Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans

Abstract

Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

“…where ρ is the reflectance of surface water, λ is the wavelength of each band, R λ is the radiance at top of the atmosphere, ESUN is the extraterrestrial solar irradiance, and θ is the solar zenith angle. The pixels were masked if they were not within 0-1 or the solar zenith was larger than 75 • [26,43].…”

“…TP concentration estimation methods based on remote sensing can be divided into semi-analytical approaches and empirical approaches. Semi-analytical approaches [26][27][28] construct a relationship between the reflectance of surface water and the inherent optical characteristics of lakes to inverse TP concentration. The semi-analytical approaches can decouple the contribution of water substances, which lends to the approaches' explainability and provides a physical underpinning [26].…”

“…Semi-analytical approaches [26][27][28] construct a relationship between the reflectance of surface water and the inherent optical characteristics of lakes to inverse TP concentration. The semi-analytical approaches can decouple the contribution of water substances, which lends to the approaches' explainability and provides a physical underpinning [26]. Because the inherent optical characteristics of lakes are often different, the semi-analytical methods lack generality [8,29].…”

“…Neural networks [26,[33][34][35] have been proven to have higher accuracies in TP concentration estimation. Their results show that the correlation between remote-sensing imagery and TP concentration can be modeled by complex neural networks.…”

“…We used an ANN to establish the correlation between the in situ TP concentration of inland lakes and the remote-sensing reflectance of MODIS due to its powerful learning ability. Although water quality parameters vary in a few hours under the conditions of wind and rain [21,26,34], MODIS has high temporal resolution and can follow the daily change of TP concentration in inland lakes [43]. MODIS has a broad spectrum, spanning visible, infrared spectra, and thermal infrared.…”