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

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

Assessment of long-term consistency of ocean-color satellite-derived chlorophyll-a products in the Persian Gulf

Document Type : Original Research Article

Author
Masoud Moradi 1, 2
1 Iranian National Institute of Oceanography and Atmospheric Science (INIOAS)
2 Center for International Scientific Studies & Collaboration (CISSC), ministry of Science Research and Technology, Tehran, Iran
10.22034/ijcoe.2022.160790
Abstract
Over the past two decades, several ocean color satellites have operated in parallel. The combination of different ocean color satellite sensor products is a vital task for studying the biogeochemistry of seas. In this study, we evaluated the temporal consistency of the monthly time-series and monthly interannual variations of satellite-derived chlorophyll-a concentrations (Chl-a) from four satellite sensors during 2002-2020 period over the Persian Gulf. Statistical correlation between Chl-a time series and anomalies from selected satellite sensors were significantly correlated for 84% area of the Persian Gulf. Correlations were reasonably sensitive to the choice of Chl-a retrieval and atmospheric correction algorithms. The standard algorithms for Chl-a retrieval showed the lowest value of correlations, and it was indicated that these algorithms were not suitable for Chl-a estimations from satellite sensors over the Persian Gulf. The OCI algorithm for Chl-a retrieval showed more consistency among different satellite sensors and it was shown that it is more suitable than previous ones for estimation of Chl-a from selected satellite sensors. Also, it was shown that the SeaDAS and POLYMER atmospheric correction algorithms have a great influence on the Chl-a estimations from selected satellite sensors. It was shown that more than 70% of the study area indicated imperfect consistency between selected atmospheric correction algorithms applied on different satellite sensors. Choosing the best atmospheric correction and Chl-a retrieval algorithms is the most important task in the estimation and utilization of Chl-a in the Persian Gulf.
Keywords
Remote sensing
Ocean color
Atmospheric correction
Coastal waters
Correlation analysis
  1. 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.
  2. Gregg WW, Rousseaux CS. (2014). Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models. Journal of Geophysical Research C: Oceans.119(9):5921–33.
  3. 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.
  4. Brickley PJ, Thomas AC. (2004). Satellite-measured seasonal and inter-annual chlorophyll variability in the Northeast Pacific and Coastal Gulf of Alaska. Deep-Sea Research Part II: Topical Studies in Oceanography.51(1–3):229–45.
  5. Kim W, Moon JE, Park Y, Ishizaka J. (2016). Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region. Remote Sensing of Environment. 184:482–95.
  6. Song H, Long MC, Gaube P, Frenger I, Marshall J, McGillicuddy DJ. (2018). Seasonal Variation in the Correlation Between Anomalies of Sea Level and Chlorophyll in the Antarctic Circumpolar Current. Geophysical Research Letters. 45(10):5011–9.
  7. Mannino A, Novak MG, Nelson NB, Belz M, Berthon J-F, Blough N V., et al. (2019). Measurement protocol of absorption by chromophoric dissolved organic matter (CDOM) and other dissolved materials. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation. 1:1–77.
  8. Blauw AN, Benincà E, Laane RWPM, Greenwood N, Huisman J. (2018). Predictability and environmental drivers of chlorophyll fluctuations vary across different time scales and regions of the North Sea. Progress in Oceanography. 161:1–18.
  9. 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.
  10. Allison DB, Stramski D, Mitchell BG. (2010). Seasonal and interannual variability of particulate organic carbon within the Southern Ocean from satellite ocean color observations. Journal of Geophysical Research: Oceans. 115(6).
  11. (2014). Phytoplankton functional types from Space. Reports and Monographs of the International OceanColour Coordinating Group. (15).
  12. Westberry TK, Schultz P, Behrenfeld MJ, Dunne JP, Hiscock MR, Maritorena S, et al. (2016). Annual cycles of phytoplankton biomass in the subarctic Atlantic and Pacific Ocean. Global Biogeochemical Cycles. 30(2):175–90.
  13. Friedland KD, Mouw CB, Asch RG, Ferreira ASA, Henson S, Hyde KJW, et al. (2018). Phenology and time series trends of the dominant seasonal phytoplankton bloom across global scales. Global Ecology and Biogeography. 1;27(5):551–69.
  14. Shang SL, Dong Q, Hu CM, Lin G, Li YH, Shang SP. (2014). On the consistency of MODIS chlorophyll-a products in the northern South China Sea. Biogeosciences. 11(2):269–80.
  15. Al-Naimi N, Raitsos DE, Ben-Hamadou R, Soliman Y. (2017). Evaluation of satellite retrievals of chlorophyll-a in the Arabian Gulf. Remote Sensing. 2017; 9(3).
  16. Moradi M, Kabiri K. (2015). Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf. Marine Pollution Bulletin. 98(1–2):14–25.
  17. Swift SA, Bower AS. (2003). Formation and circulation of dense water in the Persian/Arabian Gulf. Journal of Geophysical Research: Oceans. 108(1).
  18. Vaughan GO, Al-Mansoori N, Burt JA. (2018). The arabian gulf. In: World Seas: An Environmental Evaluation Volume II: The Indian Ocean to the Pacific., 1–23.
  19. Paparella F, Xu C, Vaughan GO, Burt JA. (2019). Coral bleaching in the Persian/Arabian Gulf is modulated by summer winds. Frontiers in Marine Science. 6(1).
  20. Al-Yamani F, Naqvi SWA. (2019). Chemical oceanography of the Arabian Gulf. Deep-Sea Research Part II: Topical Studies in Oceanography. 161:72–80.
  21. Hunter JR. (1983). Aspects of the dynamics of the residual circulation of the Arabian Gulf. Coastal Oceanography. 31–42.
  22. Nezlin NP, Polikarpov IG, Al-Yamani F. (2007). Satellite-measured chlorophyll distribution in the Arabian Gulf: Spatial, seasonal and inter-annual variability. International Journal of Oceans and Oceanography. 2(1):139–56.
  23. Nezlin NP, Polikarpov IG, Al-Yamani FY, Subba Rao D V., Ignatov AM. (2010). Satellite monitoring of climatic factors regulating phytoplankton variability in the Arabian (Persian) Gulf. Journal of Marine Systems. 82(1–2):47–60.
  24. Ghanea M, Moradi M, Kabiri K. (2015). Investigation the behavior of MODIS ocean color products under the 2008 red tide in the eastern Persian Gulf. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. p. 227–32.
  25. Fallahi, M., Torabi Azad, M., & Mansoury, D. (2021). A Hydrodynamic Model of Tidal Current in the Strait of Hormuz. International Journal of Coastal and Offshore Engineering, 5(1), 37-45.
  26. Moradi M, Moradi N. (2020). Correlation between concentrations of chlorophyll-a and satellite derived climatic factors in the Persian Gulf. Marine Pollution Bulletin. 161.
  27. Moradi, M. (2022). Inter-comparison of single-sensor and merged multi-sensor ocean color chlorophyll-a products in the shallow turbid waters-case study: Persian Gulf. International Journal of Coastal and Offshore Engineering, 7(2), 1-10.
  28. 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.
  29. Bailey SW, Werdell PJ. (2006). A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment. 102(1–2):12–23.
  30. Hu C, Feng L, Lee Z. (2013). Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements. Remote Sensing of Environment. 133:168–82.
  31. Uitz J, Claustre H, Gentili B, Stramski D. (2010). Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Global Biogeochemical Cycles. 24(3).
  32. Vantrepotte V, Loisel H, Mlin F, Desailly D, Duforêt-Gaurier L. (2011). Global particulate matter pool temporal variability over the SeaWiFS period (1997-2007). Geophysical Research Letters. 38(2).
  33. Loisel H, Vantrepotte V, Ouillon S, Ngoc DD, Herrmann M, Tran V, et al. (2017). Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002–2012). Remote Sensing of Environment. 190:217–32.
  34. Cui T, Zhang J, Tang J, Sathyendranath S, Groom S, Ma Y, et al. (2021). Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea). ISPRS Journal of Photogrammetry and Remote Sensing. 87:137–51.
  35. Yu Z, Yang K, Luo Y, Shang C. (2020). Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network. Journal of Hydrology. 582.
  36. Le C, Hu C, Cannizzaro J, Duan H. (2013). Long-term distribution patterns of remotely sensed water quality parameters in Chesapeake Bay. Estuarine, Coastal and Shelf Science. 128:93–103.
  37. Campbell JW. (1995). The lognormal distribution as a model for bio-optical variability in the sea. Journal of Geophysical Research. 100(C7).
  38. Greene CA, Thirumalai K, Kearney KA, Delgado JM, Schwanghart W, Wolfenbarger NS, et al. (2019). The Climate Data Toolbox for MATLAB. Geochemistry, Geophysics, Geosystems. 20(7):3774–81.
  39. Siegel DA, Behrenfeld MJ, Maritorena S, McClain CR, Antoine D, Bailey SW, et al. (2013). Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sensing of Environment. 135:77–91.
  40. Cohen P, Cohen P, West SG, Aiken LS. (2014). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Psychology Press.