Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.

Slides:

Advertisements

Similar presentations

Lisl Robertson, University of Cape Town, RSA with assistance from Stewart Bernard Christo Whittle GOOS AFRICA.

Advertisements

Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene.

Beyond Chlorophyll: Ocean color ESDRs and new products S. Maritorena, D. A. Siegel and T. Kostadinov Institute for Computational Earth System Science University.

Phytoplankton absorption from ac-9 measurements Julia Uitz Ocean Optics 2004.

Large-scale Satellite Oceanography in Eastern Pacific Upwelling Regions Andrew Thomas University of Maine Recent manuscript collaborators: Jose Luis Blanco,

Evaluation of Trends in Chlorophyll-a Concentration in Response to Climatic Variability in the Eastern Bering Sea from MODIS Puneeta Naik a,b and Menghua.

A framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters based on remote sensing reflectance for water.

Characterization of radiance uncertainties for SeaWiFS and Modis-Aqua Introduction The spectral remote sensing reflectance is arguably the most important.

Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf: In Situ Time Series for Validation and Exploration.

Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.

2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.

Primary production and community structure response to deep seawater enrichment Karin Björkman, PhD OTEC/SWAC meeting 16 September 2011.

1 Remote sensing applications in Oceanography: How much we can see using ocean color? Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal.

Organic Matter Metabolism in a Coastal Ocean Ecosystem Patricia Matrai Mike Sieracki Nicole Poulton Carlton Rauschenberg Bigelow Laboratory for Ocean Sciences.

Satellite Retrieval of Phytoplankton Community Size Structure in the Global Ocean Colleen Mouw University of Wisconsin-Madison In collaboration with Jim.

Remote Assessment of Phytoplankton Functional Types Using Retrievals of the Particle Size Distribution from Ocean Color Data Tihomir Kostadinov, David.

Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.

HAB Case Studies Current Automated Capabilities & Future Needs Automated submersible flow cytometry Flexible targets, ecological context (MVCO examples)

Plankton Analysis by Automated Submersible Imaging-in-Flow Cytometry: Transforming a Specialized Research Instrument into a Broadly Accessible Tool Heidi.

UNH Coastal Observing Center NASA GEO-CAPE workshop August 19, 2008 Ocean Biological Properties Ru Morrison.

In situ science in support of satellite ocean color objectives Jeremy Werdell NASA Goddard Space Flight Center Science Systems & Applications, Inc. 6 Jun.

Relationship between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing in the Wind River Range, Wyoming MODIS-derived snow cover measured on 30 April.

Ocean Color Observations and Their Applications to Climate Studies Alex Gilerson, Soe Hlaing, Ioannis Ioannou, Sam Ahmed Optical Remote Sensing Laboratory,

Atmospheric Correction Algorithms for Remote Sensing of Open and Coastal Waters Zia Ahmad Ocean Biology Processing Group (OBPG) NASA- Goddard Space Flight.

1 Evaluating & generalizing ocean color inversion models that retrieve marine IOPs Ocean Optics Summer Course University of Maine July 2011.

Retrieving Coastal Optical Properties from MERIS S. Ladner 1, P. Lyon 2, R. Arnone 2, R. Gould 2, T. Lawson 1, P. Martinolich 1 1) QinetiQ North America,

Assessing Biodiversity of Phytoplankton Communities from Optical Remote Sensing Rick A. Reynolds, Dariusz Stramski, and Julia Uitz Scripps Institution.

Towards community-based approaches to estimating NPP & NCP from remotely-sensed optical properties Rick A. Reynolds Scripps Institution of Oceanography.

Ocean Color Radiometer Measurements of Long Island Sound Coastal Observational platform (LISCO): Comparisons with Satellite Data & Assessments of Uncertainties.

Soe Hlaing *, Alex Gilerson, Samir Ahmed Optical Remote Sensing Laboratory, NOAA-CREST The City College of the City University of New York 1 A Bidirectional.

Norm Nelson, Dave Siegel Institute for Computational Earth System Science, UCSB Bermuda Bio-Optics Project Decade-Plus Perspective on Ocean Color.

Data Policy and SeaBASS Evolution Giulietta S. Fargion CHORS April 11, 2006.

Biodiversity hotspots of primary producers at the global scale Alice Soccodato In collaboration with: d’Ovidio F, De Monte S, Levy M, Follows M, Alvain.

Using in-situ measurements of inherent optical properties to study biogeochemical processes in aquatic systems. Emmanuel Boss Funded by:

DFO Northwest Atlantic Ocean Monitoring & Mooring Programs OSNAP Planning Meeting April 2011 BIO.

Lecture 22 Deployment Strategies Fixed Platforms Collin Roesler 18 July 2007.

Putting the puzzle together: Connecting the HyperSAS with BreveBuster absorbance spectra, fluorometer, extracted chlorophyll and MODIS data. Blake A. Schaeffer,

Title page. “ Possible Geocape Capabilities Measurements of Diurnal Variability with Requirements of hours to days……………. “

What is the key science driver for using Ocean Colour Radiometry (OCR) for research and applications? What is OCR, and what does it provide? Examples of.

Ocean Color Products: The challenge of going from stocks to rates

Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes R.W. Gould, Jr. & R.A. Arnone Naval Research Laboratory, Code 7333,

Definition and assessment of a regional Mediterranean Sea ocean colour algorithm for surface chlorophyll Gianluca Volpe National Oceanography Centre, Southampton.

NASA Ocean Color Research Team Meeting, Silver Spring, Maryland 5-7 May 2014 II. Objectives Establish a high-quality long-term observational time series.

Yvette H. Spitz Oregon State University, CEOAS Carin J. Ashjian (1), Robert G. Campbell (2), Michael Steele (3) and Jinlun Zhang (3) (1) Woods Hole Oceanographic.

The effect of wind on the estimated plume extension of the La Plata River Erica Darken Summer 2004.

Fluorescence Line Height (FLH) Ricardo Letelier, Mark Abbott, Jasmine Nahorniak Oregon State University.

The role of Optical Water Type classification in the context of GIOP Timothy S. Moore University of New Hampshire, Durham NH Mark D. Dowell Joint Research.

New Fluorescence Algorithms for the MERIS Sensor Yannick Huot and Marcel Babin Laboratoire d’Océanographie de Villefranche Antoine Mangin and Odile Fanton.

Estimating the uncertainties in the products of inversion algorithms or, how do we set the error bars for our inversion results? Emmanuel Boss, U. of Maine.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Ecosystem Theme Introduction.

Seasonality of phytoplankton distributions in the Galapagos Marine Reserve A.M. McCulloch 1, W.V. Sweet 1, B.A. Schaeffer 1, J.M. Morrison 2, D. Kamykowski.

CIOSS Ocean Optics Aug 2005 Ocean Optics, Cal/Val Plans, CDR Records for Ocean Color Ricardo M Letelier Oregon State University Outline - Defining Ocean.

ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING Julia Uitz, Dariusz Stramski, and Rick A. Reynolds Scripps Institution.

Introduction As part of a study investigating phytoplankton diversity and physiology in the Western Pacific Warm Pool, we measured group-specific rates.

WP 9 Ecosystem modelling and data assimilation The overall objective is to improve forecasts of the pre-operational systems with quantitative evaluation.

Our project involves a synthesis of satellite-based ocean color and temperature, in situ ocean data, and atmospheric O 2 /N 2 and N 2 O measurements to.

International Ocean Color Science Meeting, Darmstadt, Germany, May 6-8, 2013 III. MODIS-Aqua normalized water leaving radiance nLw III.1. R2010 vs. R2012.

Mycosporine-like Amino Acids (MAAs) may serve to photoprotect phytoplankton from UV radiation  Water soluble compounds  Exhibit Absorption maxima from.

Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.

Incorporating Satellite Time-Series data into Modeling Watson Gregg NASA/GSFC/Global Modeling and Assimilation Office Topics: Models, Satellite, and In.

Primary Production. Remote Sensing of Ocean Colour: Visible Spectral Radiometry.

Remote Sensing of the Ocean and Coastal Waters

Jian Wang, Ph.D IMCS Rutgers University

IMAGERY DERIVED CURRENTS FROM NPP Ocean Color Products 110 minutes!

RESULTS-1. Validation of chlorophyll algorithms

Yi Xu, Robert Chant, and Oscar Schofiled Coastal Ocean Observation Lab

A decadal analysis of phytoplankton community composition in the melting West Antarctic Peninsula Oscar Schofield1 Grace Saba1,

Department of Oceanography Sung-Chan Kang

Assessment of Satellite Ocean Color Products of the Coast of Martha’s Vineyard using AERONET-Ocean Color Measurements Hui Feng1, Heidi Sosik2 , and Tim.

Fig. 3 Global and basin-averaged sampling error compared with reconstructed temperature change. Global and basin-averaged sampling error compared with.

Presentation transcript:

Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation and Exploration of Remote Sensing Algorithms Woods Hole Oceanographic Institution University of New Hampshire

Project Overview Goal: Use unique time series to evaluate algorithms that extend MODIS ocean color data beyond chlorophyll to functional type or size-class- dependent phytoplankton retrievals Approach: End-to-end time series observations, with step-by-step algorithm evaluation and error analysis single cells  phytoplankton community  bulk water optical properties  sea surface optical properties (air and water)  MODIS optical properties Martha’s Vineyard Coastal Observatory Tower mounted AERONET-OC MODIS products Submersible Imaging Flow Cytometry

Approach Phytoplankton Observations Single cells to communities Biomass, size- and taxon-resolved Phytoplankton Algorithms Absorption spectral shape  size structure Diagnostic pigments  size structure Diagnostic pigments  taxonomic structure

        Variability in community structure. Diatoms Cyano- bacteria

Pigment-based retrieval of taxonomic groups Diatoms “CHEMTAX” Mackey et al In situ FCM Total Chl a = diatom Chl a + dinoflagellate Chl a + cyanobacteria Chl a + … with partitioning according to accessory pigment ratios

Pigment-based retrieval of taxonomic groups DiatomsDinoflagellatesCyanobacteria ~1  m cells 10  m

Diagnostic pigment retrieval from Rrs Pan et al band ratio algorithms AERONET-OC SeaPRISM, R rs ( ) Discrete samples HPLC pigment analysis Chl a

Diagnostic pigment retrieval from Rrs Chl aFucoxanthinZeaxanthin Pan et al band ratio algorithms

Seasonality – pigment retrievals Diatom indicator pigment Cyanobacteria indicator pigment Diatoms Cyano- bacteria

Ecosystem characterization Taxa with positive response to warmer winters Taxa with negative response to warmer winters Interannual variability – taxon specific Seasonally adjusted Biomass anomalies vs Temperature anomalies Cyanobacterium Diatoms

Ecosystem characterization Local detail  Trends and patterns of change  Regional to basin scales Decadal increase in pico-cyanobacteria at MVCO

Open data access Standard formats Processing pipelines End-to-end provenance