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25 results for “bio-optics”
Figure 1 in Exploring the dynamics of small pelagic fish catches in the Marmara Sea in relation to changing environmental and bio-optical parameters
Figure 1. Time series of deseasonalised Chl-a, net primary productivity (NPP), and sea surface temperature (SST). Solid lines show the time series, dash-dot lines show the deseasonalised time series, dashed lines indicate their respective nonlinear trends, and flat solid lines show linear trend components.
Figure 2 in Exploring the dynamics of small pelagic fish catches in the Marmara Sea in relation to changing environmental and bio-optical parameters
Figure 2. Time series of fisheries catches (tons) and fishing effort in the Marmara Sea between 2000 and 2019. Flat solid lines indicate linear trends.
Assessing controls on cross-shelf phytoplankton and suspended particle distributions using repeated bio-optical glider surveys
The data were collected from an oceanographic glider in the coastal Santa Barbara Chanel, CA during six deployments from March 2012 to June 2013. Data includes 404 cross-shelf sections of temperature (C), salinity (PSU), colored dissolved organic material (CDOM, volts; dark-corrected), particulate backscatter (bbp at 650nm, m-1) and Chlorophyll fluorescence (chl-fl, volts; dark-corrected). Each section is 35x40 in size (35 depth measurements x 40 latitude measurements about 100m apart). They were obtained from kriging of raw sections every 100m in latitude and 2m in depth. Longitude is constant at -119 44.824. Matlab files are indexed to each of the glider sections (1-404). Data are described in Hendrikx Freitas, F., D. A. Siegel, L. Washburn, S. Halewood and E. Stassinos. 2016. Assessing controls on cross-shelf phytoplankton and suspended particle distributions using repeated bio-optical glider surveys. Journal of Geophysical Research - Oceans, 121: 7776-7794. Doi: 10.1002/2016JC011781 Definition of the variables in the matlab file: Times are in matlab time. Gtime is the average timestamp of each glider section (first column = time; second column = mission number it belongs to). Gdurationanddirection is useful to know how long (in time) each section actually is, and what direction it started with (inshore-offshore or offshore-inshore). Gvel are the depth-averaged velocities obtained from the glider (columns are time, lat, lon, alongshore component of currents, cross-shore component of currents, mission number). Chl in mg/m3 = 0.0159*(dark corrected chl counts)-0.0165); N=9; r=0.82 (p-value<0.05). bbp in 1/m units and it was the optical backscatter data that were converted to particulate backscattering following standard algorithms.
Dataset from Lagrangian bio-optical drifters during four experiments in coastal and open ocean waters
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Bio-optical Database of the Arctic Ocean
<p>The Arctic bio-optical database assembles a diverse suite of biological and optical data from 34 expeditions throughout the Arctic Ocean. Data combined into a single AO database following the OBPG criteria (Pegau et al. 2003), as was done in the development of the global NASA Bio-optical Marine Algorithm Data Set (NOMAD) (Werdell 2005, Werdell & Bailey 2005). This Arctic database combines coincident <i>in situ</i> observations of IOPs, apparent optical properties (AOPs), Chl <i>a</i>, environmental data (e.g. temperature, salinity) and station metadata (e.g. sampling depth, latitude, longitude, date). Data were acquired from the NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS, https://seabass.gsfc.nasa.gov/), the LEFE CYBER database (http://www.obs-vlfr.fr/proof/index2.php), the Data and Sample Research System for Whole Cruise Information in JAMSTEC (DARWIN, http://www.godac.jamstec.go.jp), NOMAD, and individual contributors. To ensure consistency, data were limited to those that were collected using OBPG defined protocols (Pegau et al. 2003). Only observations shallower that 30 m were included. For spectral parameters, we included data at the following wavelengths that are used by satellite and thus are relevant for ocean color algorithm evaluation: 412, 443, 469, 488, 490, 510, 531, 547, 555, 645, 667, 670 and 678 nm. <i>In situ</i> measurements were binned at the same station if measurements were within 8 hours and 1° of distance (Werdell & Bailey 2005). For regional analyses, each station was assigned to one of ten sub-regions and three functional shelf-types (Carmack et al. 2006).</p>
Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing for "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea" paper.
<p>Bio-Optic bio-shortwave model code, initial conditions, river and boundary forcing as well as selected model output used for analysis and producing figures in the paper "Estimating the seaonal impact of optically significant water constituents on surface heating rates in the Western Baltic Sea". Contact Bronwyn Cahill if you have questions at: bronwyn.cahill@io-warnemuende.de</p>
Bio-optical observations of the Baltic Sea and coastal areas, 2008-2012
<p>This a dataset of optical-biogeochemical measurement results was collected during 2008-2012 as part of spring and summer cruises with R/V Aranda as well as from flow-through water samples taken with the Ferrybox system on M/S Finnmaid. The majority of observations were made in the Gulf of Finland, Baltic Proper, Archipelago Sea, and Gulf of Bothnia in the Baltic Sea. A number of riverine and inshore observations are also included. The data collection is owned by the Finnish Environment Institute SYKE and made available under a CC-BY-NC licence. </p> <p>Detail on methods and protocols are provided in the following papers </p> <ul> <li>Simis, Stefan GH; Ylöstalo, Pasi; Kallio, Kari Y; Spilling, Kristian; Kutser, Tiitt. 2017. Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea. PLoS One 12(4), e0173357. https://doi.org/10.1371/journal.pone.0173357</li> <li>Ylöstalo, Pasi; Seppälä, Jukka; Kaitala, Seppo; Maunula, Petri; Simis, Stefan. 2016. Loadings of dissolved organic matter and nutrients from the Neva River into the Gulf of Finland–Biogeochemical composition and spatial distribution within the salinity gradient. Marine Chemistry 186, 58-71. https://doi.org/10.1016/j.marchem.2016.07.004</li> </ul> <p>A large number of individuals took part in these bio-optical research cruises over the years. The authors of this dataset are particularly grateful to the contributions by international visitors, students and volunteers taking part in one or more cruises, as well as crew and support staff operating the research vessel and ship-of-opportunity. </p> <p>Variables included in the dataset include: </p> <table> <tbody> <tr> <td>Column name</td> <td>unit/format</td> <td>Description</td> </tr> <tr> <td>Secchi</td> <td>m</td> <td>Secchi disk depth</td> </tr> <tr> <td>AirTemp(38)</td> <td>°C, 01H</td> <td>Air temperature from ship weather channel 38, 1-h average</td> </tr> <tr> <td>SeaTemp(42)</td> <td>°C, 01H</td> <td>Sea temperature from ship weather channel 42, 1-h average</td> </tr> <tr> <td>WindSpeed(92)</td> <td>m/s, 10M</td> <td>Wind speed from ship weather channel 92, 10-min average</td> </tr> <tr> <td>WindDir(96)</td> <td>°, 10M</td> <td>Wind direction from ship weather channel 96, 10-min average</td> </tr> <tr> <td>Salinity(104)</td> <td>PSU, 01H</td> <td>Salinity from ship weather channel 104, 1-h average</td> </tr> <tr> <td>Rel.humid(54)</td> <td>%, 01H</td> <td>Relative humidity from ship weather channel 54, 1-h average</td> </tr> <tr> <td>Chla</td> <td>mg/m3</td> <td>Chlorophyll-a concentration (cold ethanol extraction and calibrated fluorescence)</td> </tr> <tr> <td>TSM_avg</td> <td>mg/L</td> <td>Total Suspended Matter Dry Weight, Average</td> </tr> <tr> <td>OSM_avg</td> <td>mg/L</td> <td>Dry weight of Organic fraction of TSM, Average</td> </tr> <tr> <td>ISM_avg</td> <td>mg/L</td> <td>Dry weight of Inorganic fraction of TSM, Average</td> </tr> <tr> <td>DOC_avg</td> <td>µM</td> <td>Dissolved Organic Carbon concentration, Average</td> </tr> <tr> <td>TDN_avg</td> <td>µM</td> <td>Total Dissolved Nitrogen concentration, Average</td> </tr> <tr> <td>NH4</td> <td>µM</td> <td>Ammonium concentration</td> </tr> <tr> <td>NO32</td> <td>µM</td> <td>Nitrate-Nitrate concentration</td> </tr> <tr> <td>NO2</td> <td>µM</td> <td>Nitrite concentration</td> </tr> <tr> <td>PO4</td> <td>µM</td> <td>Phosphate concentration</td> </tr> <tr> <td>SiO4</td> <td>µM</td> <td>Silicate concentration</td> </tr> <tr> <td>TN</td> <td>µM</td> <td>Total nitrogen concentration</td> </tr> <tr> <td>TP</td> <td>µM</td> <td>Total phosphorous concentration</td> </tr> <tr> <td>pH</td> <td>pH</td> <td>pH value</td> </tr> <tr> <td>Temp_CTD</td> <td>°C</td> <td>Water temperature measured by Seabird CTD on sampling rosette</td> </tr> <tr> <td>Salinity_CTD</td> <td>SSU</td> <td>Salinity measured by Seabird CTD on sampling rosette</td> </tr> <tr> <td>POC</td> <td>µM</td> <td>Particulate Organic Carbon concentration, Average</td> </tr> <tr> <td>PON</td> <td>µM</td> <td>Particulate Organic Nitrogen concentration, Average</td> </tr> <tr> <td>POP</td> <td>µM</td> <td>Particulate Organic Phosphorus concentration, Average (30.973762 g/Mol)</td> </tr> <tr> <td>Turbidity</td> <td>PSU</td> <td>Turbidity</td> </tr> <tr> <td>aCDOM</td> <td>m^-1</td> <td>spectral absorption coefficient of coloured dissolved organic matter</td> </tr> <tr> <td>CloudCover</td> <td>0-1</td> <td>Fraction (0-1) of cloud cover assesed from photos taken in the field.</td> </tr> <tr> <td>Kd</td> <td>m^-1</td> <td>spectral Vertical diffuse downwelling irradiance coefficient</td> </tr> <tr> <td>a_nap</td> <td>m^-1</td> <td>spectral absorption coefficient by non-pigmented fraction of suspended matter</td> </tr> <tr> <td>a_tsm</td> <td>m^-1</td> <td>spectral absorption coefficient by suspened matter</td> </tr> <tr> <td>R0</td> <td>-</td> <td>spectral Subsurface Irradiance Reflectance</td> </tr> <tr> <td>pigments</td> <td>mg/m3</td> <td>Chlorophyll and other pigments extracted and quantified using a combination of calibrated fluorometry and HPLC</td> </tr> </tbody> </table>
Bio-optical Database of the Arctic Ocean
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Dataset for: Sensitivity of a satellite algorithm for harmful algal blooms discrimination to the use of laboratory bio-optical data for training
<p>Two files relating to the publication by Martinez-Vicente et al. (2020).</p> <p>meris_data_karenia_alt_chla.xlsx : file containing the chlorophyll concentrations for the different areas in the MODIS images selected for training and evaluation of the algorithm.</p> <p>coefficients_for_LDA_Karenia_mikimotoi.zip: file containing the coefficients for the Linear Discriminant Analysis (LDA) resulting from the training datasets 1,2 and 3. </p> <p> </p>
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d in Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d Hymedesmia sp.
Data from: Primary production calculations for sea ice from bio-optical observations in the Baltic Sea
Bio-optics is a powerful approach for estimating photosynthesis rates, but has seldom been applied to sea ice, where measuring photosynthesis is a challenge. We measured absorption coefficients of chromophoric dissolved organic matter (CDOM), algae, and non-algal particles along with solar radiation, albedo and transmittance at four sea-ice stations in the Gulf of Finland, Baltic Sea. This unique compilation of optical and biological data for Baltic Sea ice was used to build a radiative transfer model describing the light field and the light absorption by algae in 1-cm increments. The maximum quantum yields and photoadaptation of photosynthesis were determined from 14C-incorporation in photosynthetic-irradiance experiments using melted ice. The quantum yields were applied to the radiative transfer model estimating the rate of photosynthesis based on incident solar irradiance measured at 1-min intervals. The calculated depth-integrated mean primary production was 5 mg C m–2 d–1 for the surface layer (0–20 cm ice depth) at Station 3 (fast ice) and 0.5 mg C m–2 d–1 for the bottom layer (20–57 cm ice depth). Additional calculations were performed for typical sea ice in the area in March using all ice types and a typical light spectrum, resulting in depth-integrated mean primary production rates of 34 and 5.6 mg C m–2 d–1 in surface ice and bottom ice, respectively. These calculated rates were compared to rates determined from 14C incorporation experiments with melted ice incubated in situ. The rate of the calculated photosynthesis and the rates measured in situ at Station 3 were lower than those calculated by the bio-optical algorithm for typical conditions in March in the Gulf of Finland by the bio-optical algorithm. Nevertheless, our study shows the applicability of bio-optics for estimating the photosynthesis of sea-ice algae.
Data from: Primary production calculations for sea ice from bio-optical observations in the Baltic Sea
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FLUID: Bio-optical water quality parameters (in-situ 2017-2019) in 4 Baltic lakes
<p>The database consists bio-optical measurements done under FLUID<sup>1</sup> project in 4 different lakes (Burtnieks, Lubans, Razna and Võrtsjärv) during 2017-2019.</p> <p>The listed parameters are: </p> <ul> <li>LAKE (name)</li> <li>DATE </li> <li>TIME (local time)</li> <li>LAT (latitude)</li> <li>LON (longitude)</li> <li>Air T (air temperature in Celcius)</li> <li>Water T (water temperature in 0.5 m depth in Celcius)</li> <li>DO (dissolved oxygen in ppm)</li> <li>O2 (O2 saturation in %)</li> <li>Secchi (in meters)</li> <li>Chl a (chlorophyll-a in mg/m3)</li> <li>Pheo (pheopigments in mg/m3) </li> <li>TSS (total suspended sediments in mg/l)</li> <li>OSS (organinc suspended sediments in mg/l)</li> <li>MSS (mineral suspended sediments in mg/l)</li> <li>CDOM (absorption of coloured dissolved organic matter at 400 nm)</li> <li>TN (total nitrogen in mg/l)</li> <li>TP (total phosphorus in mg/l)</li> <li>CO2 (carbon dioxide in mg/l)</li> </ul> <p><sup>1</sup>FLUID is funded by ERDF, Latvian state budget and IES proposal No.1.1.1.2/VIAA/1/16/137, Contract No. 1.1.1.2/16/I/001 “Innovative tool for lake monitoring using remote sensing data"</p>
S-MODE Shipboard Bio-optical Measurements Version 1
This dataset contains shipboard bio-optical measurements collected during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. Data are available in netCDF format.
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.
Bermuda Bio-Optics Project (BBOP)
The Bermuda Bio-Optics Project (BBOP) is a long term study of the factors contributing to the regulation of the underwater light field in the open ocean and the resulting biogeochemical impact. These studies are done, on average, once a month in conjunction with the Bermuda-Atlantic Time Series (BATS) in the Sargasso Sea.
Measurements used to develop the Bio-Optical Algorithm (BOA)
Measurements used to develop the Bio-Optical Algorithm (BOA), taken between 1991 and 1995 in the Northeast Pacific, North Atlantic, Gulf of Mexico, and Arabian Sea.
S-MODE L2 Shipboard Thermosalinograph, Meteorology, and Bio-optics Measurements Version 1
This dataset contains shipboard thermosalinograph (TSG), meteorology, and bio-optics measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) field campaign. The experiment was conducted approximately 300 km offshore of San Francisco, during a pilot campaign that spanned two weeks in October 2021, and two intensive operating periods in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The TSG instrument measures the temperature and conductivity of seawater passing through a port in the hull of the ship. TSG data is calibrated using water samples compared to standard seawater and a laboratory salinometer onboard the ship. This dataset also contains chlorophyll and meteorology measurements including air temperature, barometric pressure, wind speed and direction, relative humidity, and radiative fluxes. Data are available in netCDF format, with separate dimensions for time, time of bio-optics measurements, and time of radiometer measurements.
Bio-optical properties of the different water masses in the Gulf of St. Lawrence
The St. Lawrence ecosystem is a complex environment influenced by a variety of physical forces (runoff, winds, tides, bathymetry) that sustains a diverse food web going from phytoplankton to whales. Chlorophyll concentration is thus an important variable to measure at the scale of the ecosystem. Because of its large size, remote sensing is the only available tool to measure chlorophyll distribution in the St. Lawrence using ocean color imagery. To fully utilize this type of data, it is however important to have a sound knowledge of the bio-optical properties of the different water masses in the system. A St. Lawrence SeaWiFS program was thus built to gather this knowledge beginning in 1997.
Gulf of Mexico carbon and bio-optical measurements
Satellite Assessment of CO2 Distribution, Variability and Flux and Understanding of Control Mechanisms in a River Dominated Ocean Margin
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