Abstract
Empirical methods based on band ratios to infer chlorophyll-a concentration by satellite do not perform well over the optically complex waters of the St. Lawrence Estuary and Gulf. Using a dataset of 93 match-ups, we explore an alternative method relying on empirical orthogonal functions (EOF) to develop an algorithm that relates the satellite-derived remote sensing reflectances to in situ chlorophyll-a concentration for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Results show that an accuracy of 41% at retrieving chlorophyll-a concentration can be reached using the EOF method compared to 140% for the widely-used Ocean Chlorophyll 4 (OC4v4) empirical algorithm, 53% for the Garver-Siegel-Maritorena (GSM01) and 54% for the Generalized Inherent Optical Property (GIOP) semi-analytical algorithms. This result is possible because the EOF approach is able to extract region-specific radiometric features from the satellite remote sensing reflectances that are related to absorption properties of optical components (water, coloured dissolved organic matter and chlorophyll-a) using the visible SeaWiFS channels. The method could easily be used with other ocean-colour satellite sensors (e.g., MODIS, MERIS, VIIRS, OLCI) to extend the time series for the St. Lawrence Estuary and Gulf waters.
Highlights
A method based on empirical orthogonal functions was used to improve the accuracy at which chlorophyll-a (Chl) concentrations can be retrieved for the optically complex St
The Chl concentration retrieval accuracy was greatly improved from 140% using the OC4v4 band-ratio approach to 41%
With the increasing popularity of the SeaBASS dataset, the empirical orthogonal functions (EOF) method could be applied to global data
Summary
The St. Lawrence Estuary and Gulf (SLEG), in Eastern Canada, is a large (250,000 km2) and complex coastal ecosystem where the biological, physical and chemical features are highly dynamic as a result of strong tides, winds, a high volume of freshwater runoff, complex bathymetry and winter sea ice [1,2]. Phytoplankton form the basis of this ecosystem. Their abundance is estimated by measuring the concentration of chlorophyll-a (photosynthetic pigment contained by all phytoplankton), a proxy for phytoplankton biomass [3]. Knowledge of phytoplankton standing stock and distribution helps characterize the status of marine ecosystems, thereby facilitating their protection through sustainable management practices [7]. Phytoplankton are sensitive indicators of changing chemical and physical conditions due to their short life cycles [8,9]
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