International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

Inter-comparison of single-sensor and merged multi-sensor ocean color chlorophyll-a products in the shallow turbid waters - case study: Persian Gulf

Document Type : Original Research Article

Author
Masoud Moradi
Iranian National Institute of Oceanography and Atmospheric Science
10.22034/ijcoe.2022.155185
Abstract
Ocean color satellite sensors provide the only long-term Essential Climate Variable (ECV) globally that targets Chlorophyll-a concentrations (Chl-a) as the most important biological factor in the oceans. It is difficult to develop the long-term and consistent ocean color time-series for climate studies due to the differences in characteristics, atmospheric correction, Chl-a retrieval algorithms, and limited lifespans of individual satellite sensors. Therefore, the merged multi-sensor ocean color datasets were developed by merging data from different satellite sensor products. The performance of the commonly used single-sensor and multi-sensor merged ocean color datasets is a challenging issue over highly turbid coastal waters and dusty atmospheric conditions. In this study, we compared the common single-sensor [Sea-viewing Wide Field-ofview Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), Visible Imager Radiometer (VIIRS), and Sentinel-3 Ocean and Land Colour Instrument (OLCI)], and merged multi-sensor [Ocean Colour Climate Change Initiative (OC-CCI), and GlobColour weighted average (GC-AVW) and Garver-SiegelMaritorena (GC-GSM)] Chl-a datasets over the Persian Gulf, known as optically complex and highly turbid water bodies in a dusty atmospheric condition. The results indicate that the OC-CCI dataset provides more spatial and temporal coverages than the other datasets. Temporal consistency between single-sensor and merged datasets was made in two different timespans during the common period of sensors and during the continuous lifespan intersection between individual two-paired of datasets. The statistical metrics were calculated to show the temporal consistency between Chl-a datasets during the common and continuous time periods. Correlation between OC-CCI and the other datasets showed that the relationships between datasets did not change significantly during the proposed time periods. Further, it was indicated that the OC-CCI product is more constant than the other single-sensor and merged products. It was shown that OC-CCI datasets were more consistent with MERIS and GC-GSM datasets, and SeaWiFS and GC-AVW were not significantly correlated to the other datasets. The results revealed that the single sensor products that use POLYMER atmospheric correction algorithm (e.g. MERIS), and merged multi-sensor product that performs the GSM blending algorithms (e.g. GC-GSM) are more consistent and stable than the other products over the study area.
Keywords
Remote sensing
Phytoplankton
Spatial coverage
Complex waters
Dusty atmosphere
  1. Platt T, Stuart (2008). Why Ocean Colour? The Societal Benefits of Ocean-Colour Radiometry. vliz.be. 1–8.
  2. Longhurst A, Sathyendranath S, Platt T, Caverhill C. (1995). An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research. 17(6):1245–71.
  3. Muller-Karger FE, Kavanaugh MT, Montes E, Balch WM, Breitbart M, Chavez FP, et al. (2014). A framework for a marine biodiversity observing network within changing continental shelf seascapes. Vol. 27, Oceanography. p. 18–23.
  4. Sathyendranath S, Brewin RJ, Brockmann C, Brotas V, Calton B, Chuprin A, et al. (2019). An ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI). Sensors. 19(19):4285.
  5. Beaulieu C, Henson SA, Sarmiento JL, Dunne JP, Doney SC, Rykaczewski RR, et al. (2013). Factors challenging our ability to detect long-term trends in ocean chlorophyll. Biogeosciences. 10(4):2711–24.
  6. Racault MF, Le Quéré C, Buitenhuis E, Sathyendranath S, Platt T. (2012). Phytoplankton phenology in the global ocean. Ecological Indicators. 14(1):152–63.
  7. Steinmetz F, Deschamps P-Y, Ramon D. (2011). Atmospheric correction in presence of sun glint: application to MERIS. Optics Express. 19(10):9783.
  8. Garnesson P, Mangin A, D’Andon OF, Demaria J, Bretagnon M. (2019). The CMEMS GlobColour chlorophyll a product based on satellite observation: Multi-sensor merging and flagging strategies. Ocean Science. 15(3):819–30.
  9. Hu C, Lee Z, Franz B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans. 117(1):1011.
  10. Gohin F, Druon JN, Lampert L. (2002). A five channel chlorophyll concentration algorithm applied to Sea WiFS data processed by SeaDAS in coastal waters. International Journal of Remote Sensing. 23(8):1639–61.
  11. Morel A, Huot Y, Gentili B, Werdell PJ, Hooker SB, Franz BA. (2007). Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sensing of Environment. 111(1):69–88.
  12. Brewin RJW, Sathyendranath S, Müller D, Brockmann C, Deschamps PY, Devred E, et al. (2015). The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms. Remote Sensing of Environment. 162.
  13. Seegers BN, Stumpf RP, Schaeffer BA, Loftin KA, Werdell PJ. (2018). Performance metrics for the assessment of satellite data products: an ocean color case study. Optics Express. 26(6):7404.
  14. Campbell JW. (1995). The lognormal distribution as a model for bio-optical variability in the sea. Journal of Geophysical Research. 100(C7).
  15. Gregg WW, Casey NW, McClain CR. (2005). Recent trends in global ocean chlorophyll. Geophysical Research Letters. 32(3):1–5.
  16. Jolliff JK, Kindle JC, Shulman I, Penta B, Friedrichs MAM, Helber R, et al. (2009). Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment. Journal of Marine Systems. 76(1–2):64–82.
  17. Saba VS, Friedrichs MAM, Antoine D, Armstrong RA, Asanuma I, Behrenfeld MJ, et al. (2011). An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences. 8(2):489–503.
  18. Djavidnia S, Mélin F, (2010). Comparison of global ocean colour data records. os.copernicus.org. 6:61–76.
  19. Werdell PJ, Franz BA, Bailey SW, Harding, Jr. LW, Feldman GC. (2007). Approach for the long-term spatial and temporal evaluation of ocean color satellite data products in a coastal environment. In: Coastal Ocean Remote Sensing. p. 66800G.
  20. Taylor KE. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research Atmospheres. 106(D7):7183–92.
  21. Belo Couto A, Brotas V, Mélin F, Groom S, Sathyendranath S. (2016). Inter-comparison of OC-CCI chlorophyll-a estimates with precursor data sets. International Journal of Remote Sensing. 37(18):4337–55.
  22. Moradi M. (2021). Evaluation of merged multi-sensor ocean-color chlorophyll products in the Northern Persian Gulf. Continental Shelf Research. 221:104415.
  23. Al Shehhi MR, Gherboudj I, Ghedira H. (2014). An overview of historical harmful algae blooms outbreaks in the Arabian Seas. Marine Pollution Bulletin. 86(1–2):314–24.
  24. Moradi M, Kabiri K. (2012). Red tide detection in the Strait of Hormuz (east of the Persian Gulf) using MODIS fluorescence data. International Journal of Remote Sensing. 33(4):1015–28.
  25. Richlen ML, Morton SL, Jamali EA, Rajan A, Anderson DM. (2010). The catastrophic 2008-2009 red tide in the Arabian gulf region, with observations on the identification and phylogeny of the fish-killing dinoflagellate Cochlodinium polykrikoides. Harmful Algae. 9(2):163–72.
  26. Moradi M. (2020). Trend analysis and variations of sea surface temperature and chlorophyll-a in the Persian Gulf. Marine Pollution Bulletin. 156.
  27. Moradi M, Moradi N. (2021). Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf. Marine Pollution Bulletin. 16