SpringerLink
Log in

An empirical approach for deriving specific inland water quality parameters from high spatio-spectral resolution image

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

Abstract

Inland lake of Vembanad has benefited from continuous monitoring to evaluate water quality which has declined due to increased anthropogenic activities and climate change. Remote sensing techniques can be used to estimate and monitor inland water quality both spatially and temporally. An empirical model is presented in Vemaband lake that retrieves the specific water quality parameters through correlations between various spectral wavelengths of Sentinel-2MSI (S2MSI) with field-measured water quality parameters. This approach includes the combinations of various bands, band ratios, and band arithmetic computation of satellite sensors of spectral datasets. The specific inland water quality parameters such as chlorophyll-a (chl-a), total suspended solids (TSS), turbidity, and secchi disc depth (SDD) were retrieved from the developed water quality model through Sentinel-2A remote sensing reflectance. The result illustrates that Specific Inland Water Quality Parameters (SIWQP) strongly correlated with S2MSI reflection spectral wavelengths. The SIWQP models are constructed for TSS (R2 = 0.8008), Chl-a (R2 = 0.8055), Turbidity (R2 = 0.6329) and SDD (R2 = 0.7174).The spatial distribution of SIWQPs in Vembanad lake for March 2018 is mapped and shows the lake's water quality distribution. The research from Sentinel-2, MSI has potential and is appropriate in high spectral and spatial characteristics for retrieving and continuous monitoring of water quality parameters in the regional scale of inland water bodies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are not publicly available due to confidentiality of the data, data protection, and privacy, but are available from the corresponding author on reasonable request.

References

  • Babu NK, Omana PK, Mohan M (2010) Water and sediment quality of Ashtamudi estuary, a Ramsar site, southwest coast of India: a statistical appraisal. Environ Monit Assess 165(1):307–319

    Article  CAS  PubMed  Google Scholar 

  • Bolgrien DW, Granin NG, Levin L (1995) Surface temperature dynamics of Lake Baikal observed from AVHRR images. Photogramm Eng Remote Sens 61(2):211–216

    Google Scholar 

  • Brezonik P, Menken KD, Bauer M (2005) Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake Reserv Manag 21:373–382

    Article  Google Scholar 

  • Brivio P, Giardino C, Zilioli E (2001) Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images. Int J Remote Sens 22(2–3):487–502

    Article  Google Scholar 

  • Bustamante J, Pacios F, Díaz-Delgado R, Aragonés D (2009) Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images. J Environ Manag 90(7):2219–2225

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Carpenter D, Carpenter S (1983) Modelling inland water quality using Landsat data. Remote Sens Environ 13:345–352

    Article  Google Scholar 

  • Chacon Torres A, Ross L, Beveridge M, Watson A (1992) The application of SPOT multispectral imagery for the assessment of water quality in Lake Patzcuaro, Mexico. Int J Remote Sens 13(4):587–603

    Article  Google Scholar 

  • Chen J, Zhu W, Tian YQ, Yu Q, Zheng Y, Huang L (2017) Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements. J Appl Remote Sens 11:036007

    Article  Google Scholar 

  • Chen Q, Liu W, Huang C (2022) Long-term 10 m resolution water dynamics of Qinghai lake and the driving factors. Water 14(4):671. https://doi.org/10.3390/w14040671

    Article  Google Scholar 

  • Cheng C, Wei Y, Lv G, Yuan Z (2013) Remote estimation of chlorophyll-a concentration in turbid water using a spectral index: a case study in Taihu Lake, China. J Appl Remote Sens 7(1):1–25

    Article  Google Scholar 

  • Cowardin LM, Carter V, Golet E , LaRoe ET (1979) Classification of wetlands and deepwater habitats of the United States. US Fish and Wildlife Service FWS/OBS 79/31, pp 103

  • Dekker AG, Vos RJ, Peters SWM (2001) Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes. Sci Total Environ 268:197–214

    Article  CAS  PubMed  Google Scholar 

  • Dekker AG, Vos R, Peters S (2002) Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int J Remote Sens 23(1):15–35

    Article  Google Scholar 

  • Dogliotti A, Ruddick K, Nechad B, Doxaran D, Knaeps E (2015) A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sens Environ 156:157–168

    Article  Google Scholar 

  • Dorji P, Fearns P, Broomhall M (2016) A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data. Remote Sens 8:556

    Article  Google Scholar 

  • Dornhofer K, Goritz A, Gege P, Pflug B, Oppelt N (2016) Water constituents and water depth retrieval from sentinel-2A: a first evaluation in an Oligotrophic Lake. Remote Sens 8:941

    Article  Google Scholar 

  • Doxaran D, Froidefond JM, 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. Estuar Coast Shelf Sci 81:321–332

    Article  Google Scholar 

  • Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X (2016) Water bodies map** from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens 8:354

    Article  Google Scholar 

  • Ekercin S (2007) Water quality retrievals from high-resolution IKONOS multispectral imagery: a case study in Istanbul, Turkey. Water Air Soil Pollut 183:239–251

    Article  CAS  Google Scholar 

  • EPA, U.S.E.P.A (1993) Method 180.1—Determination of turbidity by nephelometry (revision 2.0), 10 (Cincinnati)

  • George DC (1997) The airborne remote sensing of phytoplankton chlorophyll in the lakes and tarns of the English district. Int J Remote Sens 18:1961–1975

    Article  Google Scholar 

  • Gholizadeh MH, Melesse AM, Reddi L (2016) A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 16(8):1298. https://doi.org/10.3390/s16081298

    Article  CAS  PubMed Central  Google Scholar 

  • Gitelson A, Garbuzov G, Szilagyi F, Mittenzwey K, Karnieli A, Kaiser A (1993) Quantitative remote sensing methods for real-time monitoring of inland waters quality. Int J Remote Sens 14(7):1269–1295

    Article  Google Scholar 

  • Grendaité D, Stonevicius E (2018) Chlorophyll-a concentration retrieval in eutrophic lakes in Lithuania from Sentinel-2 data. Geol Geogr 4:15–28. https://doi.org/10.6001/geol-geogr.v4i1.3720

    Article  Google Scholar 

  • Griffiths LN, Haupt TN, Zhang MWJ (2021) Role of emergent and submerged vegetation and algal communities on nutrient retention and management in a subtropical urban stormwater treatment wetland. Wetlands Ecol Manag 29:245–264

    Article  CAS  Google Scholar 

  • Haldar R, Khosa R, Gosain AK (2019) Impact of anthropogenic interventions the Vembanad lake system. Water Resour Environ Eng I. https://doi.org/10.1007/978-981-13-2044-6_2

    Article  Google Scholar 

  • Hansen CH, Burian SJ, Dennison PE, Williams GP (2017) Spatiotemporal variability of lake water quality in the context of remote sensing models. Remote Sens 9:409

    Article  Google Scholar 

  • IS: 3025 Part-10 1984 (Reaffirmed 2002)- Methods of Sampling and Test(Physical and chemical ) for water and Waste Water: Turbidity

  • Kallio K, Attila J, Härmä P, Koponen S, Pulliainen J, Hyytiäinen UM, Pyhälahti T (2008) Landsat ETM+ images in the estimation of seasonal lake water quality in boreal river basins. Environ Manag 42:511–522

    Article  Google Scholar 

  • Kim SI, Kim HC, Hyun CU (2014) High-Resolution Ocean Color products estimation in Fjord of Svalbard, Arctic sea using Landsat 8 OLI. Korea J Remote Sens 30(6):809–816

    Article  Google Scholar 

  • Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems. Cambridge University Press, Amsterdam, p 2590

    Book  Google Scholar 

  • Kulkarni A (2010) Water quality retrieval from Landsat TM imagery. Procedia Comput Sci 6:475–480

    Article  Google Scholar 

  • Kutser T (2012) The possibility of using the Landsat image archive for monitoring long time trends in coloured dissolved organic matter concentration in lake waters. Remote Sens Environ 123:334–338

    Article  Google Scholar 

  • Le C, Hu C, Cannizzaro J, English D, Muller Karger F, Lee Z (2013) Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. Remote Sens Environ 129:75–89

    Article  Google Scholar 

  • Li R, Li J (2004) Satellite remote sensing technology for lake water clarity monitoring: an overview. Environ Inf Arch 2:893–901

    Google Scholar 

  • Lim J, Choi M (2015) Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environ Monit Assess 187:1–17

    Article  CAS  Google Scholar 

  • Ma RH, Tang JW, Dai JF (2006) Bio-optical model with optimal parameter suitable for Taihu Lake in watercolor remote sensing. Int J Remote Sens 27:4305–4328

    Article  Google Scholar 

  • Mohan M, Chandran MSS, Jayasooryan KK, Ramasamy EV (2014) Mercury in the sediments of Vembanad Lake, western coast of India. Environ Monit Assess 186:3321–3336. https://doi.org/10.1007/s10661-014-3620-1

    Article  CAS  PubMed  Google Scholar 

  • Mueller JL, Morel A, Frouin R, Davis C, Arnone R, Carder K, Lee ZP, Steward RG, Hooker S, Mobley CD (2003) ocean optics protocols for satellite ocean color sensor validation, Revision 4, radiometric measurements and data analysis protocols; Tech. Memo-21621. Goddard Space Flight Center, Greenbelt

    Google Scholar 

  • Murthy TVR, Patel JG, Panigrahy S, Parihar JS (2013) National Wetland Atlas: wetlands of international importance under ramsar convention; space applications centre. ISRO, Ahmedabad, India. ISBN SAC/EPSA/ABHG/NWIA/ATLAS/38/2013, pp 1-246

  • Murugan P, Sivakumar R, Pandiyan R, Annadurai M (2016) Performance comparison of band ratio and derivative ratio algorithms in chlorophyll-A estimation using hyperspectral data. Int J Earth Sci Eng 09:347–352

    Google Scholar 

  • Myint S, Walker N (2002) Quantification of surface suspended sediments along a river-dominated coast with NOAA AVHRR and SeaWiFS measurements: Louisiana, USA. Int J Remote Sens 23:3229–3249

    Article  Google Scholar 

  • Nair PK, Suresh Babu DS (2016) Spatial shrinkage of Vembanad Lake, South West India during 1973–2015 using NDWI and MNDWI. Int J Sci Res 5(7):1–8

    Google Scholar 

  • Nas B, Ekercin S, Karabörk H, Berktay A, Mulla DJ (2010) An application of Landsat-5TM image data for waterquality map** in Lake Beysehir Turkey. Water Air Soil Pollut 212(1–4):183–197

    Article  CAS  Google Scholar 

  • Nurjaya IW, Surbakati H, Natih NMN (2019) Model of Total Suspended Solid (TSS) distribution due to coastal mining in Western Coast of Kundur Island part of Berhala Strait. IOP Conf Ser Earth Environ Sci 278:1–17

    Article  Google Scholar 

  • Ogashawara I, Mishra DR, Gitelson AA (2017) Remote sensing of inland waters: background and current state-of-the-art. In: Mishra DR, Ogashawara I, Gitelson AA (eds) Bio-optical modeling and remote sensing of inland waters. Elsevier, Amsterdam, pp 1–24

    Google Scholar 

  • Olmanson LG, Bauer ME, Brezonik PL (2008) A 20-year Landsat water clarity census of Minnesota’s 10,000 lakes. Remote Sens Environ 112:4086–4097

    Article  Google Scholar 

  • Palmer SCJ, Kutser T, Hunter PD (2015) Remote sensing of inland waters: challenges, progress, and future directions. Remote Sens Environ 157:1–8

    Article  Google Scholar 

  • Papoutsa C, Retalis A, Toulios L, Hadjimitsis DG (2014) Defining the landsat Tm/Etm+ and Chris/Proba spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy. Int J Remote Sens 35:1674–1692

    Article  Google Scholar 

  • Ramsar (2022) Information Sheet on Ramsar Wetlands (RIS). https://rsis.ramsar.org/ris/1214. Accessed 22 Feb 2022

  • Ranganathan P, Ravikanth G, Aravind NA (2022) A review of research and conservation of Myristica swamps, a threatened freshwater swamp of the Western Ghats, India. Wetlands Ecol Manag 30:171–189. https://doi.org/10.1007/s11273-021-09825-5

    Article  Google Scholar 

  • Ritchie JC, Zimba PV, Everitt JH (2003) Remote sensing techniques to assess water quality. Photogramm Eng Remote Sens 69:695–704

    Article  Google Scholar 

  • Romanelli A, Lima ML, Massone HE, Esquius KS (2014) Spatial decision support system for assessing lake pollution hazard: south eastern Pampean shallow lakes (Argentina) as a case study. Wetlands Ecol Manage 22:247–265

    Article  Google Scholar 

  • Roy PS (1989) Spectral reflectance characteristics of vegetation and their use in estimating productive potential. Proc Indian Acad Sci (plant Sci) 99(1):59–81

    Article  Google Scholar 

  • Sebastia Frasquet MT, Aguilar Maldonado JA, Santamar Del Angel E, Estornell J (2019) Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sens 11(24):2926

    Article  Google Scholar 

  • Sent G, Biguino B, Favareto L, Cruz J, Sá C, Dogliotti AI, Palma C, Brotas V, Brito AC (2021) Deriving water quality parameters using sentinel-2 imagery: a case study in the Sado Estuary, Portugal. Remote Sens 13:1043. https://doi.org/10.3390/rs13051043

    Article  Google Scholar 

  • Sivakumar R, Ghosh S (2016) Spatiotemporal dynamic study of lakes and development of mathematical prediction model using geoinformatics techniques. Arab J Geosci 9(1):1–14

    Article  CAS  Google Scholar 

  • Sivakumar R, Sridhar SS, Ramaraj M, Prasanth SV, B.R. (2018) A comparative analysis of remote sensing and in situ based TSS and turbidity study for a freshwater lake-An integrated approach. J Adv Res Dyn Control Syst 10(8):1185–1190

    Google Scholar 

  • Sruthy S, Ramasamy EV (2016) Microplastic pollution in Vembanad Lake, Kerala, India: the first report of microplastics in lake and estuarine sediments in India. Environ Pollut. https://doi.org/10.1016/j.envpol.2016.12.038

    Article  PubMed  Google Scholar 

  • Theresa Paul T, Grinson G, Dennis A, Athira NR, Biradar RS, Rajani K, Padmakumar KG (2017) Ecosystem responses in the distribution of black clam (Villorita cyprinoides) beds in Vembanad Estuary during environmental changes using GIS and RS. J Geogr Inf Syst 9:245–266

    Google Scholar 

  • Thiemann S, Kaufmann H (2000) Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake District, Germany. Remote Sens Environ 73(2):227–235

    Article  Google Scholar 

  • Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T (2016) First experiences in map** lakewater quality parameters with sentinel-2 MSI imagery. Remote Sens 8:640

    Article  Google Scholar 

  • ‘Vembanad’ (2022) Wikipedia. https://en.wikipedia.org/wiki/Vembanad Accessed: 03 Mar 2022

  • Verpoorter C, KutserT SD, Tranvik LJ (2014) A global inventory of lakes based on high-resolution satellite imagery. Geophys Res Lett 41:639–642

    Article  Google Scholar 

  • Wang H (2022) Regional assessment of ecological risk caused by human activities on wetlands in the Muleng-**ngkai Plain of China using a pressure–capital–vulnerability–response model. Wetlands Ecol Manag 30:111–126. https://doi.org/10.1007/s11273-021-09838-0

    Article  Google Scholar 

  • Wang C, Li W, Chen S, Li D, Wang D, Liu J (2018) The spatial and temporal variation of total suspended solid concentration in Pearl River Estuary during 1987–2015 based on remote sensing. Sci Total Environ 618:1125–1138

    Article  CAS  PubMed  Google Scholar 

  • 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. Int J Remote Sens 36:343–360

    Article  Google Scholar 

  • ** and analyzing China’s wetlands using MODIS time-series data. Wetl Ecol Manag 27:693–710

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This Research work has been made possible by the funding support from the Department of Science and Technology, Government of India in BDA-HSRS(Project Reference Number: BDID/01/23/2014-HSRS/14), Network Project on Imaging Spectroscopy and Applications (NISA), Interdisciplinary Cyber-Physical Systems Division. The authors are thankful to the SRM Institute of Science and Technology for providing all necessary facilities and constant encouragement for doing this research work.

Funding

This research work has been made possible by the partial funding support from Big Data Analytics/ Hyperspectral Remote Sensing (BDID/01/23/2014-HSRS/14), ICPS Division, Department of Science and Technology, Government of India.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, Chengalpattu, 603203, India

    R. Sivakumar, B. R. Sri Vishnu Prasanth & M. Ramaraj

Authors
  1. R. Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. R. Sri Vishnu Prasanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Ramaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors of this manuscript are contributed to this research study. Field water sample collections and other field-based measurements and data collection, analysis were performed by RS, SVP, and RM. RM and SVP performed the Laboratory-based water quality analysis. The satellite image processing was performed by SVP. The statistical analysis, model development, and map** were performed by RM and SVP. The manuscript draft was written by RS, SVP, and RM.

Corresponding author

Correspondence to R. Sivakumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest/ competing interests.

Ethical approval

The research work given in this paper does not involve human participants or animals. Ethical Approval for this paper is not applicable.

Informed consent

The consent to participate in this study was obtained by the authors from the authorities concerned.

Consent for publications

The author grants the publisher the sole and exclusive license of the full copyright in the contribution, which licenses the publisher hereby accepts.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sivakumar, R., Prasanth, B.R.S.V. & Ramaraj, M. An empirical approach for deriving specific inland water quality parameters from high spatio-spectral resolution image. Wetlands Ecol Manage 30, 405–422 (2022). https://doi.org/10.1007/s11273-022-09874-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-022-09874-4

Keywords

Search

Navigation