MODIS-Aqua chlorophyll-a to evaluate the DINEOF reconstruction output accuracy corresponding to variation in the form of the input data used (i.e., daily or week composite scenes) and time series length (annual or three consecutive years) for a dynamic region, the Salish Sea, Canada. The accuracy of the output data was assessed considering the original chla pixels. Daily input time series produced higher accuracy reconstructing chla (95.08–97.08% explained variance, RMSExval 1.49–1.65 mg m−3) than did all week composite counterparts (68.99–76.88% explained variance, RMSExval 1.87–2.07 mg m−3), with longer time series producing better relationships to original chla pixel concentrations. Daily images were assessed relative to extracted in situ chla measurements, with original satellite chla achieving a better relationship to in situ matchups than DINEOF gap-filled chla, and with annual DINEOF-processed data performing better than the multiyear. These results contribute to the ongoing body of work encouraging production of ocean color datasets with consistent processing for global purposes such as climate change studies. View Full-Text
Keywords: DINEOF; chlorophyll-a concentration; data reconstruction; Salish Sea; coastal ocean; MODIS-Aqua; ocean color DINEOF; chlorophyll-a concentration; data reconstruction; Salish Sea; coastal ocean; MODIS-Aqua; ocean color
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hilborn, A.; Costa, M. Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region. Remote Sens. 2018, 10, 1449.

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