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Comparative Analysis of Regression Models for Remote Sensing-Based Water Quality Assessment


Affiliations
1 Department of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641 114, Tamil Nadu, India
 

The 80-kilometer-long Vembanad Lake in Kerala, India, is a Ramsar site. Eutrophication is deteriorating its water quality and threatening its biodiversity. In this study, satellite imageries of Sentinel 2A and Landsat 8 OLI were utilized to determine its water quality. Various data sets of the water quality parameters viz. pH, Electrical conductivity, TSS, TDS, BOD, DO, chloride etc. are analyzed and interpreted. Regression models were developed on the parameters taken up for water quality analysis. The empirical R2 values of the developed models evidenced the accuracy of the developed mdoels. The findings show that remote sensing images are reliable for analyzing surface water quality characteristics. The comparative analysis of the model developed illustrated the effectiveness of using the imaging systems mentioned above for water quality index estimation through remote sensing.

Keywords

Landsat 8 OLI, Ramsar Site, Remote Sensing, Sentinel 2 A, Vembanad Lake, Water Quality.
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  • Carpenter S R, Caraco N F, Correll D L, Howarth R W, Sharpley A N & Smith V H, Non-point pollution of surface waters with phosphorus and nitrogen, Ecol Appl, 83 (1998) 559–568.

  • Purandara B K, Environmental issues of wetland ecosystem management: a case study, Ecol Environ Conserv, 14 (2008) 439–448.

  • Purandara B K & Dora Y L, Studies on texture and Organic matter in the sediments of Vembanad Lake and near shore sediments, Proc National Seminar Estuarine Manag (New Delhi) 1997, 449–452.

  • Purandara B K, Jamadar B S, Chandramohan T, Jose M K & Venkatesh B, Water quality assessment of a lentic water body using remote sensing: A case study, Environ Pollut, 2018, 371–380.

  • Ritchie J C & Cooper C M, Remote sensing techniques for determining water quality: applications to TMLDs, TMDL Sci Issues Conf Water Environ Federation (Alexandria, VA) 2001, 367–374

  • Naithani R, Physicochemical assessment of water quality with respect to Remote sensing and GIS techniques, Adv Pure Appl Chem, 1 (2012) 1–6.

  • Akbar T A, Hassan Q & Acharia G, A remote sensing based framework for predicting water quality of different source waters. Satellite based remote sensing approach for acquiring water quality data, Int Arch Photogramm Remote Sens Spat Inf Sci - ISPRS Arch, 34 (2010) 1–4

  • Lathrop R, Landsat thematic mapper monitoring of turbid inland water quality, Photog Eng Remote Sens, 58 (1992) 465–470.

  • Choubey V K, Monitoring water quality in reservoirs with IRS- 1A-LISS-I, Water Resour Manag, 8 (1994) 121–136.

  • Sugumaran R, Clayton M, Ecker M, Stigliani B, Gimond M & Green N, Hyperspectral remote sensing and geographical information systems tools to assess water quality areas and shallow inland lake, University of Northern Iowa and NASA Kennedy Space Center, Iowa Space Grant (Cooperative), Final Technical Report, 2005, 1–35.

  • Sudheer K P, Chaubey I & Garg V, Lake water quality assessment from LANDSAT thematic mapper data using neural network: An approach to optimal band combination selection, J Am Water Resource Assoc, 2006, 1683–1695.

  • Weiss M, Jacob F & Duveiller G, Remote sensing for agricultural applications: A meta-review, Remote Sens Environ, 236 (2020) 111402.

  • Orimoloye I R & Ololade O, Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology, Int J Env Sci Tech, 17 (2020) 4465–4480.

  • Mohandas K A & Brema J, Remote Sensing based water quality analysis of Vembanad lake using Landsat 8 OLI images and inverse distance weighted (IDW) method, Des Eng, 7 (2021) 10295–10310.

  • Kirk J T O, Light and Photosynthesis in Aquatic Ecosystems (Cambridge University Press, Cambridge, United Kingdom) 1983.

  • Liu D, Chin C, Gong J & Fu D, Remote sensing of chlorophyll-a concentrations of the Pearl river estuary from MODIS land bands, Int J Remote Sens, 31 (2010) 4625–4633.

  • Gibson P J & Power C H, Introductory remote sensing—digital image processing and applications (Routledge Pub, London) 2000.

  • Arkin P A & Meisner B N, The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84, Mon Weather Rev, 115 (1987) 51–74.

  • Schmugge T J, Kustas W P, Ritchie J C, Jackson T J & Rango A, Remote sensing in hydrology, Adv Water Resour, 25 (2002) 1367–1385.

  • Shrestha S, & Kazama F, Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan, Environ Model Softw, 22 (2007) 464–475.

  • Akbar A T, Hassan K Q & Achari G, A methodology for clustering lakes in Alberta on the basis of water quality parameters, Clean Soil Air Water, 39(10) (2011) 916–924.

  • Sharaf El Din E, Zhang Y & Suliman A, Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework, Int J Remote Sens, 38(4) (2017) 1023–1042.

  • Mohandas K A & J Brema, Remote sensing based water quality analysis of Vembanad lake, J Green Eng, 11(1) (2021) 1011–1020.

  • Brando V E & Dekker A G, Satellite hyper spectral remote sensing for estimating estuarine and coastal water quality, IEEE Trans Geosci Remote Sens, 41(6) (2003) 1378–1387.

  • Mohandas K A & Brema J, Linear regression based water quality analysis through spatial distribution of parameters for Vembanad lake, Int J Aquatic Sci, 12(3) (2021).

  • Alparslan E, Aydöner C, Tufekci V & Tüfekci H, Water quality assessment at Ömerli dam using remote sensing techniques, Environ Monit Assess, 135 (2007) 391–398.

  • Courault D, Seguin B & Olioso A, Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches, Irrig Drain Syst, 19 (2005) 223–249.

  • Govender M, Chetty K & Bulcock H, A review of hyperspectral remote sensing and its application in vegetation and water resource studies, http://www.wrc.org.za/Pages /KnowledgeHub.aspx, (2013).

  • Sri Dhivya K K & Bhuvaneswari P T V, Multiple linear regression based water quality parameter modeling to detect hexavalent chromium in drinking water, in Int Conf Wirel Commun, Signal Process Netw (WiSPNET) (2017) (Chennai, India) 22–24 March 2017, 2434–2439.

  • Shafique N A, Fulk F, Autrey B C & Flotemersch J, Hyperspectral remote sensing of water quality parameters for large rivers in the Ohio River basin, First Interagency Conf Res Watershed (Benson, AZ) 2003, 216–221.

  • Warren M A, Simis S G, Martinez-Vicente V, Poser K, Bresciani M & Alikas K, Assessment of atmospheric correction algorithms for the Sentinel-2A multi spectral imager over coastal and inland waters, Remote Sens Environ, 225 (2019) 267–289.

  • Toming K, Kutser T, Laas A, Sepp M, Paavel B & Nõges T, First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery, Remote Sens, 8(8) (2016) 640.

  • Elhag M, Gitas I, Othman A, Bahrawi J & Gikas P, Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia, Water, 11(3) (2019) 556.

  • Hassan G, Shaheen M E & Taie S A, Prediction framework for water quality parameters monitoring via remote sensing, in First Int Conf Smart Syst Emerg Technol (IEEE) (Riyadh, Saudi Arabia) 03-05 November 2020, 59–64.

  • López-Puigdollers D, Mateo-García G & Gómez-Chova L, Benchmarking deep learning models for cloud detection in Landsat-8 and Sentinel-2 images, Remote Sens, 13(5) (2021) 992.


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  • Comparative Analysis of Regression Models for Remote Sensing-Based Water Quality Assessment

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Authors

K A Mohandas
Department of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641 114, Tamil Nadu, India
J Brema
Department of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641 114, Tamil Nadu, India

Abstract


The 80-kilometer-long Vembanad Lake in Kerala, India, is a Ramsar site. Eutrophication is deteriorating its water quality and threatening its biodiversity. In this study, satellite imageries of Sentinel 2A and Landsat 8 OLI were utilized to determine its water quality. Various data sets of the water quality parameters viz. pH, Electrical conductivity, TSS, TDS, BOD, DO, chloride etc. are analyzed and interpreted. Regression models were developed on the parameters taken up for water quality analysis. The empirical R2 values of the developed models evidenced the accuracy of the developed mdoels. The findings show that remote sensing images are reliable for analyzing surface water quality characteristics. The comparative analysis of the model developed illustrated the effectiveness of using the imaging systems mentioned above for water quality index estimation through remote sensing.

Keywords


Landsat 8 OLI, Ramsar Site, Remote Sensing, Sentinel 2 A, Vembanad Lake, Water Quality.

References