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.
Highlights
Satellite-based monitoring of oceanic biogeochemical variables, such as chlorophyll-a concentration, is still challenged by spatial data loss [1]
The Data INterpolating Empirical Orthogonal Functions (DINEOF) method was applied to Salish Sea MODISA-derived chla products spanning a three-year time period to investigate the accuracy of the derived products according to dataset study period, yearly versus multiyear, and forms of input data, daily versus week composite
Other studies use DINEOF for reconstructing chla for further analyses (e.g., [15,61]), the current study demonstrated that considering the temporal characteristics of an input dataset is an important factor in the effectiveness of the chla reconstruction accuracy
Summary
Satellite-based monitoring of oceanic biogeochemical variables, such as chlorophyll-a concentration (chla), is still challenged by spatial data loss [1]. Recent DINEOF applications include spatial reconstructions of satellite-derived time series of sea surface temperature (SST) [2,11,12,13], sea surface salinity (SSS) [14], chla [15,16,17], turbidity [18], and total suspended matter (TSM) [19], or in multivariate form to exploit natural correlations between variables such as for SST + chla [20,21]. In the Salish Sea region, phytoplankton productivity varies markedly temporally and spatially due to physical forcings, which include Fraser River discharge, tidal activity, solar radiation, and wind stress [27,28]. Absorption from CDOM and chla makes up a higher component of total attenuation in waters north of the Fraser River plume [33,34]
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