Ocean colour remote sensing in high latitude environments (1)
Ocean colour remote sensing has often been used to study polar seas, especially in Antarctica where
the optical properties of the upper ocean are not as complex as they are in the Arctic (Comiso et al.,
1990, Comiso et al., 1993, Sullivan et al., 1993, Arrigo et al., 1998, Stramski et al., 1999, Arrigo et al.,
2008b). It was shown based on OC data that primary production in Antarctic waters has changed little
over the last 14 years (Arrigo et al., 2008b). In contrast, the few studies that have been conducted to
date in the Arctic Ocean suggest that pan‐Arctic primary production, as well as photooxidation of
coloured dissolved organic matter have been increasing (Belanger et al., 2006, Pabi et al., 2008, Arrigo
et al., 2008a) as a consequence of receding perennial ice. The annual maximum phytoplankton
biomass is now reached earlier in several Arctic seas (Kahru et al., 2010).
As the extent of the seasonal ice zone increases (difference between the annual maximum and minimum extents), ice‐edge blooms may play a heightened role (Perrette et al., 2011).
The on‐going changes within the context of accelerating climate change necessitate a vastly improved understanding of the polar ecosystems based on an intensive observation program. The use of ocean colour remote sensing in polar regions is, however, impeded by a number of difficulties and intrinsic limitations including:
- The prevailing low solar elevations. At high latitudes, the Sun zenith angle is often larger than the maximum (generally 70°) for which atmospheric correction algorithms have been developed based on plane‐parallel radiative transfer calculations. Consequently, at high latitudes, a large fraction of the ocean surface is undocumented for a large part of the year even though primary production may be significant.
- The impact of ice on remotely sensed reflectance. Belanger et al (2007) and Wang & Shi (2009), used radiative transfer simulations to examine the effects of the sea ice adjacency and sub‐pixel ice contamination on retrieved seawater reflectance and level‐2 ocean products. They found significant impacts within the first several kilometres from the ice‐edge and for concentrations of sub‐pixel ice floes exceeding a few percent.
- The deep chlorophyll maximum (DCM). A DCM is very often observed both in the Antarctic and Arctic Oceans. In the Arctic Ocean, the freeze‐thaw cycle of sea ice and the large export of freshwater to the ocean by large Arctic rivers create pronounced haline stratification within the surface layer. In post‐bloom conditions, a deep‐chlorophyll maximum is associated with such vertical stratification.
- Contrary to the DCM observed at lower latitudes (Cullen, 1982), the Arctic DCM often corresponds to a maximum in particulate carbon and primary production (Martin et al., 2010). The statistical relationships between surface chlorophyll and chlorophyll concentration at depth developed for lower latitudes (Morel & Berthon, 1989) are most probably not valid for the polar seas (Martin et al., 2010).
- Ignoring the vertical structure of the chlorophyll profile in the Arctic Ocean leads to significant errors in the estimation of the areal primary production (Pabi et al., 2008, Hill & Zimmerman, 2010).
- The peculiar phytoplankton photosynthetic parameters. The low irradiance and seawater temperature prevailing in polar seas are associated with unique biooptical and photosynthetic parameters characteristic of extreme environments (Rey, 1991) that must be accounted for in primary production models. To date, only a few studies have attempted to do so in the Arctic Ocean (Arrigo et al., 2008b).
- The optical complexity of seawater, especially over the Arctic shelves. Because of the important freshwater inputs, the Arctic continental shelves, which occupy 50% of the area, are characterized by high concentrations of CDOM (Matsuoka et al., 2007, Belanger et al., 2008). Also, as a consequence of photoacclimation to low irradiances, phytoplankton cells often contain large amounts of pigments. The chlorophyll‐specific absorption coefficient is therefore particularly low due to pronounced pigment packaging (Cota et al., 2003, Wang et al., 2005). Because of these optical peculiarities, standard ocean colour algorithms do not work in the Arctic Ocean (Cota et al., 2004, Matsuoka et al., 2007).
- The persistence of clouds and fog. High latitudes are known to present a heavy cloud cover. In addition, as soon as sea ice melts and opens waters come in direct contact with the atmosphere, fog develops near the sea surface. These features limit the usage of ocean colour data.
- Ocean colour remote sensing in polar seas
- Ocean, sea ice and atmosphere in Arctic and Antarctic: relevant features
- Seawater optical properties
- Retrieval of ocean properties from ocean colour:
- Atmospheric corrections
- Contamination of the signal by sea ice
- Retrieval of IOPs and AOPs, and biogeochemically relevant variables
- Availability of data as favoured by polar orbits and limited by elevated Cloudiness
- Primary production estimates from OC in polar seas
- General features of Arctic and Antarctic Oceans related to PP (phytoplankton species, annual cycle of PP, nutrients, DCM)
- PP models and their validation
- Results from PP models
Arrigo KR, Van Dijken G, Pabi S, 2008a. Impact of a shrinking Arctic ice cover on marine primary
production. Geophysical Research Letters 35.
Arrigo KR, Van Dijken GL, Bushinsky S, 2008b. Primary production in the Southern Ocean, 1997 2006. Journal of Geophysical ResearchOceans 113.
Arrigo KR, Worthen D, Schnell A, Lizotte MP, 1998. Primary production in Southern Ocean waters. Journal of Geophysical ResearchOceans 103, 15587‐600.
Belanger S, Babin M, Larouche P, 2008. An empirical ocean color algorithm for estimating the contribution of chromophoric dissolved organic matter to total light absorption in optically complex waters. Journal of Geophysical ResearchOceans 113.
Belanger S, Ehn JK, Babin M, 2007. Impact of sea ice on the retrieval of water‐leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data. Remote Sensing of Environment 111, 51‐68.
Belanger S, Xie HX, Krotkov N, Larouche P, Vincent WF, Babin M, 2006. Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: Interannual variability and implications of climate change. Global Biogeochemical Cycles 20.
Comiso JC, Maynard NG, Smith WO, Sullivan CW, 1990. Satellite Ocean Color Studies of Antarctic Ice Edges in Summer and Autumn. Journal of Geophysical ResearchOceans 95, 9481‐96.
Comiso JC, Mcclain CR, Sullivan CW, Ryan JP, Leonard CL, 1993. Coastal Zone Color Scanner Pigment Concentrations in the Southern‐Ocean and Relationships to Geophysical Surface‐Features. Journal of Geophysical ResearchOceans 98, 2419‐51.
Cota GF, Harrison WG, Platt T, Sathyendranath S, Stuart V, 2003. Bio‐optical properties of the Labrador Sea. Journal of Geophysical ResearchOceans 108.
Cota GF, Wang H, Comiso JC, 2004. Transformation of global satellite chlorophyll retrievals with a regionally tuned algorithm. Remote Sensing of Environment 90, 373‐7.
Cullen JJ, 1982. The Deep Chlorophyll Maximum ‐ Comparing Vertical Profiles of Chlorophyll‐A. Canadian Journal of Fisheries and Aquatic Sciences 39, 791‐803.
Hill VJ, Zimmerman RC, 2010. Estimates of primary production by remote sensing in the Arctic Ocean: Assessment of accuracy with passive and active sensors. DeepSea Research Part IOceanographic Research Papers 57, 1243‐54.
Kahru M, Brotas M, Manzano‐Sarabia M, Mitchell BG, 2010. Are phytoplankton blooms occurring earlier in theArctic? Global change biology doi: 10.111/j.13652486.2010.02312.x.
Martin J, Tremblay JE, Gagnon J, et al., 2010. Prevalence, structure and properties of subsurface chlorophyll maxima in Canadian Arctic waters. Marine EcologyProgress Series 412, 69‐84.
Matsuoka A, Huot Y, Shimada K, Saitoh SI, Babin M, 2007. Bio‐optical characteristics of the western Arctic Ocean: implications for ocean color algorithms. Canadian Journal of Remote Sensing 33, 503‐18. Morel A, Berthon JF, 1989. Surface Pigments, Algal Biomass Profiles, and Potential Production of the Euphotic Layer ‐ Relationships Reinvestigated in View of Remote‐Sensing Applications. Limnology and Oceanography 34, 1545‐62.
Pabi S, Van Dijken GL, Arrigo KR, 2008. Primary production in the Arctic Ocean, 1998‐2006. Journal of Geophysical ResearchOceans 113.
Perrette M, Yool A, Quartly GD, Popova EE, 2011. Near‐ubiquity of ice‐edge blooms in the Arctic. Biogeosciences 8, 515‐24.
Rey F, 1991. Photosynthesis‐Irradiance Relationships in Natural Phytoplankton Populations of the Barents Sea. Polar Research 10, 105‐16.
Stramski D, Reynolds RA, Kahru M, Mitchell BG, 1999. Estimation of particulate organic carbon in the ocean from satellite remote sensing. Science 285, 239‐42.
Sullivan CW, Arrigo KR, Mcclain CR, Comiso JC, Firestone J, 1993. Distributions of Phytoplankton Blooms in the Southern‐Ocean. Science 262, 1832‐7.
Wang J, Cota GF, Ruble DA, 2005. Absorption and backscattering in the Beaufort and Chukchi Seas. Journal of Geophysical ResearchOceans 110.
Wang MH, Shi W, 2009. Detection of Ice and Mixed Ice‐Water Pixels for MODIS Ocean Color Data Processing. Ieee Transactions on Geoscience and Remote Sensing 47, 2510‐8.
Speaker(s) : Marcel Babin, Excellence Research Chair in Remote Sensing of Canada'sNew Arctic Frontier Université Laval, Québec, QC, Canada, G1V 0A6
Public : All
Date : Friday 13 july 2012
Place : Villefranche-sur-Mer
- Ocean Colour Algorithms (1)
- Inherent optical properties of ocean waters
- Ocean Colour Algorithms (2)
- Inversion of inherent optical properties from remote sensing
- Hyperspectral remote sensing of optically shallow waters
- Atmospheric correction issues unique to shallow waters
- Iop applications
- Techniques used for inverting atmospherically corrected rrs spectra
- Improved ocean ecosystem predictions through improved light calculations
- Ecosystem predictions using accurate radiative transfer models
- Above and in water radiometry: methods and calibration requirements
- Uncertainty analysis and application of in situ radiometric products
- In Situ Measurements (1)
- Errors and uncertainties in ocean colour remote sensing (1)
- In Situ Measurements (2)
- Errors and uncertainties in ocean colour remote sensing (2)
- High-resolution hyperspectral oc rs in coastal areas (1)
- High-resolution hyperspectral oc rs in coastal areas (2)
- Atmospheric correction of ocean colour rs observations (1)
- Use and importance of oc remote sensing in global coupled bgc models
- Atmospheric correction of ocean colour rs observations (2)
- Ocean colour remote sensing in turbid coastal waters (1)
- Using the oc time series to address climate change
- Atmospheric correction of ocean colour rs observations (3)
- Ocean colour remote sensing in turbid coastal waters (2)
- Harmful algal blooms: the contrast with other algal blooms (1)
- Ocean colour remote sensing in high latitude environments (1)
- Harmful algal blooms: the contrast with other algal blooms (2)
- Phytoplankton fluorescence: theory and interpretation from oc remote sensing (2)
- Ocean colour remote sensing in high latitude environments (2)
- Phytoplankton fluorescence: theory and interpretation from oc remote sensing (1) Detailed list
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