Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.

Analyzing Satellite Ocean Color Match-Up Protocols Using the Satellite Validation Navy Tool (SAVANT) at MOBY and Two AERONET-OC Sites

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors.

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

Remote Sensing of Phytoplankton Pigments

Pigments, as a vital part of phytoplankton, act as the light harvesters and protectors in the process of photosynthesis. Historically, most of the previous studies have been focused on chlorophyll a, the primary light harvesting pigment. With the advances in technologies, especially High-Performance Liquid Chromatography (HPLC) and satellite ocean color remote sensing, recent studies promote the importance of the phytoplankton accessory pigments. In this chapter, we will overview the technology advances in phytoplankton pigment identification, the history of ocean color remote sensing and its application in retrieving phytoplankton pigments, and the existing challenges and opportunities for future studies in this field.