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  • 1
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    Unknown
    Optical Society of America
    In:  Applied Optics, 41 (15). p. 2705.
    Publication Date: 2016-01-11
    Description: Semianalytical (SA) ocean color models have advantages over conventional band ratio algorithms in that multiple ocean properties can be retrieved simultaneously from a single water-leaving radiance spectrum. However, the complexity of SA models has stalled their development, and operational implementation as optimal SA parameter values are hard to determine because of limitations in development data sets and the lack of robust tuning procedures. We present a procedure for optimizing SA ocean color models for global applications. The SA model to be optimized retrieves simultaneous estimates for chlorophyll (Chl) concentration, the absorption coefficient for dissolved and detrital materials [a cdm(443)], and the particulate backscatter coefficient [b bp(443)] from measurements of the normalized water-leaving radiance spectrum. Parameters for the model are tuned by simulated annealing as the global optimization protocol. We first evaluate the robustness of the tuning method using synthetic data sets, and we then apply the tuning procedure to an in situ data set. With the tuned SA parameters, the accuracy of retrievals found with the globally optimized model (the Garver-Siegel-Maritorena model version 1; hereafter GSM01) is excellent and results are comparable with the current Sea-viewing Wide Field-of-view sensor (SeaWiFS) algorithm for Chl. The advantage of the GSM01 model is that simultaneous retrievals of a cdm(443) and b bp(443) are made that greatly extend the nature of global applications that can be explored. Current limitations and further developments of the model are discussed.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2024-04-20
    Description: Monthly global 4km satellite products spanning September 1997 to December 2020. The data contains Particle Size Distribution (PSD) parameters of an assumed power-law PSD, absolute and fractional size-partitioned phytoplankton carbon and associated variables such as particulate organic carbon (POC) and Chlorophyll-a as derived from the PSD algorithm. The retrieval is based on a backscattering bio-optical model using two particle populations and coated spheres for phytoplankton inherent optical properties (IOP) modeling, and a retrieval using spectral angle mapping (SAM - where satellite spectra are classified using a comparison to a collection of modeled end-member spectra, by treating spectra as vectors and using their dot product). Partial uncertainties are given as standard deviation and are estimated using a combination of Monte Carlo simulations and analytical error propagation. An empirical tuning factor is given for attaining more realistic estimated model concentrations of POC and Chlorophyll-a. The tuning factor is multiplicative, to be applied in linear space. This tuning factor has not been applied to the monthly data, users can choose whether or not to apply it to absolute carbon and Chlorophyll-a concentrations. The factor does not affect retrievals of fractional contributions of phytoplankton size classes to total phytoplankton carbon. Monthly climatologies files and an overall climatology file are also provided, and in those files, both untuned (tuning factor not applied) and tuned (tuning factor applied) variables are provided, for user convenience. Input remote-sensing reflectance data are v5.0 of the Ocean Colour -Climate Change Initiative (OC-CCI) of the European Space Agency. The OC-CCI general reference is Sathyendranath et al. (2019; doi:10.3390/s19194285), and for v5.0 of the dataset, the reference is Sathyendranath et al. (2021; doi:10.5285/1dbe7a109c0244aaad713e078fd3059a). More detailed metadata, including geospatial metadata, are given in the netCDF files. Variable names should be self-explanatory. Quick browse images are provided as well. Coastlines in these quick browse images are from v2.3.7 of the GSHHS data set - see Wessel and Smith (1996) (doi:10.1029/96JB00104). Modeling and data processing was done in MATLAB ®.
    Keywords: Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); coated spheres; Comment; equivalent algal populations; Image; MATLAB ® - modeling and processing; Mie theory; OC-CCI; ocean color; ocean colour; Particle size distribution; Phytoplankton; phytoplankton carbon; phytoplankton functional types; phytoplankton size classes
    Type: Dataset
    Format: text/tab-separated-values, 880 data points
    Location Call Number Limitation Availability
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  • 3
    Publication Date: 2024-05-11
    Keywords: File content; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 6 data points
    Location Call Number Limitation Availability
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  • 4
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    Unknown
    PANGAEA
    In:  Supplement to: Nechad, Bouchra; Ruddick, Kevin; Schroeder, Thomas; Oubelkheir, Kadija; Blondeau-Patissier, David; Cherukuru, Nagur; Brando, Vittorio E; Dekker, Arnold G; Clementson, Lesley; Banks, Andrew; Maritorena, Stéphane; Werdell, P Jeremy; Sá, Carolina; Brotas, Vanda; Caballero de Frutos, Isabel; Ahn, Yu-Hwan; Salama, Suhyb; Tilstone, Gavin; Martinez-Vicente, Victor; Foley, David; McKibben, Morgaine; Nahorniak, Jasmine; Peterson, Tawnya D; Siliò-Calzada, Ana; Röttgers, Rüdiger; Lee, Zhongping; Peters, Marco (2015): CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters. Earth System Science Data, 7(2), 319-348, https://doi.org/10.5194/essd-7-319-2015
    Publication Date: 2024-05-11
    Description: The CoastColour project Round Robin (CCRR) project (http://www.coastcolour.org) funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). The datasets mainly consisted of 6,484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll-a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5,000 simulations) and from 3,054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Limitation Availability
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  • 5
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    Unknown
    PANGAEA
    In:  Supplement to: Casey, Kimberly A; Rousseaux, Cecile S; Gregg, Watson W; Boss, Emmanuel; Chase, Alison P; Craig, Susanne E; Mouw, Colleen B; Reynolds, Rick A; Stramski, Dariusz; Ackleson, Steven G; Bricaud, Annick; Schaeffer, Blake; Lewis, Marlon R; Maritorena, Stéphane (2020): A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications. Earth System Science Data, 12(2), 1123-1139, https://doi.org/10.5194/essd-12-1123-2020
    Publication Date: 2024-05-11
    Description: Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) - Next Generation airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries and oceans. Specific in situ measurements include coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering, as well as remote sensing reflectance, and irradiance reflectance.
    Keywords: Date/time end; Date/time start; File content; File format; File name; File size; GlobCover; LATITUDE; LONGITUDE; Reference/source; Uniform resource locator/link to file; Uniform resource locator/link to reference
    Type: Dataset
    Format: text/tab-separated-values, 628 data points
    Location Call Number Limitation Availability
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  • 6
    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; Remote sensing reflectance at 435 nm; Remote sensing reflectance at 436 nm; Remote sensing reflectance at 437 nm; Remote sensing reflectance at 438 nm; Remote sensing reflectance at 439 nm; Remote sensing reflectance at 440 nm; Remote sensing reflectance at 441 nm; Remote sensing reflectance at 442 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 444 nm; Remote sensing reflectance at 445 nm; Remote sensing reflectance at 446 nm; Remote sensing reflectance at 447 nm; Remote sensing reflectance at 448 nm; Remote sensing reflectance at 449 nm; Remote sensing reflectance at 450 nm; Remote sensing reflectance at 451 nm; Remote sensing reflectance at 452 nm; Remote sensing reflectance at 453 nm; Remote sensing reflectance at 454 nm; Remote sensing reflectance at 455 nm; Remote sensing reflectance at 456 nm; Remote sensing reflectance at 457 nm; Remote sensing reflectance at 458 nm; Remote sensing reflectance at 459 nm; Remote sensing reflectance at 460 nm; Remote sensing reflectance at 461 nm; Remote sensing reflectance at 462 nm; Remote sensing reflectance at 463 nm; Remote sensing reflectance at 464 nm; Remote sensing reflectance at 465 nm; Remote sensing reflectance at 466 nm; Remote sensing reflectance at 467 nm; Remote sensing reflectance at 468 nm; Remote sensing reflectance at 469 nm; Remote sensing reflectance at 470 nm; Remote sensing reflectance at 471 nm; Remote sensing reflectance at 472 nm; Remote sensing reflectance at 473 nm; Remote sensing reflectance at 474 nm; Remote sensing reflectance at 475 nm; Remote sensing reflectance at 476 nm; Remote sensing reflectance at 477 nm; Remote sensing reflectance at 478 nm; Remote sensing reflectance at 479 nm; Remote sensing reflectance at 480 nm; Remote sensing reflectance at 481 nm; Remote sensing reflectance at 482 nm; Remote sensing reflectance at 483 nm; Remote sensing reflectance at 484 nm; Remote sensing reflectance at 485 nm; Remote sensing reflectance at 486 nm; Remote sensing reflectance at 487 nm; Remote sensing reflectance at 488 nm; Remote sensing reflectance at 489 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 491 nm; Remote sensing reflectance at 492 nm; Remote sensing reflectance at 493 nm; Remote sensing reflectance at 494 nm; Remote sensing reflectance at 495 nm; Remote sensing reflectance at 496 nm; Remote sensing reflectance at 497 nm; Remote sensing reflectance at 498 nm; Remote sensing reflectance at 499 nm; Remote sensing reflectance at 500 nm; Remote sensing reflectance at 501 nm; Remote sensing reflectance at 502 nm; Remote sensing reflectance at 503 nm; Remote sensing reflectance at 504 nm; Remote sensing reflectance at 505 nm; Remote sensing reflectance at 506 nm; Remote sensing reflectance at 507 nm; Remote sensing reflectance at 508 nm; Remote sensing reflectance at 509 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 511 nm; Remote sensing reflectance at 512 nm; Remote sensing reflectance at 513 nm; Remote sensing reflectance at 514 nm; Remote sensing reflectance at 515 nm; Remote sensing reflectance at 516 nm; Remote sensing reflectance at 517 nm; Remote sensing reflectance at 518 nm; Remote sensing reflectance at 519 nm; Remote sensing reflectance at 520 nm; Remote sensing reflectance at 521 nm; Remote sensing reflectance at 522 nm; Remote sensing reflectance at 523 nm; Remote sensing reflectance at 524 nm; Remote sensing reflectance at 525 nm; Remote sensing reflectance at 526 nm; Remote sensing reflectance at 527 nm; Remote sensing reflectance at 528 nm; Remote sensing reflectance at 529 nm; Remote sensing reflectance at 530 nm; Remote sensing reflectance at 531 nm; Remote sensing reflectance at 532 nm; Remote sensing reflectance at 533 nm; Remote sensing reflectance at 534 nm; Remote sensing reflectance at 535 nm; Remote sensing reflectance at 536 nm; Remote sensing reflectance at 537 nm; Remote sensing reflectance at 538 nm; Remote sensing reflectance at 539 nm; Remote sensing reflectance at 540 nm; Remote sensing reflectance at 541 nm; Remote sensing reflectance at 542 nm; Remote sensing reflectance at 543 nm; Remote sensing reflectance at 544 nm; Remote sensing reflectance at 545 nm; Remote sensing reflectance at 546 nm; Remote sensing reflectance at 547 nm; Remote sensing reflectance at 548 nm; Remote sensing reflectance at 549 nm; Remote sensing reflectance at 550 nm; Remote sensing reflectance at 551 nm; Remote sensing reflectance at 552 nm; Remote sensing reflectance at 553 nm; Remote sensing reflectance at 554 nm; Remote sensing reflectance at 555 nm; Remote sensing reflectance at 556 nm; Remote sensing reflectance at 557 nm; Remote sensing reflectance at 558 nm; Remote sensing reflectance at 559 nm; Remote sensing reflectance at 560 nm; Remote sensing reflectance at 561 nm; Remote sensing reflectance at 562 nm; Remote sensing reflectance at 563 nm; Remote sensing reflectance at 564 nm; Remote sensing reflectance at 565 nm; Remote sensing reflectance at 566 nm; Remote sensing reflectance at 567 nm; Remote sensing reflectance at 568 nm; Remote sensing reflectance at 569 nm; Remote sensing reflectance at 570 nm; Remote sensing reflectance at 571 nm; Remote sensing reflectance at 572 nm; Remote sensing reflectance at 573 nm; Remote sensing reflectance at 574 nm; Remote sensing reflectance at 575 nm; Remote sensing reflectance at 576 nm; Remote sensing reflectance at 577 nm; Remote sensing reflectance at 578 nm; Remote sensing reflectance at 579 nm; Remote sensing reflectance at 580 nm; Remote sensing reflectance at 581 nm; Remote sensing reflectance at 582 nm; Remote sensing reflectance at 583 nm; Remote sensing reflectance at 584 nm; Remote sensing reflectance at 585 nm; Remote sensing reflectance at 586 nm; Remote sensing reflectance at 587 nm; Remote sensing reflectance at 588 nm; Remote sensing reflectance at 589 nm; Remote sensing reflectance at 590 nm; Remote sensing reflectance at
    Type: Dataset
    Format: text/tab-separated-values, 47995 data points
    Location Call Number Limitation Availability
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  • 7
    Publication Date: 2024-05-11
    Keywords: Chlorophyll a; Comment; DATE/TIME; DEPTH, water; Gravimetric analysis (GF/F filtered); High Performance Liquid Chromatography (HPLC); LATITUDE; LONGITUDE; Radiance reflectance, water leaving at 412.5 nm; Radiance reflectance, water leaving at 442.5 nm; Radiance reflectance, water leaving at 490 nm; Radiance reflectance, water leaving at 510 nm; Radiance reflectance, water leaving at 560 nm; Radiance reflectance, water leaving at 620 nm; Radiance reflectance, water leaving at 665 nm; Radiance reflectance, water leaving at 681.25 nm; Radiance reflectance, water leaving at 708.75 nm; Reference of data; Sample ID; Suspended matter, total
    Type: Dataset
    Format: text/tab-separated-values, 4527 data points
    Location Call Number Limitation Availability
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