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  • 1
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    Global surface ocean HPLC phytoplankton pigments and hyperspectral remote sensing reflectance (2021)
    PANGAEA
    Details
    Publication Date: 2024-05-11
    Description: This dataset is a global surface ocean compilation of high-performance liquid chromatography (HPLC) phytoplankton pigment concentrations and hyperspectral remote sensing reflectance (Rrs) data, with associated temperature and salinity measurements. The pigments measured include: total chlorophyll-a (Tchla), 19'-hexanoyloxyfucoxanthin (HexFuco), 19'-butanoyloxyfucoxanthin (ButFuco), alloxanthin (Allo), fucoxanthin (Fuco), peridinin (Perid), zeaxanthin (Zea), divinyl chlorophyll a (DVchla), monovinyl chlorophyll b (MVchlb), chlorophyll c1+c2 (Chlc12), chlorophyll c3 (Chlc3), neoxanthin (Neo), and violaxanthin (Viola). Rrs data are measured at 1 nm spectral resolution from 400-700 nm. The Rrs data from the ANT cruises were collected using a RAMSES hyperspectral radiometer, the Rrs data from the NAAMES, SABOR, Tara, RemSensPOC, BIOSOPE, and EXPORTS cruises were generated by a HyperPro (Satlantic, Inc.) hyperspectral radiometer. All samples presented in this dataset have previously been published and are publicly available, as referenced in the table: ANT: Bracher et al. (2015), https://doi.org/10.1594/PANGAEA.847820, NAAMES: Behrenfeld et al. (2014a), http://dx.doi.org/10.5067/SeaBASS/NAAMES/DATA001, Remote Sensing of POC: Cetinić (2013), http://dx.doi.org/10.5067/SeaBASS/REMSENSPOC/DATA001, SABOR: Behrenfeld et al. (2014b), http://dx.doi.org/10.5067/SeaBASS/SABOR/DATA001, Tara Oceans: Boss and Claustre (2009), http://dx.doi.org/10.5067/SeaBASS/TARA_OCEANS_EXPEDITION/DATA001, Tara Mediterranean: Boss and Claustre (2014), http://dx.doi.org/10.5067/SeaBASS/TARA_MEDITERRANEAN/DATA001, BIOSOPE: Claustre and Sciandra (2004), https://doi.org/10.17600/4010100 hosted at http://www.obs-vlfr.fr/proof/php/bio_open_access_data.php, EXPORTS: Behrenfeld et al. (2018), http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001. This compilation of these data is used in Kramer et al. (2021) to evaluate a model that reconstructs pigment concentrations from hyperspectral remote sensing reflectance.
    Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; Alloxanthin; alpha-Carotene + beta-Carotene; Campaign; Chlorophyll a, total; Chlorophyll b + divinyl chlorophyll b; Chlorophyll c1+c2; Chlorophyll c1+c2+c3; Chlorophyll c3; Chlorophyllide a; CTD; DATE/TIME; DEPTH, water; Diadinoxanthin; Diatoxanthin; Divinyl chlorophyll a; Divinyl chlorophyll b; Fucoxanthin; global compilation; High Performance Liquid Chromatography (HPLC); HPLC pigments; Hyperspectral radiometer; LATITUDE; LONGITUDE; Lutein; Monovinyl chlorophyll a; Monovinyl chlorophyll b; Neoxanthin; ocean color; Peridinin; Phaeophorbide a; Phaeophytin; phytoplankton pigments; Prasinoxanthin; Principal investigator; remote sensing reflectance; Remote sensing reflectance at 400 nm; Remote sensing reflectance at 401 nm; Remote sensing reflectance at 402 nm; Remote sensing reflectance at 403 nm; Remote sensing reflectance at 404 nm; Remote sensing reflectance at 405 nm; Remote sensing reflectance at 406 nm; Remote sensing reflectance at 407 nm; Remote sensing reflectance at 408 nm; Remote sensing reflectance at 409 nm; Remote sensing reflectance at 410 nm; Remote sensing reflectance at 411 nm; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 413 nm; Remote sensing reflectance at 414 nm; Remote sensing reflectance at 415 nm; Remote sensing reflectance at 416 nm; Remote sensing reflectance at 417 nm; Remote sensing reflectance at 418 nm; Remote sensing reflectance at 419 nm; Remote sensing reflectance at 420 nm; Remote sensing reflectance at 421 nm; Remote sensing reflectance at 422 nm; Remote sensing reflectance at 423 nm; Remote sensing reflectance at 424 nm; Remote sensing reflectance at 425 nm; Remote sensing reflectance at 426 nm; Remote sensing reflectance at 427 nm; Remote sensing reflectance at 428 nm; Remote sensing reflectance at 429 nm; Remote sensing reflectance at 430 nm; Remote sensing reflectance at 431 nm; Remote sensing reflectance at 432 nm; Remote sensing reflectance at 433 nm; Remote sensing reflectance at 434 nm; 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    Type: Dataset
    Format: text/tab-separated-values, 47995 data points
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    DATA PANGAEA
  • 2
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    Evaluation of accuracy and precision in an amplicon sequencing workflow for marine protist communities (2019)
    Wiley
    Details
    Publication Date: 2019-12-18
    Electronic ISSN: 1541-5856
    Topics: Biology , Geosciences
    Published by Wiley on behalf of Association for the Sciences of Limnology and Oceanography.
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  • 3
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    Marine diatom proteorhodopsins and their potential role in coping with low iron availability (2015)
    Springer Nature
    Details
    Publication Date: 2015-05-29
    Print ISSN: 1751-7362
    Electronic ISSN: 1751-7370
    Topics: Biology
    Published by Springer Nature
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  • 4
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    California Wildfire Burns Boundaries Between Science and Art (2020)
    The Oceanography Society
    Details
    Publication Date: 2020-03-01
    Print ISSN: 1042-8275
    Electronic ISSN: 2377-617X
    Topics: Geosciences
    Published by The Oceanography Society
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  • 5
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    Satellite detection of dinoflagellate blooms off California by UV reflectance ratios (2021)
    University of California Press
    Details
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kahru, M., Anderson, C., Barton, A. D., Carter, M. L., Catlett, D., Send, U., Sosik, H. M., Weiss, E. L., & Mitchell, B. G. Satellite detection of dinoflagellate blooms off California by UV reflectance ratios. Elementa: Science of the Anthropocene, 9(1), (2021): 00157, https://doi.org/10.1525/elementa.2020.00157.
    Description: As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios 〈1 of remote sensing reflectance of the UV band at 380 nm to that of the blue band at 443 nm were used as an indicator of the dinoflagellate bloom. The satellite data indicated that an observed, long, and narrow nearshore band of elevated chlorophyll-a (Chl-a) concentrations, extending from northern Baja to Santa Monica Bay, was dominated by L. polyedra. In other high Chl-a regions, the ratios were 〉1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales.
    Description: Part of this work was funded by National Science Foundation (NSF) grants to the CCE-LTER Program, most recently OCE-1637632. Processing of Second-Generation Global Imager satellite data was funded by Japan Aerospace Exploration Agency. Data shown in Figure 1 were collected by BGM and MK with support from the NASA SIMBIOS project. DC was supported by the NASA Biodiversity and Ecological Forecasting Program (Grant NNX14AR62A), the Bureau of Ocean and Energy Management Ecosystem Studies Program (BOEM award MC15AC00006), and the NOAA through the Santa Barbara Channel Marine Biodiversity Observation Network. HMS was supported by NSF (Grant OCE-1810927) and the Simons Foundation (Grant 561126). ELW was supported by NSF GRFP (Grant DGE-1650112). Funding for Scripps and Santa Monica Piers sampling was through the Southern California Coastal Ocean Observing Harmful Algal Bloom Monitoring Program by NOAA NA16NOS0120022.
    Keywords: Remote sensing ; Ocean color ; Dinoflagellates ; Harmful algal blooms ; California Current Ecosystem ; Plankton
    Repository Name: Woods Hole Open Access Server
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  • 6
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    Satellite remote sensing and the Marine Biodiversity Observation Network: current science and future steps (2022)
    Oceanography Society
    Details
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kavanaugh, M. T., Bell, T., Catlett, D. C., Cimino, M. A., Doney, S. C., Klajbor, W., Messie, M., Montes, E., Muller-Karger, F. E., Otis, D., Santora, J. A., Schroeder, I. D., Trinanes, J., & Siegel, D. A. Satellite remote sensing and the Marine Biodiversity Observation Network: current science and future steps. Oceanography, 34(2), (2021): 62–79, https://doi.org/10.5670/oceanog.2021.215.
    Description: Coastal ecosystems are rapidly changing due to human-caused global warming, rising sea level, changing circulation patterns, sea ice loss, and acidification that in turn alter the productivity and composition of marine biological communities. In addition, regional pressures associated with growing human populations and economies result in changes in infrastructure, land use, and other development; greater extraction of fisheries and other natural resources; alteration of benthic seascapes; increased pollution; and eutrophication. Understanding biodiversity is fundamental to assessing and managing human activities that sustain ecosystem health and services and mitigate humankind’s indiscretions. Remote-sensing observations provide rapid and synoptic data for assessing biophysical interactions at multiple spatial and temporal scales and thus are useful for monitoring biodiversity in critical coastal zones. However, many challenges remain because of complex bio-optical signals, poor signal retrieval, and suboptimal algorithms. Here, we highlight four approaches in remote sensing that complement the Marine Biodiversity Observation Network (MBON). MBON observations help quantify plankton community composition, foundation species, and unique species habitat relationships, as well as inform species distribution models. In concert with in situ observations across multiple platforms, these efforts contribute to monitoring biodiversity changes in complex coastal regions by providing oceanographic context, contributing to algorithm and indicator development, and creating linkages between long-term ecological studies, the next generations of satellite sensors, and marine ecosystem management.
    Description: The authors would like to acknowledge the support of the Marine Biodiversity Observation Network (MBON), through National Aeronautics and Space Administration (NASA) awards NNX14AP62A, 80NSSC20K0017MK, NNX14AR62AFMK, 80NSSC20M0001, and 80NSSC20M008; and National Oceanic and Atmospheric Administration (NOAA) Integrated Ocean Observing System grant NA19NOS0120199. In addition, the work was supported by the Group on Earth Observations NASA awards 80NSSC18K0318 to EM and 80NSSC18K0412 to MK. FMK acknowledges the US National Science Foundation (NSF) grant 2500-1710-00 to the OceanObs Research Coordination Network, and the Gulf of Mexico Coastal Ocean Observing System NOAA Cooperative Agreement NA16NOS0120018. MM and JS were also supported by the NASA Life in Moving Oceans award 80NSSC17K0574. DS, TB, and DC acknowledge Plumes and Blumes NASA award 80NSSC18K0735, the Bureau of Ocean and Energy Management Ecosystem Studies program award MC15AC00006, NASA PACE Science Team award 80NSSC20M0226, and NSF Santa Barbara Coastal Long Term Ecological Research site award OCE-1831937.
    Repository Name: Woods Hole Open Access Server
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