Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines

Total Page:16

File Type:pdf, Size:1020Kb

Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines doi:10.25923/twc0-f025 Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines NOAA NOS National Centers for Coastal Ocean Science Center for Coastal Monitoring and Assessment Timothy Wynne, Andrew Meredith, Travis Briggs, Wayne Litaker, Richard Stumpf October 2018 NOAA TECHNICAL MEMORANDUM NOS NCCOS 252 NOAA National Centers for Coastal Ocean Science Citation Wynne, T., A. Meredith, T. Briggs, W. Litaker, and R. Stumpf 2018. Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines. NOAA Technical Memorandum NOS NCCOS 252. Silver Spring, MD. 48 pp. doi:10.25923/twc0-f025 Mention of trade names or commercial products does not constitute endorsement or recommendation for their use by the United States government. Harmful Algal Bloom Forecasting Branch Ocean Color Satellite Imagery Processing Guidelines October 2018 Timothy Wynne, Andrew Meredith, Travis Briggs, Wayne Litaker, and Richard Stumpf Harmful Algal Bloom –Forecasting Branch National Oceanic and Atmospheric Administration, National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, Silver Spring, MD 20910 NOAA Technical Memorandum NOS NCCOS 252 United States Department National Oceanic and National of Commerce Atmospheric Administration Ocean Service Wilbur L. Ross, Jr. Tim Gallaudet Nicole LeBoeuf Secretary RDML (Ret.), Acting Administrator Acting Assistant Administrator Table of Contents 1. Introduction .......................................................................................................................................... 1 2. Satellites From Which Data Are Obtained ............................................................................................ 2 2.1. SeaWiFS ......................................................................................................................................... 2 2.2. MODIS ........................................................................................................................................... 2 2.3. MERIS ............................................................................................................................................ 3 2.4. OLCI ............................................................................................................................................... 3 2.5. Sentinel-2 ...................................................................................................................................... 3 2.6. Landsat .......................................................................................................................................... 4 2.7. VIIRS .............................................................................................................................................. 4 3. Products ................................................................................................................................................ 4 3.1. Cyanobacterial Index (CI) ................................................................................................................... 6 3.1.1. CIcyano .................................................................................................................................. 7 1.1. Maximum Chlorophyll Index (MCI) ............................................................................................. 10 1.2. Diffuse Attenuation Coefficient (Kd) ........................................................................................... 11 1.3. Chlorophyll .................................................................................................................................. 12 1.3.1. OCx ...................................................................................................................................... 12 1.3.2. ESA alg1 and alg2 ................................................................................................................ 13 1.4. True Color .................................................................................................................................... 14 1.5. Scumcolor.................................................................................................................................... 14 1.6. Rrs665 ......................................................................................................................................... 15 2. Processing ........................................................................................................................................... 17 2.1. Product format ............................................................................................................................ 18 2.2. Product file naming ..................................................................................................................... 18 2.3. Level 3 GeoTiff format ................................................................................................................ 19 2.4. Flags ............................................................................................................................................ 20 2.4.1. Clouds .................................................................................................................................. 20 2.4.2. Glint ..................................................................................................................................... 20 2.4.3. Snow and Ice ....................................................................................................................... 21 2.4.4. Sensor Saturation ................................................................................................................ 22 2.5. Land and Dry Lake Bed ................................................................................................................ 23 3. Georeferencing ................................................................................................................................... 24 4. Dissemination ..................................................................................................................................... 24 4.1. Website ....................................................................................................................................... 24 4.2. HAB bulletin ................................................................................................................................ 24 4.3. Other ........................................................................................................................................... 25 5. Future work ......................................................................................................................................... 25 References .................................................................................................................................................. 26 Acronyms Used: .......................................................................................................................................... 28 Appendices .................................................................................................................................................. 29 A1. SeaWiFS band characteristics ..................................................................................................... 29 A2. MODIS band characteristics from Section 1.2 ............................................................................ 29 A3. MERIS band characteristics from Section 1.3 ............................................................................. 31 A4. OLCI band characteristics from section 1.4 ................................................................................ 32 A5. Landsat-8 OLI specifications ........................................................................................................ 33 A6. Sentinel-2 band characteristics from Section 1.5 ....................................................................... 33 A7. VIIRS band characteristics from Section 1.6 ............................................................................... 34 B1. SeaWiFS flags .................................................................................................................................... 35 B2. MERIS/OLCI flags ............................................................................................................................... 36 B3. MODIS flags ....................................................................................................................................... 38 Appendix C: Clear water correction ........................................................................................................ 40 CI: MODIS clear water correction ........................................................................................................... 40 C2: MERIS/OLCI clear water correction .................................................................................................. 40 Appendix D: Scaling Factors .................................................................................................................... 41 Appendix E: Clear water correction ........................................................................................................ 41 Appendix F: MODIS processing flowchart ..............................................................................................
Recommended publications
  • A Comparison of Global Estimates of Marine Primary Production from Ocean Color
    ARTICLE IN PRESS Deep-Sea Research II 53 (2006) 741–770 www.elsevier.com/locate/dsr2 A comparison of global estimates of marine primary production from ocean color Mary-Elena Carra,Ã, Marjorie A.M. Friedrichsb,bb, Marjorie Schmeltza, Maki Noguchi Aitac, David Antoined, Kevin R. Arrigoe, Ichio Asanumaf, Olivier Aumontg, Richard Barberh, Michael Behrenfeldi, Robert Bidigarej, Erik T. Buitenhuisk, Janet Campbelll, Aurea Ciottim, Heidi Dierssenn, Mark Dowello, John Dunnep, Wayne Esaiasq, Bernard Gentilid, Watson Greggq, Steve Groomr, Nicolas Hoepffnero, Joji Ishizakas, Takahiko Kamedat, Corinne Le Que´re´k,u, Steven Lohrenzv, John Marraw, Fre´de´ric Me´lino, Keith Moorex, Andre´Moreld, Tasha E. Reddye, John Ryany, Michele Scardiz, Tim Smythr, Kevin Turpieq, Gavin Tilstoner, Kirk Watersaa, Yasuhiro Yamanakac aJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91101-8099, USA bCenter for Coastal Physical Oceanography, Old Dominion University, Crittenton Hall, 768 West 52nd Street, Norfolk, VA 23529, USA cEcosystem Change Research Program, Frontier Research Center for Global Change, 3173-25,Showa-machi, Yokohama 236-0001, Japan dLaboratoire d’Oce´anographie de Villefranche, 06238, Villefranche sur Mer, France eDepartment of Geophysics, Stanford University, Stanford, CA 94305-2215, USA fTokyo University of Information Sciences 1200-1, Yato, Wakaba, Chiba 265-8501, Japan gLaboratoire d’Oce´anographie Dynamique et de Climatologie, Univ Paris 06, MNHN, IRD,CNRS, Paris F-75252 05, France hDuke University
    [Show full text]
  • Euphotic Zone Depth: Its Derivation and Implication to Ocean-Color Remote Sensing" (2007)
    University of South Florida Scholar Commons Marine Science Faculty Publications College of Marine Science 3-16-2007 Euphotic Zone Depth: Its Derivation and Implication to Ocean- Color Remote Sensing ZhongPing Lee Stennis Space Center Alan Weidemann Stennis Space Center John Kindle Stennis Space Center Robert Arnone Stennis Space Center Kendall L. Carder University of South Florida, [email protected] See next page for additional authors Follow this and additional works at: https://scholarcommons.usf.edu/msc_facpub Part of the Marine Biology Commons Scholar Commons Citation Lee, ZhongPing; Weidemann, Alan; Kindle, John; Arnone, Robert; Carder, Kendall L.; and Davis, Curtiss, "Euphotic Zone Depth: Its Derivation and Implication to Ocean-Color Remote Sensing" (2007). Marine Science Faculty Publications. 11. https://scholarcommons.usf.edu/msc_facpub/11 This Article is brought to you for free and open access by the College of Marine Science at Scholar Commons. It has been accepted for inclusion in Marine Science Faculty Publications by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Authors ZhongPing Lee, Alan Weidemann, John Kindle, Robert Arnone, Kendall L. Carder, and Curtiss Davis This article is available at Scholar Commons: https://scholarcommons.usf.edu/msc_facpub/11 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112, C03009, doi:10.1029/2006JC003802, 2007 Euphotic zone depth: Its derivation and implication to ocean-color remote sensing ZhongPing Lee,1 Alan Weidemann,1 John Kindle,1 Robert Arnone,1 Kendall L. Carder,2 and Curtiss Davis3 Received 6 July 2006; revised 12 October 2006; accepted 1 November 2006; published 16 March 2007. [1] Euphotic zone depth, z1%, reflects the depth where photosynthetic available radiation (PAR) is 1% of its surface value.
    [Show full text]
  • Status of ESA Missions
    ESA Atmospheric Science Conference, Barcelona, 2009 ESA Earth Observation missions Henri Laur ESA EO Missions Management Office EO missions handled by ESA 1990 2000 METEOSAT M-1, 2, 3, 4, 5, 6, 7 2010 METEOSAT Second Generation MSG-1, -2, -3 METEOSAT Third Generation Meteo METOP-1, -2, -3 in cooperation with EUMETSAT ESA Earth Observation 2009 budget Science ~ 600 M€ (16.3% of ESA budget) to better ERS-1, -2 understand the Earth ENVISAT Applications Services to initiate long term monitoring systems and services ERS-1ERS-1 andand ERS-2ERS-2 missionsmissions 18 years of ERS-1/2 SAR data in the archive ERS-2 achieved 14 years in orbit in April 2009 Î ERS-2 was designed for 3 years nominal lifetime ! Platform Î no gyroscopes since 2001 Æ Gyro-less operations Î failure of the Low Bit Rate recorder in 2003 Æ set-up of a collaborative network of acquisition stations Instruments Î all instruments (but ATSR) work satisfactorily and provides useful data Î Good prospect to operate ERS-2 mission until mid-2011 Envisat MERIS (Yesterday, 6 Sep. 2009) Bordeaux Toulouse Bilbao Pyrenees ESA Atmospheric Science Conference 2009 Barcelona Zaragoza ENVISAT mission: Arctic 2007 7 years ! ~2600 Bam earthquake scientific http://www.esa.int/LivingPlanet2010/ projects First images Tectonic uplift (Andaman) + GMES pre- Hurricane operational Katrina projects Global air 09 pollution ber 20 Novem e: 15 Ozone hole 2003 eadlin acts d Abstr Chlorophyll Prestige tanker B-15A concentration oil slick iceberg CO2 map Envisat Envisat Symposium IS ER y Symposium R M R
    [Show full text]
  • The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms
    environments Article The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms Igor Ogashawara Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA; [email protected] Received: 6 May 2019; Accepted: 1 June 2019; Published: 3 June 2019 Abstract: Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed. Keywords: phycocyanin; chlorophyll-a; water quality; Lake Erie; cyanobacteria; bio-optical modeling 1.
    [Show full text]
  • FAME-C: Cloud Property Retrieval Using Synergistic AATSR and MERIS Observations
    Atmos. Meas. Tech., 7, 3873–3890, 2014 www.atmos-meas-tech.net/7/3873/2014/ doi:10.5194/amt-7-3873-2014 © Author(s) 2014. CC Attribution 3.0 License. FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations C. K. Carbajal Henken, R. Lindstrot, R. Preusker, and J. Fischer Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany Correspondence to: C. K. Carbajal Henken ([email protected]) Received: 29 April 2014 – Published in Atmos. Meas. Tech. Discuss.: 19 May 2014 Revised: 17 September 2014 – Accepted: 11 October 2014 – Published: 25 November 2014 Abstract. A newly developed daytime cloud property re- trievals. Biases are generally smallest for marine stratocu- trieval algorithm, FAME-C (Freie Universität Berlin AATSR mulus clouds: −0.28, 0.41 µm and −0.18 g m−2 for cloud MERIS Cloud), is presented. Synergistic observations from optical thickness, effective radius and cloud water path, re- the Advanced Along-Track Scanning Radiometer (AATSR) spectively. This is also true for the root-mean-square devia- and the Medium Resolution Imaging Spectrometer (MERIS), tion. Furthermore, both cloud top height products are com- both mounted on the polar-orbiting Environmental Satellite pared to cloud top heights derived from ground-based cloud (Envisat), are used for cloud screening. For cloudy pixels radars located at several Atmospheric Radiation Measure- two main steps are carried out in a sequential form. First, ment (ARM) sites. FAME-C mostly shows an underestima- a cloud optical and microphysical property retrieval is per- tion of cloud top heights when compared to radar observa- formed using an AATSR near-infrared and visible channel.
    [Show full text]
  • Special Topics in Ocean Optics Protocols, Part 2
    NASA/TM-2004- Ocean Optics Protocols For Satellite Ocean Color Sensor Validation, Revision 5, Volume VI: Special Topics in Ocean Optics Protocols, Part 2 James L. Mueller and Giulietta S. Fargion and Charles R. McClain, Editors J. L. Mueller, S. W. Brown, D. K. Clark, B. C. Johnson, H. Yoon, K. R. Lykke, S. J. Flora, M. E. Feinholz, N. Souaidia, C. Pietras, T. C. Stone, M. A. Yarbrough, Y. S. Kim, R. A. Barnes, Authors. National Aeronautics and Space administration Goddard Space Flight Space Center Greenbelt, Maryland 20771 February 2004 NASA/TM-2004- James L. Mueller1 and Giulietta S. Fargion2 Editors Ocean Optics Protocols For Satellite Ocean Color Sensor Validation, Revision 5, Volume VI, Part 2: Special Topics in Ocean Optics Protocols, Part 2 James L Mueller, CHORS, San Diego State University, San Diego, California Giulietta S. Fargion, Science Applications International Corporation, Beltsville, Maryland Charles R. McClain, NASA Goddard Space Flight Center, Greenbelt, Maryland B. Carol Johnson, Steven W. Brown, Howard Yoon, Keith Lykke, Nordine Souaidia, National Institute of Standards and Technology, Gaithersburg Maryland Dennis K. Clark, National Oceanic and Atmospheric Administration, National Environmental Satellite Data and Information Service, Camp Springs, Maryland Stephanie Flora, Michael E. Feinholz, Mark Yarbrough, Moss Landing Marine Laboratories, San Jose State University, Moss Landing, California Yong Sung Kim, STG Inc., Rockville, Maryland Christophe Pietras, Robert A. Barnes, SAIC General Sciences Corporation, Beltsville,
    [Show full text]
  • The CMEMS Globcolour Chlorophyll a Product Based on Satellite Observation: Multi-Sensor Merging and flagging Strategies
    Ocean Sci., 15, 819–830, 2019 https://doi.org/10.5194/os-15-819-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. The CMEMS GlobColour chlorophyll a product based on satellite observation: multi-sensor merging and flagging strategies Philippe Garnesson, Antoine Mangin, Odile Fanton d’Andon, Julien Demaria, and Marine Bretagnon ACRI-ST, Sophia Antipolis, 06904, France Correspondence: Philippe Garnesson ([email protected]) Received: 21 December 2018 – Discussion started: 2 January 2019 Revised: 20 May 2019 – Accepted: 21 May 2019 – Published: 24 June 2019 Abstract. This paper concerns the GlobColour-merged will require a better characterisation and additional inter- chlorophyll a products based on satellite observation (SeaW- comparison with in situ data. iFS, MERIS, MODIS, VIIRS and OLCI) and disseminated in the framework of the Copernicus Marine Environmental Monitoring Service (CMEMS). This work highlights the main advantages provided by the 1 Introduction Copernicus GlobColour processor which is used to serve CMEMS with a long time series from 1997 to present at The Copernicus Marine Environmental Service (CMEMS) the global level (4 km spatial resolution) and for the Atlantic provides regular and systematic reference information on the level 4 product (1 km spatial resolution). physical state and on marine ecosystems for global oceans To compute the merged chlorophyll a product, two major and for European regional seas, including temperature, cur- topics are discussed: rents, salinity, sea surface height, sea ice, marine optical properties and other such parameters. – The first of these topics is the strategy for merging This capacity encompasses satellite and in situ data- remote-sensing data, for which two options are consid- derived products, the description of the current situation ered.
    [Show full text]
  • Sentinel-2 and Sentinel-3 Mission Overview and Status
    Sentinel-2 and Sentinel-3 Mission Overview and Status Craig Donlon – Sentinel-3 Mission Scientist and Ferran Gascon – Sentinel-2 QC manager and others @ESA IOCCG-21, Santa Monica, USA 1-3rd March 2016 Overview – What is Copernicus? – Sentinel-2 mission and status – Sentinel-3 mission and status Sentinel-3A OLCI first light Svalbard: 29 Feb 14:07:45-14:09:45 UTC Bands 4, 6 and 7 (of 21) at 490, 560, 620 nm Spain and Gibraltar 1 March 10:32:48-10:34:48 UTC California USA 29 Feb 17:44:54-17:46:54 UTC What is Copernicus? Space Component In-Situ Services A European Data Component response to global needs 6 What is Copernicus? – Space in Action for You! • A source of information for policymakers, industry, scientists, business and the public • A European response to global issues: • manage the environment; • understand and to mitigate the effects of climate change; • ensure civil security • A user-driven programme of services for environment and security • An integrated Earth Observation system (combining space-based and in-situ data with Earth System Models) Components & Competences Coordinators: Partners: Private Space Industries companies Component National Space Agencies Overall Programme EMCWF EMSA Mercator snd Ocean Coordination FRONTEX Service Services operators Component EUSC EEA JRC In-situ data are supporting the Space and Services Components Copernicus Funding Funding for Development Funding for Operational Phase until 2013 (c.e.c.): Phase as from 2014 (c.e.c.): ~€ 3.7 B (from ESA and € 4.3 B for the EU) whole programme (from EU)
    [Show full text]
  • Advances in the Monitoring of Algal Blooms by Remote Sensing: a Bibliometric Analysis
    applied sciences Article Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis Maria-Teresa Sebastiá-Frasquet 1,* , Jesús-A Aguilar-Maldonado 1 , Iván Herrero-Durá 2 , Eduardo Santamaría-del-Ángel 3 , Sergio Morell-Monzó 1 and Javier Estornell 4 1 Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, C/Paraninfo, 1, 46730 Grau de Gandia, Spain; [email protected] (J.-A.A.-M.); [email protected] (S.M.-M.) 2 KFB Acoustics Sp. z o. o. Mydlana 7, 51-502 Wrocław, Poland; [email protected] 3 Facultad de Ciencias Marinas, Universidad Autónoma de Baja California, Ensenada 22860, Mexico; [email protected] 4 Geo-Environmental Cartography and Remote Sensing Group, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain; [email protected] * Correspondence: [email protected] Received: 20 September 2020; Accepted: 4 November 2020; Published: 6 November 2020 Abstract: Since remote sensing of ocean colour began in 1978, several ocean-colour sensors have been launched to measure ocean properties. These measures have been applied to study water quality, and they specifically can be used to study algal blooms. Blooms are a natural phenomenon that, due to anthropogenic activities, appear to have increased in frequency, intensity, and geographic distribution. This paper aims to provide a systematic analysis of research on remote sensing of algal blooms during 1999–2019 via bibliometric technique. This study aims to reveal the limitations of current studies to analyse climatic variability effect. A total of 1292 peer-reviewed articles published between January 1999 and December 2019 were collected.
    [Show full text]
  • Sentinel-2 Red-Edge Bands Capabilities for Retrieving Chlorophyll-A: Lake Burullus, Egypt
    Sentinel-2 Red-Edge Bands Capabilities for Retrieving Chlorophyll-a: Lake Burullus, Egypt M. S. Salama1, Hanan Farag1,2 and Maha Tawfik2 The TIGER initiative 1- University of Twente, The Netherlands 2- National Water Research Center, Egypt UNIVERSITY TWENTE. Outlines • Why Snetinel-2 for water quality? • Challenges. • Requirements and objectives. • Study area and data. • Why Red-bands models? • Calibration and validation. • Rapid Eye Chl-a products. • Sentinel-2 expected accuracy. • Demonstration with SPOT time series. • Preliminary conclusions. UNIVERSITY TWENTE. Why Sentinel-2 for water quality? The aquatic biosphere is uniquely monitored by EO sensors as they provide synoptic information on key water quality variables at high temporal frequency. The Multi-Spectral Instrument (MSI) payload of SENTINEL-2 mission will sample 13 spectral bands: four bands at 10 m, six bands at 20 m and three bands at 60 m spatial resolution. The twin satellites of SENTINEL-2 mission offer data at frequency of 5 days at the Equator. UNIVERSITY TWENTE. Challenges 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Part of the 33 34 35 reality36 37 38 39 40 41 42 43 What you see is not what you get! UNIVERSITY TWENTE. Requirements and objective Consistent EO-estimates of water quality parameters in inland and coastal waters requires three components: – (i) a reliable atmospheric correction method; – (ii) an accurate retrieval algorithm and – (iii) an objective method for calibration and validation. The objective : – Investigate the capabilities of red-bands of the Sentinel-2 MSI sensor in detecting Chl-a in inland waters; – Calibrate and validate such a model.
    [Show full text]
  • An Overview of the Seawifs Project and Strategies for Producing a Climate Research Quality Global Ocean Bio-Optical Time Series
    ARTICLE IN PRESS Deep-Sea Research II 51 (2004) 5–42 An overview of the SeaWiFS project andstrategies for producing a climate research quality global ocean bio-optical time series Charles R. McClain*, Gene C. Feldman, Stanford B. Hooker Code 970.2, Office for Global Carbon Studies, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA Received1 April 2003; accepted19 November 2003 Abstract The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project Office was formally initiated at the NASA Goddard Space Flight Center in 1990. Seven years later, the sensor was launched by Orbital Sciences Corporation under a data- buy contract to provide 5 years of science quality data for global ocean biogeochemistry research. To date, the SeaWiFS program has greatly exceeded the mission goals established over a decade ago in terms of data quality, data accessibility andusability, ocean community infrastructure development,cost efficiency, andcommunity service. The SeaWiFS Project Office andits collaborators in the scientific community have madesubstantial contributions in the areas of satellite calibration, product validation, near-real time data access, field data collection, protocol development, in situ instrumentation technology, operational data system development, and desktop level-0 to level-3 processing software. One important aspect of the SeaWiFS program is the high level of science community cooperation andparticipation. This article summarizes the key activities andapproaches the SeaWiFS Project Office pursuedto define,achieve, and maintain the mission objectives. These achievements have enabledthe user community to publish a large andgrowing volume of research such as those contributedto this special volume of Deep-Sea Research. Finally, some examples of major geophysical events (oceanic, atmospheric, andterrestrial) capturedby SeaWiFS are presentedto demonstratethe versatility of the sensor.
    [Show full text]
  • Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data
    remote sensing Article Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data Min-Sun Lee 1,2, Kyung-Ae Park 2,3,* and Fiorenza Micheli 1,4,5 1 Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; [email protected] (M.-S.L.); [email protected] (F.M.) 2 Department of Earth Science Education, Seoul National University, Seoul 08826, Korea 3 Research Institute of Oceanography, Seoul National University, Seoul 08826, Korea 4 Department of Biology, Stanford University, Stanford, CA 94305, USA 5 Stanford Center for Ocean Solutions, Stanford University, Pacific Grove, CA 93950, USA * Correspondence: [email protected]; Tel.: +82-2-880-7780 Abstract: Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite- based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks.
    [Show full text]