Abstract
Primary productivity expressed as the abundance of phytoplankton measured by the chlorophyll-a concentration (Chl-a), and water clarity in terms of suspended particulate matter, are considered as key indicators defining the water quality of any aquatic system. To maintain the good water quality, it is important to continuously monitor the spatio-temporal variability of these key indicators. Optical satellite remote sensing techniques in the visible spectral range are well known for their cost-effectiveness in estimating the water quality features on sufficient spatial and temporal scale with better radiometry. To overcome that, level 1C images from the multi-spectral instrument (MSI) onboard Sentinel 2 (S2), a medium to high resolution satellite sensor, were used in the present study. Even though there has been a radical improvement in the development of semi-analytical optical algorithms especially using band ratio methods, they need accurate spectral and specific absorption characteristics which are challenging to obtain for many inland water bodies. Machine learning algorithms, on the other hand can statistically derive the spatio-temporal distribution of chlorophyll-a and suspended matter from explicit optical relationships without the complexities of conventional empirical or semi-analytical algorithms. In this study, the best suitable machine learning (ML) algorithm using S2-MSI data to retrieve (Chl-a) and total suspended matter (TSM) for tropical lakes and inland waters were identified from the available machine learning models. The ML prediction models were trained using the surface reflectance together with the vegetation and water indices that are sensitive to Chl-a and TSM obtained from Sentinel-2 data. In situ Chl-a values for validation of the machine learning models were obtained from multiple field surveys conducted along the inland water bodies (Vellar river in Tamilnadu, and Paleru and Karedu inland tributaries of Krishna River in Andhra Pradesh) and a tropical coastal region (Palk Bay) in the south east coast of India (Palk Bay). From the validation analysis it was evident that Support Vector Machine (SVM) performed better in deriving the Chl-a (R2 = 0.81; RMSE = 0.19) and Radom Forest (RF) model performed better in modeling TSM distribution along the studied water bodies (R2 = 0.98; RMSE = 1.46). Validation of ML-based models for optically different water bodies proved the efficiency of the SVM and RF models in estimating the optical constituents in inland water bodies and tropical coastal waters with optical complexities from mixed composition of water constituents. The capability of medium resolution satellite like Sentinel 2 can hence provide means to establish tools to monitor the biophysical conditions of small inland water system effectively when coupled with machine learning methods.
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References
Ansper A, Alikas K (2019) Retrieval of chlorophyll a from Sentinel-2 MSI data for the European Union water framework directive reporting purposes. Remote Sens 11(1):64
Battude M, Al Bitar A, Morin D, Cros J, Huc M, Sicre (2016) Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sens Environ 184:668–681
Berdalet E, Fleming LE, Gowen R, Davidson K, Hess P, Backer LC, Enevoldsen H (2016) Marine harmful algal blooms, human health and wellbeing: challenges and opportunities in the 21st century. J Mar Biol Assoc UK 96(1):61–91
Birth GS, McVey GR (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agron J 60(6):640–643
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Bukata RP, Jerome JH, Kondratyev KY, Pozdnyakov DV (2018) Optical properties and remote sensing of inland and coastal waters. CRC Press
Carder KL, Chen FR, Lee Z, Hawes SK, Cannizzaro JP (2003) MODIS ocean science team algorithm theoretical basis document. ATBD, 19(Version 7), 7–18
Ceyhun Ö, Yalçın A (2010) Remote sensing of water depths in shallow waters via artificial neural networks. Estuar Coast Shelf Sci 89(1):89–96
Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In; Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794
Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H (2015) Xgboost: extreme gradient boosting. R package version 0.4–2, 1(4):1–4
Chen X, Li YS, Liu Z, Yin K, Li Z, Wai OW, King B (2004) Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong. Cont Shelf Res 24(16):1827–1843
Clark DK (1981) Phytoplankton pigment algorithms for the Nimbus-7 CZCS. In: Oceanography from space. Springer, Boston, MA, pp 227–237
Danovaro R, Carugati L, Berzano M, Cahill AE, Carvalho S, Chenuil A (2016) Implementing and innovating marine monitoring approaches for assessing marine environmental status. Front Mar Sci 3:213
Dekker AG, Vos RJ, Peters SWM (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
Doerffer R, Schiller H (2007) The MERIS Case 2 water algorithm. Int J Remote Sens 28(3–4):517–535
Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87
Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 9:155–161
Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36
Forkuor G, Dimobe K, Serme I, Tondoh JE (2018) Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GISci Remote Sens 55(3):331–354
Fritsch S, Guenther F, Guenther MF (2019) Package ‘neuralnet’. Train Neural Netw
Gattuso JP, Frankignoulle M, Wollast R (1998) Carbon and carbonate metabolism in coastal aquatic ecosystems. Annu Rev Ecol Syst 29(1):405–434
Hoagland PADM, Anderson DM, Kaoru Y, White AW (2002) The economic effects of harmful algal blooms in the United States: estimates, assessment issues, and information needs. Estuaries 25(4):819–837
Hooker SB, McClain CR (2000) The calibration and validation of SeaWiFS data. Prog Oceanogr 45(3–4):427–465
Jang E, Im J, Ha S, Lee S, Park YG (2016) Estimation of water quality index for coastal areas in Korea using GOCI satellite data based on machine learning approaches. Korean J Remote Sens 32(3):221–234
Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab-an S4 package for kernel methods in R. J Stat Softw 11(9):1–20
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(1):1–26
Kuhn M, Johnson K (2013) Applied predictive modeling, vol 26. Springer, New York, p 13
Kupssinskü LS, Guimaraes TT, De Souza EM, Zanotta DC, Veronez MR, Gonzaga Jr L, Mauad FF (2020) A method for chlorophyll-a and suspended solids prediction through remote sensing and machine learning. Sensors (Basel, Switzerland) 20(7)
Kwon YS, Baek SH, Lim YK, Pyo J, Ligaray M, Park Y, Cho KH (2018) Monitoring coastal chlorophyll-a concentrations in coastal areas using machine learning models. Water 10(8):1020
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(1):66–74
Lenoble J, Herman M, Deuzé JL, Lafrance B, Santer R, Tanré D (2007) A successive order of scattering code for solving the vector equation of transfer in the earth’s atmosphere with aerosols. J Quant Spectrosc Radiat Transfer 107(3):479–507
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22
Lin Q, Zhang K, McGowan S, Capo E, Shen J (2021) Synergistic impacts of nutrient enrichment and climate change on long-term water quality and ecological dynamics in contrasting shallow-lake zones. Limnol Oceanogr 66(9):3271–3286
Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58(3–4):458–465
Liu XQ, Huang TL, Li N, Yang SY, Li Y, Xu J, Wang HY (2019) Algal bloom and mechanism of hypoxia in the metalimnion of the Lijiahe Reservoir during thermal stratification. Huan jing ke xue= Huanjing kexue 40(5):2258–2264
Maier HR, Morgan N, Chow CW (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19(5):485–494
Mamun M, Lee SJ, An KG (2018) Temporal and spatial variation of nutrients, suspended solids, and chlorophyll in Yeongsan watershed. J Asia-Pacific Biodivers 11(2):206–216
McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432
Matsushita B, Yang W, Jaelani LM, Setiawan F, Fukushima T (2016) Monitoring water quality with remote sensing image data. Remote Sens Sustain, 163–189
Matthews MW (2011) A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int J Remote Sens 32(21):6855–6899
Menon N, George G, Ranith R, Sajin V, Murali S, Abdulaziz A et al (2021) Citizen science tools reveal changes in estuarine water quality following demolition of buildings. Remote Sens 13(9):1683
Meyer JL, Sale MJ, Mulholland PJ, Poff NL (1999) Impacts of climate change on aquatic ecosystem functioning and health 1. JAWRA J Am Water Resour Assoc 35(6):1373–1386
Nonhebel S, Kastner T (2011) Changing demand for food, livestock feed and biofuels in the past and in the near future. Livest Sci 139(1–2):3–10
Oommen T, Misra D, Twarakavi NK, Prakash A, Sahoo B, Bandopadhyay S (2008) An objective analysis of support vector machine based classification for remote sensing. Math Geosci 40(4):409–424
Park TG, Lim WA, Park YT, Lee CK, Jeong HJ (2013) Economic impact, management and mitigation of red tides in Korea. Harmful Algae 30:S131–S143
Peterson KT, Sagan V, Sidike P, Hasenmueller EA, Sloan JJ, Knouft JH (2019) Machine learning-based ensemble prediction of water-quality variables using feature-level and decision-level fusion with proximal remote sensing. Photogramm Eng Remote Sens 85(4):269–280
Rathore SS, Chandravanshi P, Chandravanshi A, Jaiswal K (2016) Eutrophication: impacts of excess nutrient inputs on aquatic ecosystem. IOSR J Agric Vet Sci 9(10):89–96
Reis Costa P (2016) Impact and effects of paralytic shellfish poisoning toxins derived from harmful algal blooms to marine fish. Fish Fish 17(1):226–248
Saberioon M, Brom J, Nedbal V, Souc̆ek P, Císar̆ P (2020) Chlorophyll-a and total suspended solids retrieval and mapping using Sentinel-2A and machine learning for inland waters. Ecol Ind 113:106236
Sathyendranath S, Abdulaziz A, Menon N, George G, Evers-King H, Kulk G et al (2020) Building capacity and resilience against diseases transmitted via water under climate perturbations and extreme weather stress. In: Ferretti S (ed) Space Capacity Building in the XXI Century, pp 281–298
Schindler DW (1987) Detecting ecosystem responses to anthropogenic stress. Can J Fish Aquat Sci 44(S1):s6–s25
Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220(6):888–895
Su RC, Blomquist TM, Kleinhenz AL, Khalaf FK, Dube P, Lad A et al (2019) Exposure to the harmful algal bloom (HAB) toxin microcystin-LR (MC-LR) prolongs and increases severity of dextran sulfate sodium (DSS)-induced colitis. Toxins 11(6):371
Thissen UVBR, Van Brakel R, De Weijer AP, Melssen WJ, Buydens LMC (2003) Using support vector machines for time series prediction. Chemom Intell Lab Syst 69(1–2):35–49
Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T (2016) First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sens 8(8):640
Tyler AN, Hunter PD, Spyrakos E, Groom S, Constantinescu AM, Kitchen J (2016) Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters. Sci Total Environ 572:1307–1321
Usali N, Ismail MH (2010) Use of remote sensing and GIS in monitoring water quality. J Sustain Dev 3(3):228
Uudeberg K, Ansko I, Põru G, Ansper A, Reinart A (2019) Using optical water types to monitor changes in optically complex inland and coastal waters. Remote Sens 11(19):2297
Waller DH, Hart WC (1986) Solids, nutrients, and chlorides in urban runoff. In: Urban runoff pollution. Springer, Berlin, Heidelberg, pp 59–85
Zarco-Tejada PJ, Ustin SL (2001) Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites. In IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217) (Vol. 1, pp. 342–344). IEEE.
Acknowledgements
The authors would like to put on record their immense gratitude to the European Space Agency (ESA) for making the Sentinel-2/MSI data freely available. All the participants of the field campaigns are sincerely thanked for their cooperation during the data collection. Authors RR, AJK, NN and LHP acknowledge the financial and infrastructural support provided by Nansen Environmental Remote Sensing Center, Norway and Nansen Environmental Research Centre (India). CJ thanks the General Manager, RRSC-East, Chief General Manager, RCs, NRSC, for their support and encouragement during this work.
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Ranith, R., Menon, N.N., Joseph, K.A., Jayaram, C., Pettersson, L.H. (2022). Water Quality Assessment from Medium Resolution Satellite Data Using Machine Learning Methods. In: Jha, C.S., Pandey, A., Chowdary, V., Singh, V. (eds) Geospatial Technologies for Resources Planning and Management. Water Science and Technology Library, vol 115. Springer, Cham. https://doi.org/10.1007/978-3-030-98981-1_9
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