Monitoring of chlorophyll-A, organic carbon, salinity and water surface temperature off the coast of Sistan and Baluchestan using remote sensing data

Subject Areas : Natural resources and environmental management

Elham Shahri ¹ , Mohammad Hossein Sayadi ² , Elham Yousefi ³

1 - PhD. Student of Environmental Science and Engineering, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran
2 - Associate Professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran
3 - Assistant Professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran

Received: 2021-07-09 Accepted : 2021-08-24 Published : 2021-12-22

Keywords: Oman Sea, satellite imagery, Google Earth Engine (GEE), MODIS,

Abstract :

Background and Objective The seas and oceans play an important role in climate conditions as well as climate change. In addition, physical and biological phenomena are among the most important factors affecting the chemistry and environment of the sea. Therefore, it is important to know the physical processes that govern the seas and oceans, as well as the correlation between these properties and biological properties. Remote sensing algorithms use a close range of blue, green, yellow, red, and infrared, so monitoring of chlorophyll-A, the phytoplankton pigment of oceanic and coastal waters, can be measured and evaluated using state-of-the-art remote sensing technology.Materials and Methods In this study, the capability of remote sensing methods has been used to investigate the status of coastal water quality characteristics of Sistan and Baluchestan provinces. For this purpose, the status of chlorophyll-A has been used using OC3 bio-optical algorithms in ENVI as well as the predecessors of the Google Earth Engine platform. Google Earth Engine is an open-source spatial analysis platform that enables users to visualize and analyze planetary satellite images. Using this system, various spectral processes can be performed on different surface phenomena with different satellite data. It is also possible to perform calculations on large volumes of data without the need for high-power systems. The salinity parameter of MIRAS's SMOS satellite was used in SNAP software to investigate the parameters of chlorophyll, temperature, and organic carbon using Terra's MODIS satellite images. The time to be studied in the images used and field sampling is May 2020. In order to extract the concentration of chlorophyll-A, bio-optical algorithms based on blue and green bands (OC3) were used in ENVI software. Bio-optical models combine optical measurements of reflection or radiation with biological parameters such as chlorophyll concentration, water quality, and more. Water temperature is one of the most important factors in the life of the sea, so those marine animals can survive and reproduce only in a certain range of water temperatures. Therefore, phytoplankton is very sensitive to changes in water temperature and react to temperature. Water level can determine their frequency and distribution. In this study, the product MIR_OSUDP2 of the SMOS satellite of MIRAS on 3rd of May 2020, for the study area from  https://smos-diss.eo.esa.int/ was used.Results and Discussion The results showed that the amount of chlorophyll-A is higher along the shores and the stations near Joud and the estuary has a higher concentration of chlorophyll-A. The results showed the outputs of two different methods for estimating chlorophyll-A in the study area are similar. Also, according to the results, it is clear that the amount of chlorophyll-A has increased in Chabahar, Konarak, Jude, and Goater stations in recent years. In Chabahar and Konarak regions, this increase has been significant for ten years, and the sudden increase in chlorophyll in recent years in field stations requires more studies to identify the causes and should be considered. The chart below shows the rate of change in chlorophyll-A from 2019 to 2020. According to the results, the amount of organic carbon follows the amount of chlorophyll-A and in areas such as Chabahar and Konarak we see higher levels of organic carbon. Also, the highest increase in temperature in all three periods studied was in Chabahar and Konarak ports, of which human activities are one of the main factors. Also, by examining the ten-year trend, increasing temperature changes can be seen in the ports of Maidan and Jude. The general trend of temperature is decreasing to the east as expected because it is closer to open waters. Seasons when water temperatures are lower, chlorophyll-A levels are higher. Chlorophyll-A map output results by ENVI software and Google Earth Engine platform, chlorophyll-A concentrations were higher in autumn and winter than in spring and summer, high chlorophyll-A-concentrations are common in cold tropical and subtropical seasons. Also, the concentration of chlorophyll-A in the study areas along the coast is higher than the offshore areas, which is related to the chlorophyll-A harvesting algorithm in type 1 waters; In other words, coastal areas have more value than offshore areas due to shallow depth, high turbidity and suspended sediments. Because there is no river discharge in this area, these areas are mostly affected by hydrodynamic processes such as wind direction and sea currents. The lowest chlorophyll-A concentrations were observed in the region from May to September, which was contrary to fluctuations in water surface temperature, which could be due to rising currents. The amount of organic carbon is one of the most important factors for evaluating the performance of aquatic ecosystems, which determines the potential of ecosystems for fishery products; The results of the study of organic carbon showed that the amount of organic carbon as chlorophyll-A in the two seasons of autumn and winter was higher than spring and summer so that the trend of changes in organic carbon also followed the trend of changes in chlorophyll-A. There is a correlation between temperature fluctuations and chlorophyll-A, this correlation indicates the importance of water surface temperature in changes in the growth rate of phytoplankton as one of the climatic factors and has made the most important parameter affecting chlorophyll-A, water surface temperature. According to the obtained results, the trend of temperature changes in the last ten years is increasing and the hottest stations are Chabahar and Konarak stations. In terms of salinity, areas with lower salinity had higher chlorophyll-A levels. Comparison of the data obtained from this study with the above indicates that the range of recorded fluctuations of the quality parameters studied in the natural waters of the region and is consistent with similar studies in the study area by other experts.Conclusion The results of this study show the acceptable accuracy of the results compared to the data of similar researchers in addition to the speed and ease of the method. Therefore, with the help of remote sensing science, timely monitoring of the quality parameters of water areas can prevent major crises and save time and money, problems that may be irreversible if they occur.

References:

Acheampong C. 2018. Deriving algal concentration from Sentinel-2 through a downscaling technique: A case near the intake of a desalination plant. Journal of Geophysical Research 103: 24937-24953. doi:https://doi.org/10.1029/98JC02160.

Bouman HA, Jackson T, Sathyendranath S, Platt T. 2020. Vertical structure in chlorophyll profiles: influence on primary production in the Arctic Ocean. Philosophical Transactions of the Royal Society A, 378(2181): 20190351. doi:https://doi.org/10.1098/rsta.2019.0351.

Cadée GC, Hegeman J. 1991. Phytoplankton primary production, chlorophyll and species composition, organic carbon and turbidity in the Marsdiep in 1990, compared with foregoing years. Hydrobiological Bulletin, 25(1): 29-35. doi:https://doi.org/10.1007/BF02259586.

Cui T, Zhang J, Wang K, Wei J, Mu B, Ma Y, Zhu J, Liu R, Chen X. 2020. Remote sensing of chlorophyll a concentration in turbid coastal waters based on a global optical water classification system. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 187-201. doi:https://doi.org/10.1016/j.isprsjprs.2020.02.017.

Deng Y, Zhang Y, Li D, Shi K, Zhang Y. 2017. Temporal and spatial dynamics of phytoplankton primary production in Lake Taihu derived from MODIS data. Remote Sensing, 9(3): 195. doi:https://doi.org/10.3390/rs9030195.

Gholamalifad M, Ahmadi B, Nouri P. 2020. Remote Sensing Monitoring of Sea Surface Temperature and Chlorophyll-a Variability in the Persian Gulf and Oman Sea: Influential Factors on Net Primary Production. Fisheries Science and Technology, 9(4): 305-333. http://jfst.modares.ac.ir/article-306-49533-en.html. (In Persian).

Gregg WW, Casey NW, McClain CR. 2005. Recent trends in global ocean chlorophyll. Geophysical Research Letters, 32(3). doi:https://doi.org/10.1029/2004GL021808.

Haghparast M, Mokhtarzade M. 2018. Estimation of turbidity and chlorophyll a concentration in the Caspian Sea through time series analysis of satellite images and wavelet neural networks. Iranian Journal of Remote Sensing & GIS, 10(1): 91-108. (In Persian).

Hernandez O, Jouanno J, Echevin V, Aumont O. 2017. Modification of sea surface temperature by chlorophyll concentration in the Atlantic upwelling systems. Journal of Geophysical Research: Oceans, 122(7): 5367-5389. doi:https://doi.org/10.1002/2016JC012330.

Hu M, Zhang Y, Ma R, Xue K, Cao Z, Chu Q, Jing Y. 2021. Optimized remote sensing estimation of the lake algal biomass by considering the vertically heterogeneous chlorophyll distribution: Study case in Lake Chaohu of China. Science of The Total Environment, 771: 144811. doi:https://doi.org/10.1016/j.scitotenv.2020.144811.

Huang Y, Jiang D, Zhuang D, Fu J. 2010. Evaluation of hyperspectral indices for chlorophyll-a concentration estimation in Tangxun Lake (Wuhan, China). International journal of environmental research and public health, 7(6): 2437-2451. doi:https://doi.org/10.3390/ijerph7062437.

Irwin AJ, Finkel ZV. 2008. Mining a sea of data: Deducing the environmental controls of ocean chlorophyll. PloS one, 3(11): e3836. doi:https://doi.org/10.1371/journal.pone.0003836.

Kavak MT. 2012. Long term investigation of SST regime variability and its relationship with phytoplankton in the Caspian Sea using remotely sensed AVHRR and SeaWiFS data. Turkish Journal of Fisheries and Aquatic Sciences, 12(3). doi:https://doi.org/10.4194/1303-2712-v12_3_20.

Kessouri F, Ulses C, Estournel C, Marsaleix P, d'Ortenzio F, Severin T, Taillandier V, Conan P. 2018. Vertical mixing effects on phytoplankton dynamics and organic carbon export in the western Mediterranean Sea. Journal of Geophysical Research: Oceans, 123(3): 1647-1669. doi:https://doi.org/10.1002/2016JC012669.

Khebri Z, Nejadkoorki F, Sodaie Zadeh H. 2015. The relationship between land use vector parameters and river water quality using GIS (Case study: Zayandehrood river). Journal of RS and GIS for Natural Resources, 6(1): 79-89. http://girs.iaubushehr.ac.ir/article_516775.html?lang=en. (In Persian).

Mahdavifard M, Valizadeh Kamran K, Atazadeh E. 2020. Estimation of chlorophyll-a concentration using ground data and Sentinel-2 and Landsat-8 Satellite images processing (Case study: Tiab Estuary). Journal of RS and GIS for Natural Resources, 11(1): 72-83. http://girs.iaubushehr.ac.ir/article_672377.html?lang=en. (In Persian).

Martin S. 2014. An introduction to ocean remote sensing. Cambridge University Press, illustrated, revised, 496 p.

Mascarenhas V, Keck T. 2018. Marine optics and ocean color remote sensing. In: YOUMARES 8–Oceans Across Boundaries: Learning from each other, Proceedings of the 2017 conference for YOUng MARine RESearchers in Kiel, Germany. p 41.

Mir Alizadehfard SR, Mansouri S. 2019. Evaluation of indicators of remote sensing measurement in quantitative and qualitative studies of surface water with Landsat-8 satellite images (Case study: South of Khuzestan province). Journal of RS and GIS for Natural Resources, 10(2): 63-84. http://girs.iaubushehr.ac.ir/article_666799_en.html. (In Persian).

Moghadam NK, Motesharezadeh B, Maali-Amiri R, Lajayer BA, Astatkie T. 2020. Effects of potassium and zinc on physiology and chlorophyll fluorescence of two cultivars of canola grown under salinity stress. Arabian Journal of Geosciences, 13(16): 1-8. doi:https://doi.org/10.1007/s12517-020-05776-y.

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. doi:https://doi.org/10.1016/j.marpolbul.2015.07.018.

Nezlin NP, Polikarpov IG, Al-Yamani FY, Rao DS, 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. doi:https://doi.org/10.1016/j.jmarsys.2010.03.003.

Papenfus M, Schaeffer B, Pollard AI, Loftin K. 2020. Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs. Environmental Monitoring and Assessment, 192(12): 1-22. doi:https://doi.org/10.1007/s10661-020-08631-5.

Poddar S, Chacko N, Swain D. 2019. Estimation of Chlorophyll-a in northern coastal Bay of Bengal using Landsat-8 OLI and Sentinel-2 MSI sensors. Frontiers in Marine Science, 6: 598. doi:https://doi.org/10.3389/fmars.2019.00598.

Reilly JE, Maritorena S, Siegel DA, O’Brien MC, Toole D, Mitchell BG, Kahru M, Chavez FP, Strutton P, Cota GF. 2000. Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. SeaWiFS postlaunch calibration and validation analyses, Part, 3: 9-23.

Reynolds RM. 1993. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman—Results from the Mt Mitchell expedition. Marine Pollution Bulletin, 27: 35-59. doi:https://doi.org/10.1016/0025-326X(93)90007-7.

Simpson JH, Sharples J. 2012. Introduction to the physical and biological oceanography of shelf seas. Cambridge University Press, 345 p.

Tepanosayn G, Muradyan V, Hovsepyan A, Minasyan L, Asmaryan S. 2017. A Landsat 8 OLI Satellite Data-Based Assessment of Spatio-Temporal Variations of Lake Sevan Phytoplankton Biomass. Ann Valahia Univ Targoviste Geogr Ser, 17(1): 83-89. doi:https://doi.org/10.1515/avutgs-2017-0008.

Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T. 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8(8): 640. doi:https://doi.org/10.3390/rs8080640.

Vinh PQ, Ha NTT, Binh NT, Thang NN, Oanh L, Thao N. 2019. Developing algorithm for estimating chlorophyll-a concentration in the Thac Ba Reservoir surface water using Landsat 8 Imagery. VIETNAM Journal of Earth Sciences, 41(1): 10-20. doi:https://doi.org/10.15625/0866-7187/41/1/13542.

Watanabe F, Alcantara E, Rodrigues T, Rotta L, Bernardo N, Imai N. 2017. Remote sensing of the chlorophyll-a based on OLI/Landsat-8 and MSI/Sentinel-2A (Barra Bonita reservoir, Brazil). Anais da Academia Brasileira de Ciências, 90: 1987-2000. doi:https://doi.org/10.1590/0001-3765201720170125.