An Innovative Approach to Delineate and Differentiate Clear and Turbid Water Ponds in Indian Sundarban Area Using Sentinel-2 MSI Data


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Authors

  • N SUDARSAN Indian Institute of Engineering Science and Technology, Shibpur, Howrah - 711 103, West Bengal, India
  • TANUMI KUMAR Regional Remote Sensing Centre - East, NRSC, ISRO, New Town, Kolkata - 700 156, West Bengal, India
  • CHALANTIKA LAHA SALUI Indian Institute of Engineering Science Technology, Shibpur, Howrah - 711 103, West Bengal, India
  • K CHANDRASEKAR National Remote Sensing Centre, ISRO, Hyderabad - 500 037, Telangana, India
  • SOUMYA BANDYOPADHYAY Regional Remote Sensing Centre - East, NRSC, ISRO, New Town, Kolkata - 700 156, West Bengal, India

https://doi.org/10.54894/JISCAR.40.2.2022.125302

Keywords:

Decision tree, Remote sensing, Salinity, Sundarbans, Turbidity index, Water quality

Abstract

Pond water is the main source of irrigation in the Sundarbans area of West Bengal. The serious issues faced in agricultural lands and ponds are due to salinity.  As salinity cannot be detected on high scales through satellite images, in this study the remotely sensed turbidity has been compared and studied with laboratory-measured salinity. The high-resolution Sentinel-2 MSI data with optimum bands for water quality detection have been used in this study. The small agricultural ponds in the study area were not detected by the conventionally used water indices, as the ponds were surrounded by homestead vegetation. Hence, a new index called Pond Probability Spectral Index (PPSI) was formulated and used in the decision tree algorithm to delineate all the ponds. Turbidity indices were used over the extracted ponds to classify them into three classes, clear, turbid and highly turbid. From the field accuracy assessment, one of the turbidity indices outperformed in segregating the ponds with an overall accuracy of 83.3%. It was also found that salinity was inversely proportional to turbidity only when the turbidity value was greater than 100 NTU. The total estimated pond area (average of 3 seasons) in the Sundarban region was 471.66 km2.

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References

Al-Sameraiy, M. (2012). A novel water pre-treatment approach for turbidity removal using date seeds and pollen sheath. Journal of Water Resource and Protection 04(02): 79-92. https://doi.org/10.4236/jwarp.2012.42010.

Asrafuzzaman, Md., Fakhruddin, A.N.M. and Hossain, Md. A. (2011). Reduction of turbidity of water using locally available natural coagulants. ISRN Microbiology 2011: 632189. https://doi.org/10.5402/2011/632189.

Dörnhöfer, K., Göritz, A., Gege, P., Pflug, B. and Oppelt, N. (2016). Water constituents and water depth retrieval from Sentinel-2A - A first evaluation in an oligotrophic lake. Remote Sensing 8(11). https://doi.org/10.3390/rs8110941.

Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W. and Li, X. (2016). Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing 8(4). https://doi.org/10.3390/rs8040354.

Elhag, M., Gitas, I., Othman, A., Bahrawi, J. and Gikas, P. (2019). Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia. Water (Switzerland) 11(3). https://doi.org/10.3390/w11030556.

Ghosh, A. (2017). Sustainability Conflicts in Coastal India: Hazards, Changing Climate and Development Discourses in the Sundarbans, Springer Cham. 245 p. https://doi.org/10.1007/978-3-319-63892-8.

Hendges, E.R., Fallador, F.A.C. and Andres, J. (2018). Correlation study between land use and covering with surface temperature registered by Landsat 8 satellite. Sociedade & Natureza 32: 338-347.

Jawak, S.D., Kulkarni, K. and Luis, A.J. (2015). A review on extraction of lakes from remotely sensed optical satellite data with a special focus on cryospheric lakes. Advances in Remote Sensing 04(03): 196-213. https://doi.org/10.4236/ars.2015.43016.

Krishan, G., Sudarsan, N., Sidhu, B. S. and Vashisth, R. (2021). Impact of lockdown due to COVID-19 pandemic on groundwater salinity in Punjab, India: some hydrogeoethics issues. Sustainable Water Resources Management 7:27. https://doi.org/10.1007/s40899-021-00510-2.

Kumar, T., Mandal, A., Dutta, D., Nagaraja, R. and Dadhwal, V.K. (2017). Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto International 34(4): 415–442. https://doi.org/10.1080/10106049.2017.1408699.

Kutser, T., Paavel, B., Verpoorter, C., Ligi, M., Soomets, T., Toming, K. and Casal, G. (2016). Remote sensing of black lakes and using 810 nm reflectance peak for retrieving water quality parameters of optically complex waters. Remote Sensing 8(6). https://doi.org/10.3390/rs8060497.

Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione, J.A. and Lafaye, M. (2007). Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley fever epidemics in Senegal. Remote Sensing of Environment 106(1): 66-74. https://doi.org/10.1016/j.rse.2006.07.012.

Li, D. and Liu, S. (2019). Sensors in water quality monitoring. In: Water Quality Monitoring and Management, D. Li and S. Liu (eds.), Academic Press, USA. 361 p. https://doi.org/10.1016/b978-0-12-811330-1.00001-6.

Li-Gang, F., Shui-Sen, C., Li, H. L. and Cai-Dong, G. (2008). Monitoring water constituents and salinity variations of saltwater using EO-1 Hyperion satellite imagery in the Pearl river estuary, china. International Geoscience and Remote Sensing Symposium 1(1): 438-441. https://doi.org/10.1109/IGARSS.2008.4778889.

Martins, V.S., Barbosa, C.C.F., de Carvalho, L.A.S., Jorge, D.S.F., Lobo, F. de L. and de Moraes Novo, E.M.L. (2017). Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sensing 9(4). https://doi.org/10.3390/rs9040322.

Mukherjee, S. (2014). Studies on removal of organic and inorganic load from wastewater using coagulation-flocculation and advanced techniques. Unpublished Ph.D. Thesis, Maulana Abdul Kalam Azad University of Technology, West Bengal, India.

Müller, D.N., Wilck, N., Haase, S., Kleinewietfeld, M. and Linker, R.A. (2019). Sodium in the microenvironment regulates immune responses and tissue homeostasis. Nature Reviews Immunology 19(4): 243-254. https://doi.org/10.1038/s41577-018-0113-4.

Palmer, S.C.J., Kutser, T. and Hunter, P.D. (2015). Remote sensing of inland waters: Challenges, progress and future directions. Remote Sensing of Environment 157: 1-8. https://doi.org/10.1016/j.rse.2014.09.021.

Petus, C., Chust, G., Gohin, F., Doxaran, D., Froidefond, J.-M. and 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. and Huan, N.H. (2017). Spatiotemporal variation of turbidity based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water (Switzerland) 9(8). https://doi.org/10.3390/w9080570.

Rana, H. and Neeru, N. (2017). Hybrid technique for detection of water using satellite images. International Journal of Advanced Research in Computer Science 8(7): 659-666. https://doi.org/10.26483/ijarcs.v8i7.4361.

Rusydi, A.F. (2018). Correlation between conductivity and total dissolved solid in various type of water: A review. IOP Conference Series: Earth and Environmental Science 118: 012019. https://doi.org/10.1088/1755-1315/118/1/012019.

Sarp, G. and Ozcelik, M. (2017). Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science 11(3): 381-391. https://doi.org/10.1016/j.jtusci.2016.04.005.

Shah, D. and Zaveri, T. (2021). Hyperspectral endmember extraction using Pearson’s correlation coefficient. International Journal of Computational Science and Engineering 24(1): 89-97. https://doi.org/10.1504/IJCSE.2021.113656.

Somvanshi, S., Kunwar, P., Singh, N.B., Shukla, S.P. and Pathak, V. (2012). Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh. International Journal of Environmental Sciences 3(1): 62-75. https://doi.org/10.6088/ijes.2012030131008.

Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B. and Nõges, T. (2016). First experiences in mapping lakewater quality parameters with Sentinel-2 MSI imagery. Remote Sensing 8(8): 1-14. https://doi.org/10.3390/rs8080640.

Zhang, C., Kovacs, J.M., Liu, Y., Flores-Verdugo, F. and Flores-de-Santiago, F. (2014). Separating mangrove species and conditions using laboratory hyperspectral data: A case study of a degraded mangrove forest of the Mexican Pacific. Remote Sensing 6(12): 11673-11688. https://doi.org/10.3390/rs61211673.

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Submitted

2022-06-30

Published

2023-04-20

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How to Cite

SUDARSAN, N., KUMAR, T., SALUI, C. L., CHANDRASEKAR, K., & BANDYOPADHYAY, S. (2023). An Innovative Approach to Delineate and Differentiate Clear and Turbid Water Ponds in Indian Sundarban Area Using Sentinel-2 MSI Data. Journal of the Indian Society of Coastal Agricultural Research, 40(2), 14-32. https://doi.org/10.54894/JISCAR.40.2.2022.125302
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