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A critical and intensive review on assessment of water quality parameters through geospatial techniques

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Abstract

Evaluation of water quality is a priority work nowadays. In order to monitor and map, the water quality for a wide range on different scales (spatial, temporal), the geospatial technique has the potential to minimize the field and laboratory work. The review has emphasized the advance of remote sensing for the effectiveness of spectral analysis, bio-optical estimation, empirical method, and application of machine learning for water quality assessment. The water quality parameters (turbidity, suspended particles, chlorophyll, etc.) and their retrieval techniques are described in a scientific manner. Available satellite, bands, resolution, and spectrum ranges for specific parameters are critically described in this review with challenges in remote sensing for water quality analysis, considering non-optical active parameters. The application of statistical programmes like linear (multiple regression analysis) and non-linear approaches is discussed for better assessment of water quality. Emphasis is given on comparison between different models to increase the accuracy level of remote sensing of water quality assessment. A direction is suggested for future development in the field of estimation of water pollution assessment through geospatial techniques.

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References

  • Abayazid HO, El-Adawy A (2019) Assessment of a non-optical water quality property using space-based imagery in Egyptian coastal lake. Journal of Water Resource and Protection 11:713–727. https://doi.org/10.4236/jwarp.2019.116042

    Article  CAS  Google Scholar 

  • Ahn YH, Shanmugam P, Moon JE, Ryu JH (2008) Satellite remote sensing of a low-salinity water plume in the East China Sea. InAnnalesGeophysicae. Copernicus GmbH 26:2019–2035. https://doi.org/10.5194/angeo-26-2019-2008

    Article  Google Scholar 

  • Aiken GR (1985) Humic substances in soil, sediment and water. Geochemistry, isolation and characterization 21:213–214. https://doi.org/10.1002/gj.3350210213

    Article  Google Scholar 

  • Akbar TA, Hassan Q, Achari G (2010) A remote sensing based framework for predicting water quality of different source waters. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34:1–4

    Google Scholar 

  • Allan MG, Hicks BJ, Brabyn L (2007) Remote sensing of water quality in the Rotorua lakes. https://hdl.handle.net/10289/3785

  • Alparslan E, Aydöner C, Tufekci V, Tüfekci H (2007) Water quality assessment at Ömerli Dam using remote sensing techniques. Environ Monit Assess 135(1):391–398. https://doi.org/10.1007/s10661-007-9658-6

    Article  CAS  Google Scholar 

  • Álvarez-Robles JA, Zarazaga-Soria FJ, Latre MA, Béjar R, Muro-Medrano PR (2006) Water quality monitoring based on sediment distribution using satellite imagery. In Proceedings of the 9th AGILE Conference on Geographic Information Science, Visegrad, Hungary (pp. 20-22).approach. Remote Sensing of Environment 240, 111604. 10

  • Araujo GS, Abessa DM, Soares AM, Loureiro S (2019) Multi-generational exposure to Pb in two monophyletic Daphnia species: individual, functional and population related endpoints. Ecotoxicol Environ Saf 173:77–85. https://doi.org/10.1016/j.ecoenv.2019.02.001

    Article  CAS  Google Scholar 

  • Baban SM (1993) Detecting water quality parameters in the Norfolk Broads, UK, using Landsat imagery. Int J Remote Sens 14(7):1247–1267. https://doi.org/10.1080/01431169308953955

    Article  Google Scholar 

  • Becker RH, Sultan MI, Boyer GL, Twiss MR, Konopko E (2009) Map** cyanobacterial blooms in the Great Lakes using MODIS. J Great Lakes Res 35:447–453

    Article  CAS  Google Scholar 

  • Bhatti AM, Rundquist DC, Nasu S, Takagi M (2008) Assessing the potential of remotely sensed data for water quality monitoring of coastal and inland waters

  • Bonansea M, Rodriguez MC, Pinotti L, Ferrero S (2015) Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sens Environ 158:28–41

    Article  Google Scholar 

  • Boucher J, Weathers KC, Norouzi H, Steele B (2018) Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring. Ecol Appl 28(4):1044–1054. https://doi.org/10.1002/eap.1708

    Article  Google Scholar 

  • Braga F, Giardino C, Bassani C, Matta E, Candiani G, Strömbeck N, Bresciani M (2013) Assessing water quality in the northern Adriatic Sea from HICO™ data. Remote sensing letters 4(10):1028–1037. https://doi.org/10.1080/2150704X.2013.830203

    Article  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 and Reservoir Management 21(4):373–382

    Article  Google Scholar 

  • Brezonik PL, Olmanson LG, Bauer ME, Kloiber SM (2007) Measuring water clarity and quality in minnesota lakes and rivers: A census-based approach using remote-sensing techniques. Cura Rep 37:3–313

  • Buiteveld H, Hakvoort JH, Donze M (1994) Optical properties of pure water. In Ocean Optics XII (Vol. 2258, pp. 174-183). International Society for Optics and Photonics. https://doi.org/10.1117/12.190060

  • Carmichael WW, Boyer GL (2016) Health impacts from cyanobacteria harmful algae blooms: Implications for the North American Great Lakes. Harmful Algae 54:194–212. https://doi.org/10.1016/j.hal.2016.02.002

    Article  Google Scholar 

  • Chang NB, Imen S, Vannah B (2015) Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit Rev Environ Sci Technol 45:101–166. https://doi.org/10.1080/10643389.2013.829981

    Article  CAS  Google Scholar 

  • Chapman D(1996) Water quality assessments - A Guide To Use Of Biota, Sediments And Water In Environmental Monitoring. E&FN Spon, an imprint of Chapman & Hall

  • Chen L (2003) A study of applying genetic programming to reservoir trophic state evaluation using remote sensor data. Int J Remote Sens 24:2265–2275. https://doi.org/10.1080/01431160210154966

    Article  Google Scholar 

  • Chen Y, Fan C, Teubner K, Dokulil M (2003) Changes of nutrients and phytoplankton chlorophyll-a in a large shallow lake, Taihu, China: an 8-year investigation. Hydrobiologia 506(1-3):273–279. https://doi.org/10.1023/B:HYDR.0000008604.09751.01

    Article  Google Scholar 

  • Chen L, Tan CH, Kao SJ, Wang TS (2008) Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery. Water Res 42:296–306. https://doi.org/10.1016/j.watres.2007.07.014

    Article  CAS  Google Scholar 

  • Chipman JW, Olmanson LG, Gitelson AA (2009) Remote sensing methods for lake management: a guide for resource managers and decision-makers. North American Lake Management Society

  • Chislock MF, Doster E, Zitomer RA, Wilson AE (2013) Eutrophication: causes, consequences, and controls in aquatic ecosystems. Nature Education Knowledge 4(4):10 https://www.researchgate.net/publication/285683019

    Google Scholar 

  • Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger JC, Skakun SV, Justice C (2018) The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens Environ 219:145–161. https://doi.org/10.1016/j.rse.2018.09.002

    Article  Google Scholar 

  • Craig SE, Jones CT, Li WK, Lazin G, Horne E, Caverhill C, Cullen JJ (2012) Deriving optical metrics of coastal phytoplankton biomass from ocean colour. Remote Sens Environ 119:72–83. https://doi.org/10.1016/j.rse.2011.12.007

    Article  Google Scholar 

  • Crittenden JC, Hand DW, Howe KJ, Rhodes Trussell R, Tchobanoglous G(2012) Water treatment principles and design, thirded. John Wiley & Sons

  • D'Alimonte D, Zibordi G, Berthon JF (2004) Determination of CDOM and NPPM absorption coefficient spectra from coastal water remote sensing reflectance. IEEE Trans Geosci Remote Sens 42(8):1770–1777. https://doi.org/10.1109/TGRS.2004.831444

    Article  Google Scholar 

  • Davis TW, Berry DL, Boyer GL, Gobler CJ (2009) The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8:715–725. https://doi.org/10.1016/j.hal.2009.02.004

    Article  CAS  Google Scholar 

  • Dekker AG, Peters SW (1993) The use of the thematic mapper for the analysis of eutrophic lakes: a case study in the Netherlands. Int J Remote Sens 14(5):799–821. https://doi.org/10.1080/01431169308904379

    Article  Google Scholar 

  • Dekker AG, Zamurovic-Nenad Z, Hoogenboom HJ, Peters SWM (1996) Remote sensing, ecological water quality modelling and in-situ measurements: a case study in shallow lakes. Hydrol Sci J 41(4):531–547. https://doi.org/10.1080/02626669609491524

  • Dierberg FE, Carriker NE (1994) Field testing two instruments for remotely sensing water quality in the Tennessee Valley. Environ Sci Technol 28:16–25. https://doi.org/10.1021/es00050a004

    Article  CAS  Google Scholar 

  • Dörnhöfer K, Oppelt N (2016) Remote sensing for lake research and monitoring–Recent advances. Ecol Indic 64:105–122. https://doi.org/10.1016/j.ecolind.2015.12.009

    Article  CAS  Google Scholar 

  • D'Sa EJ (2008) Colored dissolved organic matter in coastal waters influenced by the Atchafalaya River, USA: effects of an algal bloom. J Appl Remote Sens 2(1):023502. https://doi.org/10.1117/1.2838253

    Article  Google Scholar 

  • D'Sa EJ, Miller RL (2003) Bio-optical properties in waters influenced by the Mississippi River during low flow conditions. Remote Sens Environ 84(4):538–549. https://doi.org/10.1016/S0034-4257(02)00163-3

    Article  Google Scholar 

  • Elhag M, Gitas I, Othman A, Bahrawi J, Gikas P (2019) Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia. Water 11(3):556. https://doi.org/10.3390/w11030556

    Article  CAS  Google Scholar 

  • Fiorani L, Fantoni R, Lazzara L, Nardello I, Okladnikov I, Palucci A (2006) Lidar calibration of satellite sensed CDOM in the southern ocean. EARSeLeProc 5(1):89–99

    Google Scholar 

  • Flink P, Lindell LT, Östlund C (2001) Statistical analysis of hyperspectral data from two Swedish lakes. Sci Total Environ 68:155–169. https://doi.org/10.1016/S0048-9697(00)00686-0

    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  Google Scholar 

  • Giardino C, Brando VE, Dekker AG, Strömbeck N, Candiani G (2007) Assessment of water quality in Lake Garda (Italy) using Hyperion. Remote Sens Environ 109(2):183–195. https://doi.org/10.1016/j.rse.2006.12.017

    Article  Google Scholar 

  • Giardino C, Bresciani M, Stroppiana D, Oggioni A, Morabito (2013) Optical remote sensing of lakes: an overview on Lake Maggiore. J Limnol 73. https://doi.org/10.4081/jlimnol.2014.817

  • Gitelson A, Garbuzov G, Szilagyi F, Mittenzwey KH, Karnieli A, Kaiser A (1993) Quantitative remote sensing methods for real-time monitoring of inland waters quality. Int J Remote Sens 14:1269–1295. https://doi.org/10.1080/01431169308953956

    Article  Google Scholar 

  • Gómez JA, Alonso CA, García AA (2011) Remote sensing as a tool for monitoring water quality parameters for Mediterranean Lakes of European Union water framework directive (WFD) and as a system of surveillance of cyanobacterial harmful algae blooms (SCyanoHABs). Environ Monit Assess 181(1):317–334. https://doi.org/10.1007/s10661-010-1831-7

    Article  CAS  Google Scholar 

  • Gray JR (2000) Comparability of suspended-sediment concentration and total suspended solids data (No. 4191). US Department of the interior, US Geological Survey

  • Hansen CH, Williams GP, Adjei Z, Barlow A, Nelson EJ, Miller AW (2015) Reservoir water quality monitoring using remote sensing with seasonal models: case study of five central-Utah reservoirs. Lake and Reservoir Management 31(3):225–240. https://doi.org/10.1080/10402381.2015.1065937

    Article  CAS  Google Scholar 

  • Harrington JA, Repic RL (1995) Hyperspectral and video remote sensing of oklahoma lakes. In Papers and Proceedings of Applied Geography Conferences (Vol. 18, pp. 79-86).APPLIED GEOGRAPHY CONFERENCES.

  • Helms JR, Stubbins A, Ritchie JD, Minor EC, Kieber DJ, Mopper K (2008) Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnol.Oceanogr. 53:955–969. https://doi.org/10.4319/lo.2008.53.3.0955

    Article  Google Scholar 

  • Hicks BJ, Stichbury GA, Brabyn LK, Allan MG, Ashraf S (2013) Hindcasting water clarity from Landsat satellite images of unmonitored shallow lakes in the Waikato region, New Zealand. Environ Monit Assess 185:7245–7261. https://doi.org/10.1007/s10661-013-3098-2

    Article  CAS  Google Scholar 

  • Huang JJ, Guo H, Chen B, Guo X, Singh VP (2020) Retrieval of non-optically active parameters for small scale urban waterbodies by a machine learning-based strategy. doi: https://doi.org/10.20944/preprints202004.0111.v1

  • Hunter PD, Tyler AN, Carvalho L, Codd GA, Maberly SC (2010) Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes. Remote Sens Environ 114(11):2705–2718. https://doi.org/10.1016/j.rse.2010.06.006

    Article  Google Scholar 

  • IOCCG (2006a) Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications. IOCCG Report number 5. Dartmouth, NS: IOCCG

  • IOCCG (2006b) In: Lee, Z. (Ed.), Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications. Reports of the International Ocean- Colour Coordinating Group, No. 5, Dartmouth, Canada IOCCG

  • IOCCG (2018) Earth observations in support of global water quality monitoring. In: Greb S, Dekker A, Binding C (eds) IOCCG Report Series. Canada, International Ocean Colour Coordinating Group, Dartmouth

    Google Scholar 

  • Jordan YC, Ghulam A, Chu ML (2014) Assessing the impacts of future urban development patterns and climate changes on total suspended sediment loading in surface waters using geoinformatics. J Environ Inf 24:65–79. https://doi.org/10.3808/JEI.201400283

    Article  Google Scholar 

  • Kallio K, Kutser T, Hannonen T, Koponen S, Pulliainen J, Vepsäläinen J, Pyhlaähti T (2001) Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. Sci Total Environ 268:59–77. https://doi.org/10.1016/S0048-9697(00)00685-9

    Article  CAS  Google Scholar 

  • Keiner LE, Brown CW (1999) Estimating oceanic chlorophyll concentrations with neural networks. Int J Remote Sens 20:189–194.10.1080/014311699213695

    Article  Google Scholar 

  • Khorram S, Cheshire H, Geraci AL, ROSA GL (1991) Water quality map** of Augusta Bay, Italy from Landsat-TM data. Int J Remote Sens 12(4):803–808. https://doi.org/10.1080/01431169108929696

    Article  Google Scholar 

  • Kloiber SM, Brezonik PL, Bauer ME (2002) Application of Landsat imagery to regional-scale assessments of lake clarity. Water Res 36(17):4330–4340. https://doi.org/10.1016/S0043-1354(02)00146-X

    Article  CAS  Google Scholar 

  • Kneizys FX, Shettle EP, Abreu LW, Chetwynd JH, Anderson GP(1988) Users guide to LOWTRAN 7 (No. AFGL-TR-88-0177). AFB MA, AIR FORCE GEOPHYSICS LAB HANSCOM

  • Kritzberg ES, Langenheder S, Lindström ES (2006) Influence of dissolved organic matter source on lake bacterioplankton structure and function–implications for seasonal dynamics of community composition. FEMS Microbiol Ecol 56(3):406–417. https://doi.org/10.1111/j.1574-6941.2006.00084.x

    Article  CAS  Google Scholar 

  • Kudela RM, Palacios SL, Austerberry DC, Accorsi EK, Guild LS, Torres-Perez J (2015) Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sens Environ 167:196–205. https://doi.org/10.1016/j.rse.2015.01.025

    Article  Google Scholar 

  • Kutser T (2009) Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters. Int J Remote Sens 30:4401–4425. https://doi.org/10.1080/01431160802562305

    Article  Google Scholar 

  • Kutser T, Pierson DC, KallioKY RA, Sobek S (2005) Map** lake CDOM by satellite remote sensing. Remote Sens Environ 94:535–540. https://doi.org/10.1016/j.rse.2004.11.009

    Article  Google Scholar 

  • Kutser T, Paavel B, Verpoorter C, Kauer T, Vahtmäe E (2012) Remote sensing of water quality in optically complex lakes. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 39:B8

    Google Scholar 

  • Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley Fever epidemics in Senegal. Remote Sens Environ 106:66–74. https://doi.org/10.1016/j.rse.2006.07.012

    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. https://doi.org/10.1016/j.rse.2012.11.001

    Article  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 187:384. https://doi.org/10.1007/s10661-015-4616-1

    Article  CAS  Google Scholar 

  • Lindell LT, Steinvall O, Jonsson M, Claesson T (1985) Map** of coastal-water turbidity using Landsat imagery. Int J Remote Sens 6(5):629–642. https://doi.org/10.1080/01431168508948486

    Article  Google Scholar 

  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015

    Article  Google Scholar 

  • Mahato LL, Pathak AK, Kapoor D, Patel N, Murthy M (2014) Surface water monitoring and evaluation of indravati reservoir using the application of principal component analysis using satellite remote sensing technology. In: Proceedings of Map Asia 2004. Bei**g, China, pp 26–29

    Google Scholar 

  • Maier PM, Keller S (2018) Machine learning regression on hyperspectral data to estimate multiple water parameters. In2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1-5). IEEE.DOI: https://doi.org/10.1109/WHISPERS.2018.8747010

  • Maritorena S, Guillocheau N (1996) Optical properties of water and spectral light absorption by living and non-living particles and by yellow substances in coral reef waters of French Polynesia. Mar Ecol Prog Ser 131:245–255. https://doi.org/10.3354/meps13124

    Article  Google Scholar 

  • Matthews MW, Odermatt D(2015) Improved algorithm for routine monitor-ing of cyanobacteria and eutrophication in inland and near-coastal waters. Remote Sens. V. Sagan, et al. Earth-Science Reviews 205 (2020) 103187 Environ. 156. https://doi.org/10.1016/j.rse.2014.10.010

  • Matthews MW, Bernard S, Robertson L (2012) An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters. Remote Sens Environ 124:637–652. https://doi.org/10.1016/j.rse.2012.05.032

    Article  Google Scholar 

  • McQueen DJ, Post JR, Mills EL (1986) Trophic relationships in freshwater pelagic ecosystems. Can J Fish Aquat Sci 43(8):1571–1581. https://doi.org/10.1139/f86-195

    Article  Google Scholar 

  • Menken KD, Brezonik PL, Bauer ME (2006) Influence of chlorophyll and colored dissolved organic matter (CDOM) on lake reflectance spectra: Implications for measuring lake properties by remote sensing. Lake and Reservoir Management 22(3):179–190. https://doi.org/10.1080/07438140609353895

    Article  CAS  Google Scholar 

  • Miao S, Liu C, Qian B, Miao Q (2020) Remote sensing-based water quality assessment for urban rivers: a study in linyi development area. Environ Sci Pollut Res 27(28):34586–34595. https://doi.org/10.1007/s11356-018-4038-z

    Article  CAS  Google Scholar 

  • Mishra S, Mishra DR (2012) Normalized difference chlorophyll index: a novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sens Environ 117:394–406. https://doi.org/10.1016/j.rse.2011.10.016

    Article  Google Scholar 

  • Mobley CD(1998) Hydrolight 4.0 users guide

  • Mohd Hasmadi I, Norsaliza U (2010) Analysis of SPOT-5 data for map** turbidity level of river klang. Peninsular Malaysia Appl Remote Sens J 1:14–18

    Google Scholar 

  • Moore C, Barnard A, Fietzek P, Lewis MR, Sosik HM, White S, Zielinski O (2009) Optical tools for ocean monitoring and research. Ocean Sci 5:661–684. https://doi.org/10.5194/os-5-661-2009

    Article  Google Scholar 

  • Muller-Karger FE (1992) Remote sensing of marine pollution: a challenge for the 1990s. Mar Pollut Bull 25(1-4):54–60. https://doi.org/10.1016/0025-326X(92)90186-A

    Article  Google Scholar 

  • Müller-Navarra DC, Brett MT, Park S, Chandra S, Ballantyne AP, Zorita E, Goldman CR (2004) Unsaturated fatty acid content in seston and tropho-dynamic coupling in lakes. Nature 427(6969):69–72. https://doi.org/10.1038/nature02210

    Article  CAS  Google Scholar 

  • Murphy KP (2012) Machine learning : a probabilistic perspective. MIT Press, Cambridge, Mass

    Google Scholar 

  • Murray C, Markager S, Stedmon CA, Juul-Pedersen T, Sejr MK, Bruhn A (2015) The influence of glacial melt water on bio-optical properties in two contrasting Greenlandic fjords. EstuarCoast Shelf Sci 163:72–83. https://doi.org/10.1016/j.ecss.2015.05.041

    Article  CAS  Google Scholar 

  • Myint SW, Walker ND (2002) Quantification of surface suspended sediments along a river dominated coast with NOAA AVHRR and SeaWiFS measurements: Louisiana, USA. Int J Remote Sens 23(16):3229–3249. https://doi.org/10.1080/01431160110104700

    Article  Google Scholar 

  • Neville RA, Gower JF (1977) Passive remote sensing of phytoplankton via chlorophyll α fluorescence. J Geophys Res 82:3487–3493. https://doi.org/10.1029/JC082i024p03487

    Article  Google Scholar 

  • Odermatt D, Kiselev V, Heege T, Kneubühler M, Itten KI(2008) Adjacency effect considerations and air/water constituent retrieval for Lake Constance. In: aProceedings of the 2nd MERIS/(A) ATSR user workshop. Frascati, Italy. Vol. 1.of total phosphorus (TP) in three central indiana water supply reservoirs. Water Air

  • Ogashawara I, Mishra DR, Mishra S, Curtarelli MP, Stech JL (2013) A performance review of reflectance based algorithms for predicting phycocyanin concentrations in inland waters. Remote Sens 5:4774–4798. https://doi.org/10.3390/rs5104774

    Article  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 

  • Olmanson LG, Brezonik PL, Bauer ME (2011) Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments. Water Resour Res 47. https://doi.org/10.1016/j.rse.2007.12.013

  • Olmanson LG, Brezonik PL, Bauer ME (2013) Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi river and its tributaries in Minnesota. Remote Sens Environ 130:254–265. https://doi.org/10.1016/j.rse.2012.11.023

    Article  Google Scholar 

  • Osinska-Skotak K, Kruk M, Mróz M (2007) The spatial diversification of lake water quality parameters in Mazurian lakes in summertime. Millpress, Rotterdam, The Netherlands

    Google Scholar 

  • Paerl HW, Otten TG (2013) Harmful cyanobacterial blooms: causes, consequences, and controls. Microb.Ecol 65:995–1010. https://doi.org/10.1007/s00248-012-0159-y

    Article  CAS  Google Scholar 

  • Pahlevan N, Smith B, Schalles J, Binding C, Cao Z, Ma R, Alikas K, Kangro K, Gurlin D, Hà N, Matsushita B (2020) Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: a machine-learning approach. Remote Sens Environ 240:111604. https://doi.org/10.1016/j.rse.2019.111604

    Article  Google Scholar 

  • Panda SS, Garg V, Chaubey I (2004) Artificial neural networks application in lake water quality estimation using satellite imagery. J Environ Inf 4:65–74

    Article  Google Scholar 

  • Peña-Martínez R, Ruiz-Verdú A, Domínguez-Gómez JA (2004). Map** of photosynthetic pigments in Spanish inland waters using MERIS imagery. In Proceedings of the 2004 Envisat& ERS Symposium, Salzburg, Austria (pp. 6-10)

  • Pereira LS, Andes LC, Cox AL, Ghulam A (2018) Measuring suspended-sediment concentration and turbidity in the Middle Mississippi and Lower Missouri rivers using Landsat data. J Am Water Resour Assoc 54:440–450. https://doi.org/10.1111/1752-1688.12616

    Article  Google Scholar 

  • Pérez GL, Galí M, Royer SJ, Sarmento H, Gasol JM, Marrasé C, SimóR (2016) Bio-optical characterization of offshore NW Mediterranean waters: CDOM contribution to the absorption budget and diffuse attenuation of downwelling irradiance. Deep-Sea Res I Oceanogr Res Pap 114:111–127. https://doi.org/10.1016/j.dsr.2016.05.011

    Article  CAS  Google Scholar 

  • Peterson KT, Sagan V, Sidike P, Cox A, Martinez M (2018) Suspended sediment concentration estimation from landsat imagery along the lower missouri and middle mississippi rivers using an extreme learning machine. Remote Sens 10:1503. https://doi.org/10.3390/rs10101503

    Article  Google Scholar 

  • Peterson KT, Sagan V, Sidike P, Hasenmueller EA, Sloan JJ, Knouft JH (2019) Machine learning based ensemble prediction of water quality variables using featurelevel 1 and decision-level fusion with proximal remote sensing. PhotogrammEng Remote Sens 85(4):269–280. https://doi.org/10.14358/PERS.85.4.269

    Article  Google Scholar 

  • Peterson KT, Sagan V, Sloan J (2020) Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience& Remote Sensing 57:510–525. https://doi.org/10.1080/15481603.2020.1738061

    Article  Google Scholar 

  • Preisendorfer RW (1961) Application of radiative transfer theory to light measurements in the sea. Union GeodGeophys Inst Monogr 10:11–30

    Google Scholar 

  • Qi L, Hu CM, Duan HT, Barnes BB, Ma RH (2014) An EOF-based algorithm to estimate chlorophyll a concentrations in Taihu Lake from MODIS land-band measurements: implications for near real-time applications and forecasting models. Remote Sens 6:10694–10715. https://doi.org/10.3390/rs61110694

    Article  Google Scholar 

  • Rajalahti T, Arneberg R, Berven FS, Myhr KM, Ulvik RJ, Kvalheim OM (2009) Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. ChemomIntell Lab Syst 95:35–48. https://doi.org/10.1016/j.chemolab.2008.08.004

    Article  CAS  Google Scholar 

  • Rastogi RP, Madamwar D, Incharoensakdi A (2015) Bloom dynamics of cyanobacteria and their toxins: environmental health impacts and mitigation strategies. Front Microbiol 6:1254. https://doi.org/10.3389/fmicb.2015.01254

    Article  Google Scholar 

  • Ritchie JC, Schiebe FR, McHenry R (1976) Remote sensing of suspended sediment in surface waters. PhotogrammEng Remote Sens 69:695–714

    Article  Google Scholar 

  • Ritchie JC, Zimba PV, Everitt JH (2003) Remote sensing techniques to assess water quality. Photogramm Eng Remote Sens 69(6):695–704. https://doi.org/10.14358/PERS.69.6.695

    Article  Google Scholar 

  • Rundquist DC, Han L, Schalles JF, Peake JS (1996) Remote measurement of algal chlorophyll in surface waters: the case for the first derivative of reflectance near 690 nm. Photogramm Eng Remote Sens 62(2):195–200

    Google Scholar 

  • Sagan V, Peterson KT, Maimaitijiang M, Sidike P, Sloan J, Greeling BA, Maalouf S (2020) Adams C (2020) Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth Sci Rev 22:103187. https://doi.org/10.1016/j.earscirev.2020.103187

    Article  CAS  Google Scholar 

  • Santos IR, Costa RC, Freitas U, Fillmann G (2008) Influence of effluents from a wastewater treatment plant on nutrient distribution in a coastal creek from southern Brazil. Braz Arch Biol Technol 51(1):153–162. https://doi.org/10.1590/S1516-89132008000100019

    Article  CAS  Google Scholar 

  • Satapathy DR, Vijay R, Kamble SR, Sohony RA (2010) Remote sensing of turbidity and phosphate in creeks and coast of Mumbai: an effect of organic matter. Trans GIS 146:811–832. https://doi.org/10.1111/j.1467-9671.2010.01234.x

    Article  Google Scholar 

  • Savitzky A, Golay MJ (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639. https://doi.org/10.1021/ac60214a047

    Article  CAS  Google Scholar 

  • Sawaya KE, Olmanson LG, Heinert NJ, Brezonik PL, Bauer ME (2003) Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sens Environ 88(1-2):144–156. https://doi.org/10.1016/j.rse.2003.04.006

    Article  Google Scholar 

  • Schroeder T, Brando VE, Cherukuru N, Clementson L, Blondeau-Patissier D, Dekker AG, Schaale M, Fischer J (2008) Remote sensing of apparent and inherent optical properties of Tasmanian coastal waters: application to MODIS data. InProceedings of the XIX Ocean Optics Conference, Barga, Italy. (pp. 6-10).: https://www.researchgate.net/publication/260106801

  • Shafique NA, Fulk F, Autrey BC, Flotemersch J (2003) Hyperspectral remote sensing of water quality parameters for large rivers in the Ohio River basin. In First interagency conference on research in the watershed, Benson, AZ (pp. 216-221).

  • Shirke S, Pinto SM, Kushwaha VK, Mardikar T, Vijay R (2016) Object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India. Environ Monit Assess 188(2):95. https://doi.org/10.1007/s10661-015-4981-9

    Article  Google Scholar 

  • Sidike P, Sagan V, Maimaitijiang M, Maimaitiyiming M, Shakoor N, Burken J, Mockler T, Fritschi FB (2019) dPEN: Deep progressively expanded network for map** heterogeneous agricultural landscape using WorldView-3 satellite imagery. Remote Sens Environ 221:756–772. https://doi.org/10.1016/j.rse.2018.11.031

    Article  Google Scholar 

  • Somvanshi S, Kunwar P, Singh NB, Shukla SP, Pathak V (2012) Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh. Int J Environ Sci 3(1):62–74

    CAS  Google Scholar 

  • Song Y, Song XD, Jiang H, Guo ZB, Guo QH (2010) Quantitative remote sensing retrieval for algae in inland waters. Spectrosc Spectr Anal 30(4):1075–1079. https://doi.org/10.3964/j.issn.1000-0593(2010)04-1075-05

    Article  CAS  Google Scholar 

  • Song K, Lu D, Li L, Li S, Wang Z, Du J (2012b) Remote sensing of chlorophyll-a concentration for drinking water source using genetic algorithms (GA): partial least square (PLS) modeling. Ecological Informatics 10:25–36. https://doi.org/10.1016/j.ecoinf.2011.08.006

    Article  Google Scholar 

  • Song K, Li L, Li S, Tedesco L, Hall B, Li L (2012c) Hyperspectral remote sensing of total phosphorus (TP) in three central Indiana water supply reservoirs. Water Air Soil Pollut 223(4):1481–1502. https://doi.org/10.1007/s11270-011-0959-6

    Article  CAS  Google Scholar 

  • Sudheer KP, Chaubey I, Garg V (2006) Lake water quality assessment from landsat thematic mapper data using neural network: an approach to optimal band combination selection1. JAWRA Journal of the American Water Resources Association 42(6):1683–1695. https://doi.org/10.1111/j.1752-1688.2006.tb06029.x

    Article  Google Scholar 

  • Swanson H, Zurawell R (2006) Steele Lake water quality monitoring report. Monitoring and Evaluation Branch, Environmental Assurance Division, Alberta Environment, Edmonton, AB, Canada

    Google Scholar 

  • Tehrani NC, D'Sa EJ, Osburn CL, Bianchi TS, Schaeffer BA (2013) Chromophoric dissolved organic matter and dissolved organic carbon from sea-viewing wide field-of-view sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and MERIS Sensors: case study for the Northern Gulf of Mexico. Remote Sens 5(3):1439–1464. https://doi.org/10.3390/rs5031439

    Article  Google Scholar 

  • Thurman EM(1985) Organic Geochemistry of Natural Waters.

  • Tiwari SP, Shanmugam P (2011) An optical model for the remote sensing of coloured dissolved organic matter in coastal/ocean waters. Estuar Coast Shelf Sci 93(4):396–402. https://doi.org/10.1016/j.ecss.2011.05.010

    Article  CAS  Google Scholar 

  • Turner D (2010) Remote sensing of chlorophyll a concentrations to support the Deschutes basin lake and reservoirs TMDLs. Department of Environmental Quality, Portland, OR, USA

    Google Scholar 

  • Twardowski MS, Boss E, Sullivan JM, Donaghay PL (2004) Modeling the spectral shape of absorption by chromophoric dissolved organic matter. Mar Chem 89(1-4):69–88. https://doi.org/10.1016/j.marchem.2004.02.008

    Article  CAS  Google Scholar 

  • Tyler AN, Svab E, Preston T, Présing M, Kovács WA (2006) Remote sensing of the water quality of shallow lakes: a mixture modelling approach to quantifying phytoplankton in water characterized by high-suspended sediment. Int J Remote Sens 27:1521–1537. https://doi.org/10.1080/01431160500419311

    Article  Google Scholar 

  • Van der Meer FD, De Jong SM, editors (2011) Imaging spectrometry: basic principles and prospective applications. Springer Science & Business Media.

  • Vijay R, Dey J, Sakhre S, Kumar R (2020) Impact of urbanization on creeks of Mumbai, India: a geospatial assessment approach. J Coast Conserv 24(1):1–16, 04. https://doi.org/10.1007/s11852-019-0072-y

  • Vijay R, Kushwaha VK, Pandey N, Nandy T, Wate SR (2015) Extent of sewage pollution in coastal environment of Mumbai, India: an object-based image analysis. Water and Environment Journal 29(3):365–374. https://doi.org/10.1111/wej.12115

    Article  CAS  Google Scholar 

  • Vijay R, Pinto SM, KushwahaVK, Pal S, Nandy T (2016) A multi-temporal analysis for change assessment and estimation of algal bloom in Sambhar Lake, Rajasthan, India. Environ Monit Assess188(9). https://doi.org/10.1007/s10661-016-5509-7

  • Vincent RK, Qin XM, McKay RM, Miner J, Czajkowski K, Savino J, Bridgeman T (2004) Phycocyanin detection from LANDSAT TM data for map** cyanobacterial blooms in Lake Erie. Remote Sens Environ 89:381–392. https://doi.org/10.1016/j.rse.2003.10.014

    Article  Google Scholar 

  • Visser PM, Ibelings BW, Mur LR, Walsby AE (2005) The ecophysiology of the harmful cyanobacterium Microcystis: features explaining its success and measures for its control. In: Huisman J, Matthijs HCP, Visser PM (eds) Harmful cyanobacteria. Springer-Verlag, Berlin, pp 109–142. https://doi.org/10.1007/1-4020-3022-3_6

    Chapter  Google Scholar 

  • Vollenweider RA (1976) Advances in defining critical loading levels for phosphorus in lake eutrophication. Memoriedell'IstitutoItaliano di Idrobiologia, Dott. Marco de MarchiVerbaniaPallanza.

  • Wang F, Han L, Kung HT, Van Arsdale RB (2006) Applications of Landsat-5 TM imagery in assessing and map** water quality in Reelfoot Lake, Tennessee. Int J Remote Sens 27(23):5269–5283. https://doi.org/10.1080/01431160500191704

    Article  Google Scholar 

  • Wang X, Ma L, Wang X (2010) Apply semi-supervised support vector regression for remote sensing water quality retrieving. In2010 IEEE International Geoscience and Remote Sensing Symposium (pp. 2757-2760). IEEE.DOI: https://doi.org/10.1109/IGARSS.2010.5653832

  • Wang MH, Nim CJ, Son S, Shi W (2012) Characterization of turbidity in Florida’s Lake Okeechobee and Caloosahatchee and St. Lucie Estuaries using MODIS-Aqua measurements. Water Res 46:5410–5422. https://doi.org/10.1016/j.watres.2012.07.024

    Article  CAS  Google Scholar 

  • Wang MH, Son SH, Zhang YL, Shi W (2013) Remote sensing of water optical property for China's Inland Lake Taihu using the SWIR atmospheric correction with 1640 and 2130 nm bands. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6:2505–2516. https://doi.org/10.1109/JSTARS.2013.2243820

    Article  Google Scholar 

  • Wang X, Zhang F, Kung H, Ghulam A, Trumbo A, Yang J, Ren Y, **gY (2017) Evaluation and estimation of surface water quality in an arid region based on EEMPARAFAC and 3D fluorescence spectral index: a case study of the Ebinur Lake Watershed, China. Catena 155:62–74. https://doi.org/10.1016/j.catena.2017.03.006

    Article  CAS  Google Scholar 

  • Wass PD, Marks SD, Finch JW, Leeks GJ, Ingram JK (1997) Monitoring and preliminary interpretation of in-river turbidity and remote sensed imagery for suspended sediment transport studies in the Humber catchment. Sci Total Environ 194:263–283. https://doi.org/10.1016/S0048-9697(96)05370-3

    Article  Google Scholar 

  • Wu M, Zhang W, Wang X, Luo D (2009) Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China. Environ Monit Assess 148(1-4):255–264. https://doi.org/10.1007/s10661-008-0156-2

    Article  CAS  Google Scholar 

  • Wu C, Wu J, Qi J, Zhang L, Huang H, Lou L, Chen Y (2010) Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data. Int J Remote Sens 31(9):2309–2324. https://doi.org/10.1080/01431160902973873

    Article  Google Scholar 

  • Wynne TT, Stumpf RP, Tomlinson MC, Warner RA, Tester PA, Dyble J, Fahnenstiel GL (2008) Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes. Int J Remote Sens 29:3665–3672

    Article  Google Scholar 

  • Zeinelabdeina KE, Albielyb AI (2008) Ratio image processing techniques: a prospecting tool for mineral deposits, Red Sea Hills. NE Sudan Int Arch Photogramm Remote Sens Spat Inf Sci 37:1981–1984

    Google Scholar 

  • Zhang YZ, Pulliainen J, Koponen S, Hallikainen M (2002) Water quality studies of combined optical, thermal infrared, and microwave remote sensing. Microw Opt Technol Lett 34:281–285. https://doi.org/10.1002/mop.10438

    Article  Google Scholar 

  • Zhang YC, Ma RH, Duan HT, Loiselle SA, Xu JD, Ma MX (2014) A novel algorithm to estimate algal bloom coverage to subpixel resolution in Lake Taihu. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7:3060–3068. https://doi.org/10.1109/JSTARS.2014.2327076

    Article  Google Scholar 

  • Zhu W, Yu Q, Tian YQ, Chen RF, Gardner GB (2011) Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above-surface hyperspectral remote sensing. J Geophys Res Oceans 116(C2). https://doi.org/10.1029/2010JC006523

  • Zhu W, Huang L, Sun N, Chen J, Pang S (2020) Landsat 8-observed water quality and its coupled environmental factors for urban scenery lakes: a case study of West Lake. Water Environ Res 92(2):255–265. https://doi.org/10.1002/wer.1240

    Article  CAS  Google Scholar 

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Acknowledgements

Authors are thankful to the Director of CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, for providing the necessary infrastructure and support to carry out this study.

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Jaydip Dey: Intensive review, sensors and analysis of data, limitations, and future research

Ritesh Vijay: Conceptualization, review, quality checking of data, and final editing

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Dey, J., Vijay, R. A critical and intensive review on assessment of water quality parameters through geospatial techniques. Environ Sci Pollut Res 28, 41612–41626 (2021). https://doi.org/10.1007/s11356-021-14726-4

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