Ocean Features Research Articles

At-sea distribution patterns of the Peruvian diving petrel Pelecanoides garnotii during breeding and non-breeding seasons

At-sea distributions of seabird species are strongly associated with the distribution patterns of their prey, which are influenced by physical oceanic features. During breeding and non-breeding seasons, seabirds move extraordinary distances among different environments. However, foraging site fidelity by seabirds appears to be high in areas of known high productivity, such as frontal zones and upwellings. Here, we present a tracking study for the Peruvian diving-petrel Pelecanoides garnotii, an endemic seabird of the highly productive Humboldt Current System, to assess whether adults use the same foraging areas throughout the year, combining data from nest monitoring and global location sensors (GLS) deployed on 12 individuals between two breeding seasons (2013–2014 and 2014–2015), in Choros Island (29°15′S; 71°32′W), Chile. Two main foraging areas were registered. During the breeding season, adults moved in the northern direction, between 60 to 144 km away from their colony, foraging in areas with high primary productivity. During the non-breeding period, they moved to southern latitudes (~ 1200 km). Adults spent 37% and 63% of their time in flight/land and on/underwater activities, respectively. We determined that birds move northward from their colony during breeding, where prey availability seems more predictable throughout the year. However, during the non-breeding period, it is likely that other environmental factors influence the distribution pattern of the Peruvian diving-petrel.

Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix

Abstract The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE).

Multivariable design optimization for adaptive resonance of a surface riding wave energy converter

Since the ocean waves feature irregular waves varying over time, a wave energy converter with adaptive resonance, which feasibly changes the natural frequency for different sea states, is considered. To find its optimum design, multi-dimensional parametric optimization is formed in sequence of setting ranges of submerged geometry for hydrodynamic performance, finding design specifications including the generator for maximum resonance peaks, and locating the natural frequency for individual sea states to achieve the adaptive resonance that converts the maximum annual mechanical power for the given geometry. The multivariable design optimization is performed for a kilowatt-scale Surface-Riding Wave Energy Converter, which controls the pitch natural frequency through relocating a mass vertically. The optimized design presents that the adaptive resonance improves the mechanical power conversion up to 11 times compared to the conventional fixed resonance design. Furthermore, the adaptive resonance for diverse wave spectra indicates that the optimum natural frequencies for individual sea states, maximizing the annual mechanical power conversion, do not coincide with the waves’ peak or energy period. The second-order wave loads entails further change of the optimum natural frequency resulting in subsequent 10 times performance improvement for severe sea states with high peak periods.

Ocean mixing during Hurricane Ida (2021): the impact of a freshwater barrier layer

Tropical cyclones are +ne of the costliest and deadliest natural disasters globally, and impacts are currently expected to worsen with a changing climate. Hurricane Ida (2021) made landfall as a category 4 storm on the US Gulf coast after intensifying over a Loop Current eddy and a freshwater barrier layer. This freshwater layer extended from the coast to the open ocean waters south of the shelf-break of the northern Gulf of Mexico (GoM). An autonomous underwater glider sampled this ocean feature ahead of Hurricane Ida operated through a partnership between NOAA, Navy, and academic institutions. In this study we evaluate hurricane upper ocean metrics ahead of and during the storm as well as carry out 1-D shear driven mixed layer model simulations to investigate the sensitivity of the upper ocean mixing to a barrier layer during Ida’s intensification period. In our simulations we find that the freshwater barrier layer inhibited cooling by as much as 57% and resulted in enhanced enthalpy flux to the atmosphere by as much as 11% and an increase in dynamic potential intensity (DPI) of 5 m s-1 (~9.72 knots) in the 16 hours leading up to landfall. This highlights the utility of new ocean observing systems in identifying localized ocean features that may impact storm intensity ahead of landfall. It also emphasizes the northern Gulf of Mexico and the associated Mississippi River plume as a region and feature where the details of upper ocean metrics need to be carefully considered ahead of landfalling storms.

A census and properties of mesoscale Kuril eddies in the altimetry era

The ocean-side area of the Kuril Islands is a region with high bioproductivity and rich fishing and a convergence of transformed subtropical and subarctic waters with significant impact on weather and climate. Waters of different origin mix in the region including Kuroshio Extention, Japan Sea, Oyashio, Okhotsk Sea and Alaskan Stream waters. Despite the important role of Kuril eddies (KE) in transporting heat, salt, nutrients and their biological activity, there are still major gaps in our knowledge of the properties and dynamics of these energetic and mesoscale features in the northwestern Pacific Ocean. Using altimetry data during 1993–2021, we conducted the first systematic census of KE with the help of the automatic eddy tracking algorithm AMEDA. We separated KE into four groups according to their places of formation in relation to the Kuril-Kamchatka Trench and found a strong inhomogeneous spatial distribution of the Kuril cyclones (KC) and anticyclones (KAC). Different kinematic properties of KE have been studied and analyzed. The Trench has been found to play a role of an eddy guide for long-lived eddies with a comparatively small number of KE being able to cross the Trench. The highest occurrence frequency of KAC was observed along the Trench, especially in the area east off the Bussol Strait, whereas KC occurred more frequently along the ocean side of the Trench. Among the observed long-lived KE, there were more than 90% of eddies with the nonlinearity parameter exceeding 1 implying that the majority of KE are coherent vortices transporting water with its properties. The relative concentrations of waters of different origin inside KAC and KC cores were computed for each group applying the new Lagrangian algorithm. It allowed us to detect transportation of subtropical water in the KE cores to north and subarctic water to south over the distance of the order of 1000 km and more.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Outlook: Normal Atlantic Hurricane Season Expected

Atmospheric and oceanic features are simultaneously strengthening and suppressing hurricane activity this year.

Interdecadal instability of the interannual connection between southern Tibetan Plateau precipitation and Southeast Asian summer monsoon

It is observed that the significant negative relationship between the southern Tibetan Plateau precipitation (STPP) and the Southeast Asian summer monsoon (SEASM) uncovered recently has weakened apparently during the recent decade. The current study is aimed at investigating the interdecadal instability of this TP-SEASM connection and the possible impacting factors. During 1979–2010, the TP heating significantly weakened the SEASM through the interaction between the eastward-extended South Asian high (SAH) and the westward-extended western Pacific subtropical high (WPSH), while no significant change occurred in the SEASM associated with the enhanced TP heating during 2011–2020. Compared with the climate state of 1979–2010, sea surface temperature (SST) warmed in almost entire globe and low-level cyclonic circulation and precipitation were enhanced in the SEASM region during 2011–2020. Meanwhile, the WPSH intensified and its northern boundary extended northward. These oceanic and atmospheric features could be important factors that broke the interaction between the SAH and the WPSH and weakened the negative relationship between STPP and SEASM. The model experiment forced by observed global SST warming validates the above observational features and hypothesis. The warmer SST enhanced the low-level cyclone in the SEASM region and extended the northern boundary of WPSH northward, weakening the influence of SAH on the downstream SEASM region. Therefore, the SEASM region was not under the control of an anomalous high-pressure system so that the negative TP-SEASM relationship became weakened during the recent decade.

Hydrographic open data for society

Hydrography is defined as: “The branch of applied sciences which deals with the measurement and description of the physical features of oceans, seas, coastal areas, lakes and rivers, as well as with the prediction of their change over time, for the primary purpose of safety of navigation and in support of all other marine activities, including economic development, security and defence, scientific research, and environmental protection” (Publication S-32). Due to their broad concept hydrographic data and information provide the foundational bases for marine space management, fisheries, coastal environment, policy decisions, shipping, energy and oil industry, etc. Portuguese Hydrographic Institute (IH) manages geospatial datasets from several scientific and technical domains. Data management has been on daily agenda and always has an internal priority. Facing the digital transformation tsunami and rapid evolution of society data requirements is the main driven for developing an internal sustainable open data strategy aligned with findable, accessible, interoperable and reusable (FAIR) principles and as open as possible. The European Open Data and Open Science strategies combined with the need to fill the ocean knowledge gaps are changing the way how data producers deal with geospatial information. This article presents several IH projects to increase sharing and reuse of hydrographic data by society.

Observations of the space/time scales of Beaufort sea acoustic duct variability and their impact on transmission loss via the mode interaction parameter.

The Beaufort duct (BD) is a subsurface sound channel in the western Arctic Ocean formed by cold Pacific Winter Water (PWW) sandwiched between warmer Pacific Summer Water (PSW) and Atlantic Water (AW). Sound waves can be trapped in this duct and travel long distances without experiencing lossy surface/ice interactions. This study analyzes BD vertical and temporal variability using moored oceanographic measurements from two yearlong acoustic transmission experiments (2016-2017 and 2019-2020). The focus is on BD normal mode propagation through observed ocean features, such as eddies and spicy intrusions, where direct numerical simulations and the mode interaction parameter (MIP) are used to quantify ducted mode coupling strength. The observations show strong PSW sound speed variability, weak variability in the PWW, and moderate variability in the AW, with typical time scales from days to weeks. For several hundreds Hertz propagation, the BD modes are relatively stable, except for rare episodes of strong sound speed perturbations. The MIP identifies a resonance condition such that the likelihood of coupling is greatest when there is significant sound speed variability in the horizontal wave number band 1/11<kh<1/5 km-1. MITgcm ocean model results are used to estimate sound speed fluctuations in this resonance regime.

The Magmatic Rifting of Santos Basin: Aeromagnetic Mapping of Dykes, Terranes and Marginal Structures and the Interplay Between Tectonism and Volcanism

AbstractWe present a new magnetic map integrating continental and oceanic features at the Santos Basin, in order to investigate the connection between onshore and offshore tectonics and magmatism at the early stages of rifting. The magnetic and seismic data evidence the offshore continuity of the Florianópolis and Serra do Mar dyke swarms, while the Ponta Grossa Dyke Swarm is interrupted by the marginal structures. This structural relationship brings to evidence a different timing of formation of the Ponta Grossa Dyke Swarm relative to the early opening of the margin. A very high amplitude magnetic lineament ‐ the Santos Marginal Anomaly (SAMA) reaches up to 1,500 nT and trends NE‐SW, with local E‐W inflections forming the Cananéia and Guaratiba Arcuate anomalies. Our modeling suggests that the SAMA can be associated with dykes and syn‐rift extrusive rocks linked at depth to larger magmatic bodies at the proximal and necking domains. Based on the increasing width and amplitude of the magnetic anomalies oceanwards, we propose a mechanism whereby the igneous accretion (i.e., the amount of magmatism) augmented as rifting evolved from continent to ocean, creating a volcanic rift axis. This rift was segmented by crustal discontinuities that probably nucleated on preexisting rheological contrasts, forming tectonomagmatic segments during the early extensional stages. The onshore‐offshore relationships highlight that where there was an agreement between basement fabric and dyke orientation, inheritance may have influenced their emplacement, although it was not a limiting factor for their formation. The regional geodynamics of the Gondwana opening was the first order control on the emplacements of the dyke swarms, with deviations of the magmatically influenced rift by tectonic inheritance.

Real-time detection of deep-sea hydrothermal plume based on machine vision and deep learning

Recent years have witnessed an increase in applications of artificial intelligence (AI) in the detection of oceanic features with the tremendous success of deep learning. Given the unique biological ecosystems and mineral-rich deposits, the exploration of hydrothermal fields is both scientifically and commercially important. To achieve autonomous and intelligent sampling of the hydrothermal plume by using AUV, this paper proposes an innovative method for real-time plume detection based on the YOLOv5n deep learning algorithm designed with a light-weight neural network architecture to meet the requirements of embedded platforms. Ground truth labeler app LabelImg was used to generate the ground truth data from the plume dataset created by ourselves. To accurately and efficiently detect hydrothermal plumes using an embedded system, we improved the original structure of YOLOv5n in two aspects. First, SiLU activation functions in the model were replaced by ReLU activations at shallow layers and Hard-SiLU activations at deep layers to reduce the number of calculations. Second, an attention module termed Coordinate Attention (CA) was integrated into the model to improve its sensitivity to both channel and spatial features. In addition, a transfer learning training method was adopted to further improve the model’s accuracy and generalizability. Finally, we successfully deployed the proposed model in a low-cost embedded device (NVIDIA Jetson TX2 NX) by using the TensorRT inference engine. We then installed the Jetson TX2 NX into a hovering-type AUV as its vision processing unit and conducted a plume detection test in the water tank. The water tank experimental results demonstrated that the proposed method can achieve real-time onboard hydrothermal plume detection.

A Modified U-Net Model for Predicting the Sea Surface Salinity over the Western Pacific Ocean

The prediction of oceanic features is always an important issue in oceanography, where deep learning has been proven to be a useful tool. In this study, we applied the improved U-net model to predict the monthly sea surface salinity (SSS) over the western Pacific (WP) Ocean, and the model was designed to use the SSSs from six consecutive months to predict the SSS in the next month. The monthly satellite-based SSSs in 2015–2020 were used for model training, and the data collected after January 2021 were used to evaluate the model’s predictive abilities. The results showed that the predicted SSSs represented the general patterns of SSSs over the WP region. However, the small-scale features were smoothed out in the model, and the temporal variations were also not well captured, especially over the East China Sea and Yellow Sea (ECS&amp;YS) region. To further evaluate the potential of the U-net model, a more specific model was conducted for the ECS&amp;YS region (Domain 2), which successfully predicted both spatial and temporal variations in the SSSs, including the spreading and retreating of the low-salinity tongue. Based on the comparison between the two domains and sensitivity experiments, we found that the prediction biases were contributed by the spatial distributions of the SSSs, the domain size, and the filter numbers. In addition, further multi-step prediction experiments suggested that our U-net model could also be used for long-time prediction, and we have examined up to five months. Overall, this study demonstrated the great ability and potential of the U-net model for predicting SSS, even though only a few trainable data are available.

Unsupervised time–frequency environment perception model for underwater vehicle in irregular ocean

Dynamic ocean environments are difficult to monitor and affect the motion balance of underwater vehicles. Existing unsupervised environment perception methods have poor performance and less consideration of environment and vehicle interaction effects. This paper proposes a novel unsupervised time–frequency environment perception model for underwater vehicles in the irregular ocean. First, the observed ocean information is transformed into the time–frequency space based on the wavelet transform. After that, an adaptive dual-threshold diagnosis model is proposed to automatically extract and locate ocean features and depress the background noise. Then, the environment-vehicle interaction model is derived to quantitatively predict the state change of underwater vehicles caused by environmental features. Finally, extensive experiments on the novel buoyancy-driven underwater glider and publicly South China Sea dataset are conducted to verify the effectiveness of the proposed method. The result shows that the diagnosis model can better perceive the ocean environment, and the performance outperforms traditional unsupervised statistical and supervised learning methods. The thermocline detection accuracy is 100%, and the location accuracy is 98.73%. The environment-vehicle interaction model can accurately describe the state changes of underwater vehicles under environmental influence, with an average accuracy of 94.88%.

Influence of land-sea breeze on PM_{2.5} prediction in central and southern Taiwan using composite neural network

PM_{2.5} prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM_{2.5}.

Improved Large-Scale Ocean Wave Dynamics Remote Monitoring Based on Big Data Analytics and Reanalyzed Remote Sensing

Oceans and large water bodies have the potential to generate a large amount of green and renewable energy by harvesting the ocean surface properties like wind waves and tidal waves using Wave Energy Converter (WEC) devices. Although the oceans have this potential, very little ocean energy is harvested because of improper planning and implementation challenges. Besides this, monitoring ocean waves is of immense importance as several ocean-related calamities could be prevented. Also, the ocean serves as the maritime transportation route. Therefore, a need exists for remote and continuous monitoring of ocean waves and preparing strategies for different situations. Remote sensing technology could be utilized for a large scale low-cost opportunity for monitoring entire ocean bodies and extracting several important ocean surface features like wave height, wave time period, and drift velocities that can be used to estimate the ideal locations for power generation and find locations for turbulent waters so that maritime transportation hazards could be prevented. To process this large volume of data, Big Data Analytics techniques have been used to distribute the workload to worker nodes, facilitating a fast calculation of the reanalyzed remote sensing data. The experiment was conducted on Indian Coastline. The findings from the experiment show that a total of 1.86 GWh energy can be harvested from the ocean waves of the Indian Coastline, and locations of turbulent waters can be predicted in real-time to optimize maritime transportation routes.

Characterizing the deep sound scattering layer with a moored upward-looking broadband split-beam echosounder

The deep scattering layer (DSL) is a ubiquitous feature of the global ocean. It consists of a large community of mesopelagic organisms which links the marine food web and has recently garnered much interest from commercial fisheries. Such biological communities are inherently coupled with oceanic physical processes such as mesoscale eddies, internal waves and boundary currents. However, very little is known about the physical consequences and biological responses in this coupled system. To address these questions, an upward-looking broadband split-beam echosounder (36kHz – 44 kHz) was moored at ∼580 m depth (bottom depth ∼2790 m) in the New England slope waters in July 2021. The time series analysis focuses on (1) volume scattering strength and spectrum shape and (2) target strength, layer density and compositions, and individual behavior in three dimensions. We demonstrate the seasonal variability in the biomass, distribution, and vertical migration patterns of the deep scattering layer, and quantify the temporal and spatial variability of the DSL under the influence of multiple oceanic physical processes, including warm core rings, semidiurnal internal tides, and near-inertial waves.

The Effect of Currents on Large Surface Gravity Waves Under Cyclonic Conditions in the South/Southeastern Brazil

AbstractAlthough ocean surface currents are not accounted for in most wave modeling studies, recent works have demonstrated that ocean circulation features may have a great impact on surface gravity waves. In this case study, we coupled in a one‐way mode the hydrodynamic model Regional Ocean Modeling System (ROMS) and the wave model Simulating WAves Nearshore (SWAN) to study the effect of currents on large wave events (LWEs) produced under the influence of cyclones in south/southeastern Brazil. For the five LWEs studied, chosen due to the distinct importance that current‐to‐wave effects had for the period, the results indicate that the addition of currents on the wave model improved the accuracy of the peaks of significant wave height, generally increasing them (by as much as 27%). The increase in wave height occurred mainly near the shelf edge and slope, where the southwestward‐flowing Brazil Current opposes the dominant direction of cyclone‐generated waves (usually from S/SE to S/SW). Modulation of wave height by currents in the subinertial band explained up to 95% of the total variance of the difference between results with and without ocean currents, and this band was largely related to the Brazil Current. Its near‐inertial counterpart explained up to 22% of the total variance and was primarily due to near‐inertial currents in the region. This study provides, for the first time, conclusive evidence that wave‐current interaction can impact LWEs in south/southeastern Brazil.

Climate‐influenced shifts in a highly migratory species recreational fishery

AbstractThe distribution of marine species is changing as a direct result of climate change. Large pelagic highly migratory species (HMS), like tunas, billfishes, and sharks, are particularly sensitive to environmental change due to their migratory nature and use of large‐scale ocean features. These temporal and spatial shifts are likely reflected in the Atlantic HMS recreational fishery and could have downstream effects on coastal communities. We utilized dockside intercept data from the Large Pelagics Survey (LPS) from 2002 to 2019, which conducts sampling from June to October and spans Maine to Virginia, USA. We assessed how species catch composition has changed and developed spatiotemporal models to understand latitudinal and temporal shifts in the recreational catch of 12 HMS groups. Latitudinal shifts were significantly affected by Northeast Shelf SST for 11 of the 12 HMS groups and ranged from 3 ± 1 km°C−1 for the large bluefin tuna to 40 ± 1 km°C−1 for the blue shark. In addition, the estimated day of the year when the first 25% of bigeye tuna intercepts occurred, happened over 50 days earlier in 2019 compared to 2002, suggesting the initial catch is happening earlier in recent years. These results suggest that changes in species distribution and phenology are affecting where and when HMS recreational catch occurs. Understanding these shifts would allow managers to be more responsive and flexible and also help communities prepare for changes, whether it would be a switch to a new species or shifts in the fishing season.

Revised chlorophyll-a algorithms for satellite ocean color sensors in the East/Japan Sea

The East/Japan Sea (hereafter, East Sea (ES)) is a semi-enclosed marginal sea and considered as a miniature ocean with oceanic features (e.g., currents, eddies, fronts, upwelling) and dynamic marine environments. The ES is significantly influenced by the global ocean warming with fast increases in sea surface temperature during the last several decades. The climate-induced environmental changes can affect marine ecosystem and food webs in the ES. Therefore, more accurate estimates in phytoplankton biomass in the ES with high spatial and temporal resolutions are necessary to investigate phytoplankton production, fisheries, and carbon budget as well as biological responses to the environmental and climate changes. The NASA ocean chlorophyll-type (OCx) chlorophyll-a (Chl-a) algorithms based on blue to green band ratio for the satellite ocean color data in the global ocean have been evaluated using the in situ radiometric and Chl-a measurements in the ES. The model-derived Chl-a data using the OC Chl-a algorithms present systematic overestimation in the lower Chl-a concentrations between about 0.1 and 1.0 mg/m3 in the ES (with ∼40% uncertainties). New Chl-a algorithms for the ES has been derived from the 3rd polynomial regression fits of in situ Chl-a and maximum band ratios of remote sensing reflectance (Rrs). The revised Chl-a algorithms considerably improve the Chl-a data in the ES from the satellite ocean color data in the lower Chl-a concentrations. Thus, the new Chl-a algorithms can provide more accurate assessments in biological and biogeochemical processes such as primary productivity, phytoplankton phenology, phytoplankton dynamics, and carbon cycles.