Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea

Park, Jinku; Lee, Sung Jae; Jo, Young‐Heon; Kim, Hyun Cheol

doi:10.3390/rs13204035

Cited by 3 publications

(6 citation statements)

References 48 publications

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“…Our findings demonstrate that Chl-a is the most extensively researched WQP (Figure 6c), attracting a lot of attention [22,[25][26][27]29,34,36,40,44,53,65,75,159,[163][164][165][166]169,[176][177][178][179]181,183,184,186,[190][191][192][193]200,[202][203][204]209,210,212,213,215,216,[219][220][221]223,224,[228][229][230][231]…”

Section: Water Quality Parametersmentioning

confidence: 64%

“…Notable sensors specifically designed for water color products or water quality measurements, such as the ones included in this review: MODIS [32,50,157,158,166,169,189,191,199,202,210,212,214,218,227,230,232,234,240,241,254,256,257,278], Medium Resolution Imaging Spectrometer (MERIS) [26,46,157,186,191,241], OLCI [21,26,27,33,35,39,41,43,155,176,180,190,194,200,204,208,218,226,231,…”

Section: Satellite Image Data Quality and Sensor Choicementioning

confidence: 99%

“…There was also considerable focus on studying oceanic water areas that primarily encompass open ocean regions [22,35,40,50,53,168,176,178,185,189,190,192,200,202,203,207,[228][229][230]232] (Figure 6d). These regions are characterized by extensive water circulation patterns which may pose unique challenges for remote sensing applications.…”

Section: The Water Quality Classesmentioning

confidence: 99%

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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Section: Water Quality Parametersmentioning

confidence: 64%

Section: Satellite Image Data Quality and Sensor Choicementioning

confidence: 99%

Section: The Water Quality Classesmentioning

confidence: 99%

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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“…van Bemmelen (2019) found similar spatial variability in red phalaropes migrating from Svalbard and Greenland through the Atlantic Ocean with individuals using 3 separate migration pathways and wintering areas. Furthermore, individuals migrating along common routes did not use the same stopover areas, although we did identify several population-level high-use Note that chl a values (mg m -3 ) estimated with satellite data may be upwards biased in nearshore areas, especially in areas with high turbidity from river inputs such as along the Alaska coastline (Chaves et al 2015, Park et al 2021 areas (often in highly productive areas) where multiple individuals stopped, presumably to forage (Fig. 7).…”

Section: Temporal and Spatial Variability In Southward Migration Beha...mentioning

confidence: 88%

“…9). However, chl a concentration values, as estimated with satellite data, may be upward biased in nearshore areas, especially in areas with high turbidity from river inputs such as along the Alaska coastline (Chaves et al 2015, Park et al 2021.…”

Section: Oceanographic Conditions By Regionmentioning

confidence: 99%

From land to sea: the fall migration of the red phalarope through the Western Hemisphere

Saalfeld,

Valcu,

Brown

et al. 2024

Mar. Ecol. Prog. Ser.

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Understanding how and where individuals migrate between breeding and wintering areas is important for assessing threats, identifying important areas for conservation, and determining a species’ vulnerability to changing environmental conditions. Between 2017 and 2020, we tracked post-breeding movements of 72 red phalaropes Phalaropus fulicarius with satellite tags from 7 Arctic-breeding sites in the Alaskan and Central Canadian Arctic. All tracked red phalaropes left their Arctic breeding grounds (i.e. were obligate migrants) but then switched to a more facultative migration strategy with a fly-and-forage migration pattern once in the marine environment. We documented high variability in migration timing and routes, with birds often taking indirect, circuitous routes with numerous stops that greatly lengthened both the duration and distance of their southward migration. Across nearly 500 stopover areas, which were often associated with areas of presumed greater food availability, individuals spent an average of 6 d and traveled within an average area of 1880 km2. Stopover areas were concentrated in onshore and nearshore habitats of the Beaufort and Chukchi seas, the western edge of the Bering Strait, along the Alaska Peninsula and Aleutian Islands, and near the Pribilof Islands in Alaska. Within the Beaufort and Chukchi seas, females frequently stopped within the marginal ice zone, whereas males tended to stay on land or in open water. Our results identified important marine areas that can aid future conservation and management decisions. However, conservation of the species will also need to address the numerous direct and indirect anthropogenic threats red phalaropes experience at sea, many of which are not site-specific.

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Arctic marine ecosystems face increasing climate stress

Deb

Bailey

2023

Environ. Rev.

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Arctic warming is occurring at a much faster rate than in other parts of the globe, with potentially devastating consequences for the diverse array of species within ecologically and biologically sensitive areas in the Arctic marine region. However, climate change research in this region is sparse compared to other biomes, hindering conservation efforts. In this article, we review and synthesize the available literature to understand the observed and potential impacts of climate change on different species and ecosystems in the Arctic marine region. We reviewed 253 articles reporting changes in species and ecosystems in the Arctic marine region in response to climate change (225 studies documenting observed impacts and 28 reporting predicted impacts). The review revealed that most research effort has been concentrated in only a subset of Arctic Large Marine Ecosystems (LMEs) (5/18 LMEs), with the majority of Arctic LMEs being poorly studied. The majority of the reviewed papers focused on marine mammals (19%) followed by microalgae (17%). A number of studies documented variability in planktonic communities (microalgae, macroalgae, zooplankton), resulting in alteration in ecosystem structure and function. Reproductive failure, decline in populations, and changes in diet composition, behavior and breeding biology were reported for sea birds and mammals. Further, shifts in spatial distribution of fishes were observed by several studies. This review provides persuasive evidence that multiple climate change indices in the Arctic region are changing, with impacts on almost all components of marine biotic ecosystems (from plankton to top predators). We identify a number of gaps (such as limited inventory of biota, robustness of data supporting climate change indices on an Arctic Ocean scale, and climate suitability assessment for all species) in climate change research in Arctic marine ecosystems where future studies can help to quantify the impacts of climate change on species and ecosystems and advance understanding for their adaptive management.

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Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea

Cited by 3 publications

References 48 publications

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

From land to sea: the fall migration of the red phalarope through the Western Hemisphere

Arctic marine ecosystems face increasing climate stress

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