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

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.

Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020)

“…To fill these gaps retrieved from MODIS over Romania, the Data Interpolating Empirical Orthogonal Functions (DINEOF) procedure was applied [67][68][69]. Recently, this method has been used in various remote sensing products such as for the reconstruction of total suspended matter [70] sea surface salinity [71], sea surface temperature derived from MODIS [68,72], MODIS-Aqua chlorophyll products [73] or LST over Bucharest [74]. More appropriate to our study, Filliponi et al [75] applied the DINEOF algorithm to the reconstruction of the MODIS Fraction of Green Vegetation around the world.…”

Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea

“…The total list of input parameters. Additionally, Chl-a sat data were transformed to base-e logarithm (ln) values, following the approach used by Hilborn and Costa [36]. The following discussion refers to ln(mg m −3 ) unless otherwise stated.…”

Modeling environmental influence on Atlantic bluefin tuna bycatch by Mexican longliners in the Gulf of Mexico

“…Although the combination of data from different sensors allows the reduction of cloud cover pixels, data were interpolated using the Data Interpolating Empirical Orthogonal Function (DINEOF) to prevent data gaps (Alvera‐Azcárate, Barth, Rixen, & Beckers, ; Beckers, Barth, & Alvera‐Azcarate, ). This approach has been successfully applied to reconstruct oceanographic remote sensing data sets (Ganzedo, Alvera‐Azcárate, Esnaola, Ezcurra, & Sáenz, ; Hilborn & Costa, ; Sirjacobs et al, ; Wang & Liu, ) and even fishery spatiotemporal data (Ganzedo, Erdaide, Trujillo‐Santana, Alvera‐Azcárate, & Castro, ). The resulting set comprises daily data from 1998 to 2012 on a regular 0.25 degree grid for the study area.…”