Description: Author Posting. © Oceanography Society, 2004. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 17, 2 (2004): 68-75.

Description: Satellite-sensed ocean optical features are derived from a combination of factors, including the concentration of water properties by the ocean circulation and the modifications of these properties by biological and chemical processes. To demonstrate the role of the ocean circulation, we consider a case study on the upwelling of anomalously cold, nutrient-rich water and a related sea-surface temperature pattern that was observed on the West Florida Shelf. We use in situ data and a numerical model simulation to show how this pattern evolved in time and space and to draw a connection between a narrow band of cold water observed at the coast and the origin of this water a few hundred kilometers away at the shelf break via shoreward and southward advection within the bottom frictional (Ekman) boundary layer.

Description: Support was provided by the Office of Naval Research, grant N00014-98-1-1058 as part of the HyCODE Program.

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Melin, F., Moore, T. S., Muller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zuhlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., & Platt, T. An ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI). Sensors, 19(19), (2019): 4285, doi: 10.3390/s19194285.

Description: Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Description: This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation – PORTWIMS.

Description: Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research- Biogeosciences 125(2), (2020): e2019JG005276, doi:10.1029/2019JG005276.

Description: A chemoautotrophy maximum is present in many anoxic basins at the sulfidic layer's upper boundary, but the factors controlling this feature are poorly understood. In 13 of 31 cruises to the Cariaco Basin, particulate organic carbon (POC) was enriched in 13C (δ13CPOC as high as −16‰) within the oxic/sulfidic transition compared to photic zone values (−23 to −26‰). During “heavy” cruises, fluxes of O2 and [NO3− + NO2−] to the oxic/sulfidic interface were significantly lower than during “light” cruises. Cruises with isotopically heavy POC were more common between 2013 and 2015 when suspended particles below the photic zone tended to be nitrogen rich compared to later cruises. Within the chemoautotrophic layer, nitrogen‐rich particles (molar ratio C/N〈 10) were more likely to be 13C‐enriched than nitrogen‐poor particles, implying that these inventories were dominated by living cells and fresh detritus rather than laterally transported or extensively decomposed detritus. During heavy cruises, 13C enrichments persisted to 1,300 m, providing the first evidence of downward transport of chemoautotrophically produced POC. Dissolved inorganic carbon assimilation during heavy cruises (n = 3) was faster and occurred deeper than during light cruises (n = 2). Metagenomics data from the chemoautotrophic layer during two cruises support prevalence of microorganisms carrying RuBisCO form II genes, which encode a carbon fixation enzyme that discriminates less against heavy isotopes than most other carbon fixation enzymes, and metatranscriptomics data indicate that higher expression of form II RuBisCO genes during the heavy cruises at depths where essential reactants coexist are responsible for the isotopically heavier POC.

Description: We thank the captain and crew of the B/O Hermano Gines and the staff of Estación de Investigaciones Marinas, Fundación de la Salle de Ciencias Naturales, Margarita Island, Venezuela, for their field and laboratory assistance. We are also indebted to the many students, colleagues, and technicians who have participated in this project, in particular, L. Medina Faull for contour plots, E. Tappa (USC) for POC and δ13CPOC data measured in Robert Thunell's lab, and K. Fanning and K. Buck and W. Abbott (USF) for nutrient data. Digna‐Rueda‐Roa, Laura Lorenzoni, and Matt Biddle assisted greatly in getting the data into a format suitable for submission to the BCO‐DMO database. We are also grateful to two anonymous reviewers for their insightful comments. This research was supported by grants from NSF (OCE‐1259110 awarded to M. I. S. and G. T. T.; OCE‐1258991 to R. C. T.; OCE‐0326268, OCE‐0963028, OCE‐1259043, and OCE‐1649626 to F. M. K.; and OCE‐1336082 and OCE‐1335436 awarded to V. P. E. and G. T. T., respectively), from Venezuela's FONACIT (2000001702 and 2011000353 to Y. A.), and a WHOI subaward A101259 to M. G. P. Biological and Chemical Oceanography Data Management Office Metadata landing page for the Cariaco Time series Niskin bottle data is/https://www.bco‐dmo.org/dataset/3093. For the data from our biogeochemistry cruises the BCO‐DMO Metadata landing page is https://ww.bco‐dmo.org/dataset/3120 and for the Time series CTD data is https://www.bco‐dmo.org/dataset/3092. δ13CDIC data are presented in Table S1. Metagenome and metatranscriptome data are available from SRA (accession number PRJNA544741). δ13CPOC data are available at https://doi.org/10.6084/m9.figshare.8214470.v1.

Description: 2020-07-30

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.