Estimation of coastal waters turbidity using Sentinel-2 imagery

    Muhammad Anshar Amran   Affiliation
    ; Wasir Samad Daming Affiliation


Turbidity is an important water quality parameter and an indicator of water pollution. Marine remote sensing techniques has become a useful tool for mapping of turbidity at coastal waters. The advantage of using remote sensing for water quality analysis is its ability to obtain synoptic data from the entire study area to produce continuous surface data, can shows detailed spatial variability and periodically. The empirical modeling has been applied in this study to formulate the mathematical relationship between coastal waters turbidity with Sentinel-2 reflectance. This study integrated field survey and image processing. Measurement of in-situ turbidity was done in accordance with imagery acquisition time. Imageries used for this study were Sentinel-2 level-2A. The mathematical relationship was obtained by multiple linear regression model between turbidity and Sentinel-2 reflectance. A mathematical model has been developed in Sentinel-2 imagery and successfully applied to obtain surface turbidity. Estimated turbidity derived from Sentinel-2 imagery is very close to observed turbidity so the proposed model can be used to retrieve turbidity of coastal waters. 

Keyword : coastal waters, turbidity, Sentinel-2, reflectance, empirical modeling, multiple linear regression

How to Cite
Amran, M. A., & Daming, W. S. (2023). Estimation of coastal waters turbidity using Sentinel-2 imagery. Geodesy and Cartography, 49(4), 180–185.
Published in Issue
Dec 19, 2023
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Boyd, C. E. (2000). Water quality: An introduction. Springer Science+Business Media.

Brezonik, P., Menken, K. D., & Bauer, M. (2005). Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake and Reservoir Management, 21(4), 373–382.

Brezonik, P. L., Olmanson, L. G., Bauer, M. E., & Kloiber, S. M. (2007). Measuring water clarity and quality in Minnesota lakes and rivers: A census-based approach using remote-sensing techniques. Cura Reporter, 37, 3–13.

Caballero, I., Stumpf, R. P., & Meredith, A. (2019). Preliminary assessment of turbidity and chlorophyll impact on bathymetry derived from Sentinel-2A and Sentinel-3A satellites in South Florida. Remote Sensing, 11(6), 645.

Dogliotti, A. I., Ruddick, K. G., Nechad, B., Doxaran, D., & Knaeps, E. (2015). A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment, 156, 157–168.

Doxaran, D., Froidefond, J. M., Castaing, P., & Babin, M. (2009). Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data. Estuarine, Coastal and Shelf Science, 81(3), 321–332.

Gernez, P., Barille, L., Lerouxel, A., Mazeran, C., Lucas, A., & Doxaran, D. (2014). Remote sensing of suspended particulate matter in turbid oyster-farming ecosystems. Journal of Geophysical Research: Oceans, 119(10), 7277–7294.

Güttler, F. N., Niculescu, S., & Gohin, F. (2013). Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western–northwestern Black Sea coastal zone. Remote Sensing of Environment, 132(15), 86–101.

Hu, Z., Pan, D., He, X., & Bai, Y. (2016). Diurnal variability of turbidity fronts observed by geostationary satellite ocean color remote sensing. Remote Sensing, 8(2), 147–162.

Islam, M. A., Lan-Wei, W., Smith, C. J., Reddy, S., Lewis, A., & Smith, A. (2007). Evaluation of satellite remote sensing for operational monitoring of sediment plumes produced by dredging at hay point, Queensland, Australia. Journal of Applied Remote Sensing, 1(1), 011506–011521.

Katlane, R., Dupouy, C., El Kilani, B., & Berges, J. C. (2020). Estimation of chlorophyll and turbidity using Sentinel 2A and EO1 data in Kneiss Archipelago Gulf of Gabes, Tunisia. International Journal of Geosciences, 11, 708–728.

Lihan, T., Saitoh, S. I., Iida, T., Hirawake, T., & Iida, K. (2008). Satellite-measured temporal and spatial variability of the Tokachi River plume. Estuarine Coastal and Shelf Science, 78(2), 237–249.

Liu, Y., Islam, M. A., & Gao, J. (2003). Quantification of shallow water quality parameters by means of remote sensing. Progress in Physical Geography, 27(1), 24–43.

Miller, P. I., Xu, W., & Carruthers, M. (2015). Seasonal shelf-sea front mapping using satellite ocean colour and temperature to support development of a marine protected area network. Deep-Sea Researh Part II-Topical Study in Oceanography, 119, 3–19.

Obregón, M. A., Rodrigues, G., Costa, M. J., Potes, M., & Silva, A. M. (2019). Validation of ESA Sentinel-2 L2A aerosol optical thickness and columnar water vapour during 2017–2018. Remote Sensing, 11(14), 1649.

Ouillon, S., Douillet, P., & Andrefouet, S. (2004). Coupling satellite data with in situ measurements and numerical modeling to study fine suspended-sediment transport: A study for the lagoon of New Caledonia. Coral Reefs, 23(1), 109–122.

Ouillon, S., Douillet, P., Petrenko, A., Neveux, J., Dupouy, C., Froidefond, J. M., Andréfouët, S., & Caravaca, A. M. (2008). Optical algorithms at satellite wavelengths for total suspended matter in tropical coastal waters. Sensors, 8(7), 4165–4185.

Ouma, Y. O., Noor, K., & Herbert, K. (2020). Modelling reservoir chlorophyll-a, TSS, and turbidity using Sentinel-2A MSI and Landsat-8 OLI satellite sensors with empirical multivariate regression. Journal of Sensors, 2020, 8858408.

Petus, C., Chust, G., Gohin, F., Doxaran, D., Froidefond, J. M., & Sagarminaga, Y. (2010). Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery. Continental Shelf Research, 30(5), 379–392.

Quang, N. H., Sasaki, J., Higa, H., & Huan, N. H. (2017). Spatiotemporal variation of turbidity based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water, 9(8), 570–594.

Ray, R., Mandal, S., & Dhara, A. (2013). Environmental monitoring of estuaries: Estimating and mapping various environmental indicators in Matla estuarine complex, using Landsat TM digital data. International Journal of Geomatics and Geosciences, 3(3), 570–581.

Sebastiá-Frasquet, M. T., Aguilar-Maldonado, J. A., Santamaría-Del-Ángel, E., & Estornell, J. (2019). Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sensing, 11(24), 2926.

Sravanthi, N., Ramana, I. V., Yunus Ali, P., Ashraf, M., Ali, M. M., & Narayana, A. C. (2013). An algorithm for estimating suspended sediment concentrations in the coastal waters of India using remotely sensed reflectance and its application to coastal environments. International Journal of Environmental Research, 7(4), 841–850.

Vanhellemont, Q., & Ruddick, K. (2014). Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sensing of Environment, 145, 105–115.

Wu, G., Cui, L., Liu, L., Chen, F., Fei, T., & Liu, Y. (2015). Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China. International Journal of Remote Sensing, 36(1), 343–360.

Zhang, Y., Zhang, Y., Shi, K., Zha, Y., Zhou, Y., & Liu, M. (2016). A Landsat 8 OLI-based, semianalytical model for estimating the total suspended matter concentration in the slightly turbid Xin’anjiang reservoir (China). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 1–16.