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39 results for “Ocean color”
Colored dissolved organic matter (CDOM) absorbance from lagoon, ocean, and river sites along the Alaska Beaufort Sea coast, 2021-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples from multiple depths are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, filtered, and analyzed for light absorption spectra within 24 hours of collection. Wavelength-specific light absorption coefficients are reported between 250 and 600 nanometers. The data is organized in "long" or "tidy" format, with columns of station, date, wavelength, and absorption. Please see included MakeColumnsAsWavelengths.R, MakeColumnsAsDates.R, and ReshapeDataInExcel.txt for some common ways to reorganize the table for further CDOM analysis. In 2022, data from 2019 were removed due to quality issues (please see revision 1 of this dataset for 2019 CDOM values). For users who may have used 2019 data from this dataset, there are additional files to inform decisions going forward. "BLE_LTER_CDOM_2019_sample_flags.csv" lists which 2019 samples are entirely unreliable, versus usable with caution. "BLE_LTER_CDOM_2021_blank_mean_sd_absorptions.csv" lists mean and standard deviations at each wavelength from all blanks taken in 2021; this is meant to give info on the instruments used and will not be updated further. Please see the methods section for more information on 2019 data.
BGC-Argo matchups with Ocean Color Satellite Sensors and MERRA-2 updated for 2023
<p>Updated matchup dataset as described in "Begouen Demeaux et al., Algorithms to Retrieve the Spectral Diffuse Attenuation Coefficient of Light in the Ocean from Remote Sensing, Optics Express, 2023".</p> <p>Composed of Satellites matchup from the MODIS, VIIRS and OLCI sensors with BGC-Argo floats, including Kds derived from float measurements (Kd_WV_Xing), Rrs at all wavelengths from each sensor, solar zenith angle and information on the atmospheric composition from Merra-2 matchups. </p> <p>New recomputed Kds using the Lee et al., 2005 algorithm with individual sensor coefficients are also listed (new_kd_WV_Lee_indiv), as well as recomputed Kds for a new global m2 coefficient (new_kd_WV_Lee_global). Recomputed Kds for the new coefficients of the NASA/ESA algorithm are also available (new_Kd_Aus). Lastly, Kds for the new GF algorithm depending on the MERRA inputs and IOPs is listed : (kd_WV_f). </p> <p>For any questions, do not hesitate to be in touch. </p>
Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."
<p>Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."</p> <p>The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea. This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour. We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle. This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.</p>
Fig. 5 in Distributional Range Extension of the Pale Ornate Jobfish Pristipomoides amoenus (Teleostei: Perciformes: Lutjanidae) in the Western Pacific Ocean, with Notes on Newly Recognized Diagnostic Coloration
Fig. 5. Live individuals of Pristipomoides argyrogrammicus collected from Motobu, Okinawa-jima island, Japan, and reared at Okinawa Churaumi Aquarium (photos by A. Kaneko). A, B, 200 m depth, 26 September 2019; C, 105 mm TL, juvenile, 150 m depth, 1 March 2020.
Fig. 4 in Distributional Range Extension of the Pale Ornate Jobfish Pristipomoides amoenus (Teleostei: Perciformes: Lutjanidae) in the Western Pacific Ocean, with Notes on Newly Recognized Diagnostic Coloration
Fig. 4. Distributional records of Pristipomoides amoenus. Stars and circles represent localities of specimens examined in the present and previous studies, respectively. Open symbol indicates type locality.
Fig. 3 in Distributional Range Extension of the Pale Ornate Jobfish Pristipomoides amoenus (Teleostei: Perciformes: Lutjanidae) in the Western Pacific Ocean, with Notes on Newly Recognized Diagnostic Coloration
Fig. 3. Live individual of Pristipomoides amoenus collected from Tsuken-jima island, Okinawa Islands, Japan, 300 m depth, 14 December 2019, and reared at Okinawa Churaumi Aquarium (photos by A. Kaneko). A, Lateral view; B, dorsal view.
Fig. 2 in Distributional Range Extension of the Pale Ornate Jobfish Pristipomoides amoenus (Teleostei: Perciformes: Lutjanidae) in the Western Pacific Ocean, with Notes on Newly Recognized Diagnostic Coloration
Fig. 2. Preserved specimens of (A–D) Pristipomoides amoenus and (E–H) P. argyrogrammicus. A, KAUM–I. 156091, 177.3 mm SL, Amamioshima island, Kagoshima, Japan; B, D, KAUM–I. 113361, 184.7 mm SL, Dong-gang, Pingtung, Taiwan; C, KAUM–I. 156091, 221.2 mm SL, Amami-oshima island, Kagoshima, Japan; E, KAUM–I. 139296, 141.7 mm SL, Amami-oshima island, Kagoshima, Japan; F, H, KAUM–I. 108166, 210.9 mm SL, Amami-oshima island, Kagoshima, Japan; G, KAUM–I. 51137, 277.6 mm SL, Tokara Islands, Kagoshima, Japan; D, H: dorsal view.
Fig. 1 in Distributional Range Extension of the Pale Ornate Jobfish Pristipomoides amoenus (Teleostei: Perciformes: Lutjanidae) in the Western Pacific Ocean, with Notes on Newly Recognized Diagnostic Coloration
Fig. 1. Fresh specimens of (A–C) Pristipomoides amoenus and (D–F) P. argyrogrammicus. A, KAUM–I. 156091, 177.3 mm SL, Amami-oshima island, Kagoshima, Japan; B, KAUM–I. 113361, 184.7 mm SL, Dong-gang, Pingtung, Taiwan; C, KAUM–I. 156091, 221.2 mm SL, Amami-oshima island, Kagoshima, Japan; D, KAUM–I. 139296, 141.7 mm SL, Amami-oshima island, Kagoshima, Japan; E, KAUM–I. 108166, 210.9 mm SL, Amami-oshima island, Kagoshima, Japan; F, KAUM–I. 51137, 277.6 mm SL, Tokara Islands, Kagoshima, Japan.
Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products
<p><span>Quantifying changes in phytoplankton communities using ocean color is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. Here we present a dataset of greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, fluorescence, and hyperspectral remote sensing reflectance. The measurements obtained from these experiments are valuable to facilitate development of new global and/or regional ocean color models by the broader scientific community. </span></p>
Ocean Color Data: Modis-aqua_chl-a (JJA, 2002-2018)
<p>Ocean Color Data: Modis-aqua_chl-a (JJA, 2002-2018) downloaded from the ADAM Platform (https://reliance.adamplatform.eu/) used furing the FORCeS eScience course 'Tools in Climate Science: Linking Observations with Modelling'.</p> <p> </p> <p>MODIS Chlorophyll-a Concentration This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using an empirical relationship derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs) in the blue-to-green region of the visible spectrum. The implementation is contingent on the availability three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors. The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the default chlorophyll algorithm (chlor_a) employs the standard OC3/OC4 (OCx) band ratio algorithm merged with the color index (CI) of Hu et al. (2012). As described in that paper, this refinement is restricted to relatively clear water, and the general impact is to reduce artifacts and biases in clear-water chlorophyll retrievals due to residual glint, stray light, atmospheric correction errors, and white or spectrally-linear bias errors in Rrs. As implemented, the algorithm diverges slightly from what was published in Hu et al. (2012) in that the transition between CI and OCx now occurs at 0.15 < CI < 0.2 mg/m3 to ensure a smooth transition.</p> <p> </p>
Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products
Open the record for dataset details and reuse information.
Geospatial_Ocean color
<p>This is dataset for the Geospatial class.</p> <p>Source: ECMWF</p>
FIGURE 6. Fresh coloration. A in A new species of the swimming crab genus, Laleonectes Manning & Chace, 1990 (Crustacea: Brachyura: Portunidae), from the western Indian Ocean
FIGURE 6. Fresh coloration. A, Laleonectes kuriya sp. nov., male (ZRC 2017.0335), Tamil Nadu; B, L. nipponensis (Sakai, 1938), male (RUMF-ZC-2462), Ryukyu Is.; C, L. nipponensis (Sakai, 1938), male (ZRC 2017.0009), Bohol.
Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)
<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar Müller, Eike M Schütt, Daniel Behr, Carsten Brockmann, Carole Lebreton, François Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p> </p>
Drivers of ocean iron stress variability in high nutrient-low chlorophyll regions from ocean color
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Supporting Files for Manuscript: Airfall volume of the 15 January 2022 eruption of Hunga volcano estimated from ocean color changes
<p>Supporting Files for Publication Titled: "Airfall volume of the 15 January 2022 eruption of Hunga volcano estimated from ocean color changes" Under Review in the Bulletin of Volcanology. Please cite Kelly et al. (2024) if any of the data in this repository is used.</p>
Arabian Sea bio-optical and biogeochemical data for ocean color validation
This project will collect high-quality, bio-optical, and biogeochemical data for validation of advanced satellite products from PACE OCI for the Arabian Sea, a highly under-sampled region of the worlds oceans, now experiencing dramatic ecosystem changes from human activities and climate-change. Over the past two decades, the base of the food chain of this monsoonal-driven ecosystem has transitioned from diatoms to one dominated by the mixotrophic dinoflagellate, Noctiluca scintillans (Noctiluca) that forms intense and widespread blooms visible from space. Capturing such phytoplankton transitions has been the pursuit of ocean color missions for more than three decades, and with its hyperspectral capabilities, NASAs PACE mission can now provide unprecedented insight into the response of phytoplankton communities to global pressures. Despite the dramatic rates at which the Arabian Sea has been changing, it remains among the most optically under-sampled of global water bodies. As part of this effort, we will leverage our long-standing ties with colleagues in India to collect high quality, high resolution (sub-pixel scale), continuous, underway and discrete bio-optical measurements to validate standard and advanced ocean products from PACE, essential to advance our understanding of vulnerable marine ecosystems and their response to anthropogenic change. As part of this activity, we plan to participate in one pre-monsoon cruise (2025) led by Space Applications Centre, ISRO, India, and two post-bloom ONR led cruises in April-May of 2024 and in April-May 2025. The pre-monsoon cruises are being undertaken as part of an Indo-US study focused on establishing triggers of the southwest monsoon rainfall season over the Indian sub-continent. Some of the data shared under this DOI is part of the Arabian Sea Marine environment through Science and Advanced Training (EKAMSAT) collaborative effort between the Ministry of Earth Sciences, Govt. of India and the Office of Naval Research. EKAMSAT commenced with a pilot study in June 2023. The pilot data is being archived under the SeaBASS experiment EKAMSAT_Pilot_ASTRAL (DOI: 10.5067/SeaBASS/EKAMSAT_Pilot_ASTRAL/DATA001) and can downloaded here: https://seabass.gsfc.nasa.gov/experiment/EKAMSAT_Pilot_ASTRAL.
Geostationary Coastal and Air Pollution Events measurements for Geostationary Ocean Color Imager (GOCI)
GEO-CAPE is the Geostationary Coastal and Air Pollution Events program with a focus on the Geostationary Ocean Color Imager (GOCI).
AERONET-OCEAN COLOR
The Aerosol Robotic Network (AERONET), developed to sustain atmospheric studies at various scales with measurements from worldwide distributed autonomous sun-photometers has been extended to support marine applications. This new network component called AERONET – Ocean Color (AERONET-OC), provides the additional capability of measuring the radiance emerging from the sea (i.e., water-leaving radiance) with modified sun-photometers installed on offshore platforms like lighthouses, oceanographic and oil towers. AERONET-OC is instrumental in satellite ocean color validation activities through standardized measurements a) performed at different sites with a single measuring system and protocol, b) calibrated with an identical reference source and method, and c) processed with the same code.
AERONET-OCEAN COLOR
The Aerosol Robotic Network (AERONET), developed to sustain atmospheric studies at various scales with measurements from worldwide distributed autonomous sun-photometers has been extended to support marine applications. This new network component called AERONET – Ocean Color (AERONET-OC), provides the additional capability of measuring the radiance emerging from the sea (i.e., water-leaving radiance) with modified sun-photometers installed on offshore platforms like lighthouses, oceanographic and oil towers. AERONET-OC is instrumental in satellite ocean color validation activities through standardized measurements a) performed at different sites with a single measuring system and protocol, b) calibrated with an identical reference source and method, and c) processed with the same code.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.