Multitemporal Remote Sensing of Coastal Waters

  • Susanne Kratzer
  • Krista Alikas
  • Therese Harvey
  • José María Beltrán-Abaunza
  • Evgeny Morozov
  • Sélima Ben Mustapha
  • Samantha Lavender
Chapter
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 20)

Abstract

In this chapter we address some of the recent developments in marine coastal remote sensing with regards to the evaluation of water quality from space using multi-temporal data. Most chapters in this book are devoted to terrestrial applications, whereas aquatic remote sensing requires a completely different approach in terms of mission and sensor design as well as data analysis and processing. Therefore, the first section is a general introduction to marine remote sensing. Then we report recent results from remote sensing of the Baltic Sea, which is optically dominated by the absorption of light by coloured dissolved organic matter (CDOM), and during summer months, by high standing stocks of filamentous cyanobacteria. Results both from basin-wide as well as coastal applications in the north-western Baltic Sea are presented. In next section we report results from the Bay of Biscay in the north-eastern Atlantic Ocean west of France, which is an area highly influenced by river discharge and dinoflagellate blooms, and the subsequent section is about a coastal area in the eastern Beaufort Sea in the Arctic that’s influenced by a pool of CDOM. In all sections we discuss the relevance of regional remote sensing for ecological analysis and coastal management. The chapter concludes with a synthesis on merging of satellite data from different ocean colour missions and the limitations for coastal applications are discussed.

Abbreviations

Acronym

Explanation

AVHRR:

Advanced Very High Resolution Radiometer (NOAA)

Case-1 waters

Waters that are optically dominated by water itself and by Chl-a (and correlated CDOM)

Case-2 waters

Waters that are also optically significantly influenced by SPM and/or CDOM (besides water and Chl-a)

Chl-a

Chlorophyll-a

Chl-b

Chlorophyll-b

CDOM

Chromophoric or Coloured Dissolved Organic Matter

CZCS

Coastal Zone Colour Scanner (NASA)

DIN

Dissolved Inorganic Nitrogen

DIP

Dissolved Inorganic Phosphorus

EC

European Commission

ENVISAT

European ENVIronmental SATellite (ESA)

ESA

European Space Agency

EU

European Union

FR

Full resolution

FUB

Freie Universität Berlin

GMES

Global Monitoring of Environment and Security

GSM

Global System for Mobile communications

HELCOM

HELsinki COMmission

ICOL

Improved Contrast between Ocean and Land processor

IOPs

Inherent Optical Properties

MCI

Maximum Chlorophyll Index

MERIS

MEdium Resolution Imaging Spectrometer (ESA)

MLAC

Merged Local Area Coverage

MODIS

MODerate Imaging Spectroradiometer (NASA)

NASA

National Aeronautics and Space Administration

NIR

Near-InfraRed

NOAA

National Oceanic and Atmospheric Administration

NPP

Net Primary Production

NSIDC

National Snow and Ice Data Center

OC

Ocean Colour

OLCI

Ocean and Land Colour Instrument (ESA)

RGB

Red Green Blue

RR

Reduced resolution

SeaDAS

SeaWiFS Data Analysis Software

SeaWiFS

Sea-viewing Wide Field-of-view Sensor (NASA)

SPM

Suspended Particulate Matter

SST

Sea Surface Temperature

TOA

Top-of-Atmosphere

TSM

Total Suspended Matter

VIS

Visible

WFD

Water Framework Directive 2000/60/EC

MSFD

Marine Strategy Framework Directive 2008/56/EC

Optical coefficients

a

Absorption coefficient

b

Scattering coefficient

bb

Backward scattering coefficient

bf

Forward scattering coefficient

G440

Absorption coefficient of CDOM

I

Radiance

E

Irradiance

Ed

Downwelling Irradiance

Kd

Diffuse attenuation coefficient of downwelling irradiance

Rrs

Remote Sensing Reflectance

References

  1. Alikas K, Kangro K, Reinart A (2010) Detecting cyanobacterial blooms in large North European lakes using the Maximum Chlorophyll Index. Oceanologia 52:237–257CrossRefGoogle Scholar
  2. Alikas K, Kratzer S, Reinart A, Kauer T, Paavel B (2015) Robust remote sensing algorithms to derive the diffuse attenuation coefficient for lakes and coastal waters. Limnol Oceanogr Methods 13(8):402–415CrossRefGoogle Scholar
  3. Arrigo KR, van Dijken GL (2011) Secular trends in Arctic Ocean net primary production. JGeophys Res 116, C09011Google Scholar
  4. Austin RW, Petzold TJ (1981) The determination of the diffuse attenuation coefficient of sea water using the Coastal Zone Color Scanner. In: Garver JFR (ed) Oceanography from space. Springer, New York, pp 239–256CrossRefGoogle Scholar
  5. Behrenfeld MJ, Falkowski PG (1997) Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol Oceanogr 42(1):1–20CrossRefGoogle Scholar
  6. Beltrán-Abaunza JM, Kratzer, S and Höglander H (2016) Using the MERIS archive for the evaluation of spatial-temporal variability of water quality: the Himmerfjärden nitrogen experiment viewed from space. Int J of Remote Sens (in print)Google Scholar
  7. Ben Mustapha S (2013) Etude de la variabilité spatio-temporelle des processus biologiques et physiques dans la mer de Beaufort par télédétection et dans un contexte de changements climatiques en Océan Arctique. PhD thesis Université de Sherbrooke Québec Canada 263 ppGoogle Scholar
  8. Ben Mustapha S, Bélanger S, Larouche P (2012) Evaluation of ocean color algorithms in the southeastern Beaufort Sea, Canadian Arctic: new parameterization using SeaWiFS, MODIS, and MERIS spectral bands. Can J Remote Sens 38(5):1–22Google Scholar
  9. Binding CE, Greenberg TA, Jerome JH, Bukata RP, Letourneau G (2011) An assessment of MERIS algal products during an intense bloom in Lake of the Woods. J Plankton Res 33:793–806CrossRefGoogle Scholar
  10. CEC (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy (Official Journal of the European Communities No. L327, 1.22.12.2000) BrusselsGoogle Scholar
  11. CEC (2008) Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for Community action in the field of marine environmental policy (Marine Strategy Framework Directive) (Official Journal of the European Communities No. L164/19 25.06.2008) BrusselsGoogle Scholar
  12. Claquin P, Probert I, Lefebvre S, Veron B (2008) Effects of temperature on photosynthetic parameters and TEP production in eight species of marine microalgae. Aquat Microb Ecol 5:1–11CrossRefGoogle Scholar
  13. Comiso JC (2011) Large decadal decline of the Arctic multiyear ice cover. J Clim 25(4):1176–1193CrossRefGoogle Scholar
  14. Doerffer R, Sorensen K, Aiken J (1999) MERIS potential for coastal zone applications. Int JRemote Sens 20(9):1809–1818CrossRefGoogle Scholar
  15. Donlon C, Berruti B, Buongiorno A, Ferreira MH, Féménias P, Frerick J, Goryl P, Klein U, Laur H, Mavrocordatos C, Nieke J (2012) The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sens Environ 120:37–57CrossRefGoogle Scholar
  16. Elbraechter M, Schnepf E (1996) Gymnodinium chlorophorum, a new, green bloom forming Dinoflagellate(Gymnodiniales, Dinophyceae) with a vestigial Prasinophyte endosymbiont. Phycologia 35:381–393CrossRefGoogle Scholar
  17. Fanton d’Andon OH, Antoine D, Mangin A, Maritorena S, Durand D, Pradhan Y, Lavender S, Morel A, Demaria J, Barrot G (2008) Ocean colour sensors characterisation and expected error estimates of ocean colour merged products from GlobColour, Ocean Optics XIX, Barga, Italy, 6–10 October 2008, 51 ppGoogle Scholar
  18. Ferrari G, Dowell M (1998) CDOM absorption characteristics with relation to fluorescence and salinity in coastal areas of the southern Baltic Sea. Estuar Coast Shelf Sci 47:91–105CrossRefGoogle Scholar
  19. Galley RJ, Key E, Barber DG, Hwang BJ, Ehn JK (2008) Spatial and temporal variability of sea ice in the southern Beaufort Sea and Amundsen Gulf: 1980–2004. J Geophys Res 113(C5)Google Scholar
  20. Gower J, King S, Borstad G, Brown L (2008) The importance of band at 709 nm for interpreting water-leaving spectral radiance. Can J Remote Sens 34(3):287–295Google Scholar
  21. Gregg WW, Casey NW, O’Reilly JE, Esaias WE (2009) An empirical approach to ocean color data: reducing bias and the need for post-launch radiometric re-calibration. Remote Sens Environ 113:1598–1612CrossRefGoogle Scholar
  22. Guanter L, Ruiz-Verdú A, Odermatt D, Giardino C, Simis S, Estellés V, Heege T, Domínguez-Gómez JA, Moreno J (2010) Atmospheric correction of ENVISAT/MERIS data over inland waters: validation for European lakes. Remote Sens Environ 114(3):467–480CrossRefGoogle Scholar
  23. Hajdu S, Gorokhova E, Larsson U (2015) In-depth analysis of an alternate-stage Prymnesium polylepis (Haptophyta) bloom and long-term trends in abundance of Prymnesiales species in the Baltic Sea. Mar Ecol Prog Ser 526:55–66CrossRefGoogle Scholar
  24. Harvey T, Kratzer S, Philipson P (2015) Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters. Remote Sens Environ 158:417–430CrossRefGoogle Scholar
  25. Haykin S (1998) Neural networks. A comprehensive foundation. Upper Saddle River, NJ: Prentice Hall HELCOM, 2007. Baltic Sea Action Plan (HELCOM Ministerial Meeting). Krakow, PolandGoogle Scholar
  26. HELCOM (2007) The HELCOM Baltic Sea action plan. Krakow, Poland, p 15Google Scholar
  27. Hu C, Muller-Karger FE, Andrefouet S, Carder KL (2001) Atmospheric correction and cross-calibration of LANDSAT-7/ETM+ imagery over aquatic environments: a multiplatform approach using SeaWiFS/MODIS. Remote Sens Environ 78:99–107CrossRefGoogle Scholar
  28. IOCCG (2000) Remote sensing of ocean colour in coastal, and other optically-complex waters. In: Sathyendranath S (ed) Reports of the International Ocean-Colour Coordinating Group, No. 3. IOCCG, DartmouthGoogle Scholar
  29. IOCCG (2007) Ocean-colour data merging. In: Gregg W (ed) Reports of the International Ocean-Colour Coordinating Group, No. 6. IOCCG, DartmouthGoogle Scholar
  30. IOCCG (2008) Why ccean colour? The societal benefits of ocean-colour technology. In: Platt T, Hoepffner N, Stuart V, Brown C (eds) Reports of the International Ocean-Colour Coordinating Group, No. 7. IOCCG, DartmouthGoogle Scholar
  31. Isemer HJ, Rozwadowska A (1999) Solar radiation fluxes at the surface of the Baltic Proper. Part 2. Uncertainties and comparison with simple bulk parametrisations. Oceanologia 41(2):147–185Google Scholar
  32. Kahru M, Elmgren R (2014) Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences 11(13):3619–3633CrossRefGoogle Scholar
  33. Kahru M, Savchuk OP, Elmgren R (2007) Satellite measurements of cyanobacterial bloom frequency in the Baltic Sea: interannual and spatial variability. Mar Ecol Prog Ser 343:15–23CrossRefGoogle Scholar
  34. Kahru M, Brotas V, Manzano-Sarabia M, Mitchell BG (2011) Are phytoplankton blooms occurring earlier in the Arctic? Glob Chang Biol 17(4):1733–1739CrossRefGoogle Scholar
  35. Kahru M, Kudela RM, Manzano-Sarabia M, Mitchell BG (2012) Trends in the surface chlorophyll of the California Current: merging data from multiple ocean color satellites. Deep-Sea Res II Top Stud Oceanogr 77(80):89–98CrossRefGoogle Scholar
  36. Kirk JTO (2011) Light and photosynthesis in aquatic ecosystems, 3rd edn. Cambridge University Press, Cambridge, 649 ppGoogle Scholar
  37. Kratzer S, Tett P (2009) Using bio-optics to investigate the extent of coastal waters: A Swedish case study. Hydrobiologia 629(1):169–186CrossRefGoogle Scholar
  38. Kratzer S, Vinterhav C (2010) Improvement of MERIS level 2 products in Baltic Sea coastal areas by applying the Improved Contrast between Ocean and Land processor (ICOL)-data analysis and validation. Oceanologia 52(2):211–236CrossRefGoogle Scholar
  39. Kratzer S, Håkansson B, Sahlin C (2003) Assessing Secchi and photic zone depth in the Baltic Sea from Space. Ambio 32(8):577–585CrossRefGoogle Scholar
  40. Kratzer S, Brockmann C, Moore G (2008) Using MERIS full resolution data to monitor coastal waters – a case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea. Remote Sens Environ 112:2284–2300CrossRefGoogle Scholar
  41. Kratzer S, Harvey ET, Philipson P (2014) The use of ocean color remote sensing in integrated coastal zone management-a case study from Himmerfjärden, Sweden. Mar Policy 43:29–39CrossRefGoogle Scholar
  42. Lavender SJ, Raitsos DE, Pradhan Y (2008) Variations in the phytoplankton of the North-Eastern Atlantic ocean: from the Irish Sea to the Bay of Biscay. In: Barale V, Gade M (eds) Remote sensing of the European seas. Springer, Dordrecht, pp 67–78CrossRefGoogle Scholar
  43. Leppäranta M, Myrberg K (2009) Physical oceanography of the Baltic sea. Springer, Berlin/Heidelberg/New York, 378 ppCrossRefGoogle Scholar
  44. Maritorena S, Fanton d’Andon OH, Mangin A, Siegel DA (2010) Merged satellite ocean color data products using a bio-optical model: characteristics, benefits and issues. Remote Sens Environ 114:1791–1804CrossRefGoogle Scholar
  45. Maslanik JA, Serreze MC, Agnew T (1999) On the record reduction in 1998 western Arctic sea-ice cover. Geophys Res Lett 26:1905–1908CrossRefGoogle Scholar
  46. Matsumoto T, Shinozaki F, Chikuni T, Yabuki A, Takishita K, Kawachi M, Nakayama T, Inouye I (2011) Chlorophyte origin. Protist 162:268–276CrossRefGoogle Scholar
  47. McClain CR (2009) A decade of satellite ocean color observations. Annu Rev Mar Sci 1(1):19–42CrossRefGoogle Scholar
  48. McClain EP, Pichel WG, Walton CC (1985) Comparative performance of AVHRR-based multichannel sea surface temperatures. J Geophys Res 90:11587–11601CrossRefGoogle Scholar
  49. Morel A (1980) In-water and remote measurements of ocean color. Bound-Layer Meteorol 18:177–201CrossRefGoogle Scholar
  50. Morozov EA, Korosov AA, Pozdnyakov DV, Pettersson LH, Sychev VI (2010) A new area-specific bio-optical algorithm for the Bay of Biscay and assessment of its potentials for SeaWiFS and MODIS/Aqua data merging. Int J Remote Sens 31(24):6541–6565CrossRefGoogle Scholar
  51. Morozov E, Pozdnyakov D, Smyth T, Sychev V, Grassl H (2013) Space-borne study of seasonal, multi-year, and decadal phytoplankton dynamics in the Bay of Biscay. Int J Remote Sens 34(4):1297–1331CrossRefGoogle Scholar
  52. Nazeer M, Nichol JE (2015) Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal water of Hong Kong. J Hydrol 532:80–89CrossRefGoogle Scholar
  53. Öberg J (2013) Cyanobacterial blooms in the Baltic Sea in 2013. HELCOM Baltic Sea Environment Fact Sheet 2013. http://helcom.fi/baltic-sea-trends/environment-fact-sheets/eutrophication/cyanobacterial-blooms-in-the-baltic-sea
  54. Ocean Colour Climate Change Initiative (2015) Product user guide issue 2.0.5, Retrieved from http://www.esaoceancolour-cci.org/
  55. OSPAR Commission (1992) On the assessment of the quality of the marine environment, Annex IV, OSPAR ConventionGoogle Scholar
  56. Pierson D, Kratzer S, Strömbeck N, Håkansson B (2008) Relationship between the attenuation of downwelling irradiance at 490 nm with the attenuation of PAR (400 nm- 700 nm) in the Baltic Sea. Remote Sens Environ 112(3):668–680CrossRefGoogle Scholar
  57. Pinnock S, D’Andon OF, Lavender S (2007) GlobColour-a precursor to the GMES marine core service ocean Colour. Thematic Assembly Centre. ESA Bull 132:42–49Google Scholar
  58. Preisendorfer RW (1986) Secchi disk science; visual optics of natural waters. Limnol Oceanogr 31:906–926CrossRefGoogle Scholar
  59. Robinson IS (2004) Measuring the oceans from space: the principles and methods of satellite oceanography. Springer, Berlin/New York, 670 ppGoogle Scholar
  60. Ruddick K, De Cauwer V, Park Y, Moore G (2006) Seaborne measurements of near infrared water-leaving reflectance: the similarity spectrum for turbid waters. Limnol Oceanogr 51(2):1167–1179CrossRefGoogle Scholar
  61. Ruddick K, Lacroix G, Park Y, Rousseau V, De Cauwer V, Sterckx S (2008) Overview of ocean colour: theoretical background, sensors and applicability for the detection and monitoring of harmful algae blooms (capabilities and limitations). In: Babin M, Roesler C, Cullen JJ (eds) Realtime coastal observing systems for ecosystem dynamics and harmful algal blooms, UNESCO monographs on oceanographic methodology series. UNESCO publishing, ParisGoogle Scholar
  62. Ruescas A, Brockmann C, Stelzer K, Tilstone G, Beltrán-Abaunza JM (2014) Coastcolour validation report. Deliverable DEL-27. Version 1.0. Brockmann Consult. GeesthachtGoogle Scholar
  63. Santer R, Schmechtig C (2010) Adjacency effects on water surfaces: primary scattering approximation and sensitivity study. Appl Opt 39(3):361–375CrossRefGoogle Scholar
  64. Sathyendranath S (ed) (2000) Remote sensing of ocean colour in coastal, and other optically-complex, waters. IOCCG report number 3. MacNab Print, Dartmouth, Canada: International Ocean-Colour Coordinating Group. Retrieved from http://www.ioccg.org/reports/report3.pdf
  65. Schroeder T, Behner I, Schaale M, Fischer J, Doerffer R (2007a) Atmospheric correction algorithm for MERIS above case‐2 waters. Int J Remote Sens 28:1469–1486CrossRefGoogle Scholar
  66. Schroeder T, Schaale M, Fischer J (2007b) Retrieval of atmospheric and oceanic properties from MERIS measurements: a new Case‐2 water processor for BEAM. Int J Remote Sens 28:5627–5632CrossRefGoogle Scholar
  67. Siegel H, Gerth M (2013) Sea surface temperature in the Baltic sea in 2012. Baltic Sea Environment Fact Sheet 2013. http://helcom.fi/baltic-sea-trends/environment-fact-sheets/hydrography/development-of-sea-surface-temperature-in-the-baltic-sea
  68. Siegel DA, Maritorena S, Nelson NB, Behrenfeld MJ (2005) Independence and interdependencies among global ocean color properties: reassessing the bio‐optical assumption. J Geophys Res: Oceans 110(C7)Google Scholar
  69. Steinmetz F, Deschamps PY, Ramon D (2011) Atmospheric correction in presence of sun glint: application to MERIS. Opt Express 19(10):9783–9800CrossRefGoogle Scholar
  70. Sterckx S, Knaeps E, Ruddick K (2011) Detection and correction of adjacency effects in hyperspectral airborne data of coastal and inland waters: the use of the near infrared similarity. Int J Remote Sens 32(21):6479–6505CrossRefGoogle Scholar
  71. Sterckx S, Knaeps E, Kratzer S, Ruddick K (2015) SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sens Environ 157:96–110CrossRefGoogle Scholar
  72. Tett P (1990) The photic zone. In: Herring PJ, Campbell AK, Whitfield M, Maddock L (eds) Light and life in the sea. Cambridge University Press, Cambridge, pp 59–87Google Scholar
  73. Tremblay J-É, Hattori H, Michel C, Ringuette M, Mei Z-P, Lovejoy C, Fortier L, Hobson KA, Amiel D, Cochran JK (2006) Trophic structure and pathways of biogenic carbon flow in the eastern North Water Polynya. Prog Oceanogr 71:402–425CrossRefGoogle Scholar
  74. Wang M, Son S, Harding LW (2009) Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications. J Geophys Res Oceans 114(C10), C10011CrossRefGoogle Scholar
  75. Whitehouse BG, Hutt D (2006) Observing coastal waters with spaceborne sensors. In: Richardson LL, LeDrew EF (eds) Remote sensing of aquatic coastal ecosystem processes. Springer, Dortdrecht, pp 201–215CrossRefGoogle Scholar
  76. Williams WJ, Carmack EC (2008) Combined effect of windforcing and isobath divergence on upwelling at Cape Bathurst, Beaufort Sea. J Mar Res 66:645–663CrossRefGoogle Scholar
  77. Zibordi G, Ruddick K, Ansko I, Moore G, Kratzer S, Icely J, Reinart A (2012) In situ determination of the remote sensing reflectance: an inter-comparison. Ocean Sci 8:567–586CrossRefGoogle Scholar
  78. Zimmermann HJ (2001) Fuzzy set theory. Kluwer Academic Publishers, Boston, 514 ppGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Susanne Kratzer
    • 1
  • Krista Alikas
    • 2
  • Therese Harvey
    • 1
  • José María Beltrán-Abaunza
    • 1
  • Evgeny Morozov
    • 1
    • 3
  • Sélima Ben Mustapha
    • 1
    • 4
  • Samantha Lavender
    • 5
  1. 1.Department of Ecology, Environment and Plant SciencesStockholm UniversityStockholmSweden
  2. 2.Department of Remote SensingTartu ObservatoryTartumaaEstonia
  3. 3.NIERSCSt. PetersburgRussia
  4. 4.Institut Maurice-Lamontagne, Peches et Océans CanadaMont-JoliCanada
  5. 5.Pixalytics LtdPlymouthUK

Personalised recommendations