{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T03:46:11Z","timestamp":1771559171761,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R2 &gt; 0.90, NRMSE &lt; 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model\u2019s utility in diverse environmental contexts.<\/jats:p>","DOI":"10.3390\/rs16010068","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario"],"prefix":"10.3390","volume":"16","author":[{"given":"Gordana","family":"Jakovljevic","sequence":"first","affiliation":[{"name":"Faculty of Architecture, Civil Engineering and Geodesy, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1530-3309","authenticated-orcid":false,"given":"Flor","family":"\u00c1lvarez-Taboada","sequence":"additional","affiliation":[{"name":"Department of Mining Engineering, School of Agrarian and Forest Engineering, Ponferrada Campus, Universidad de Le\u00f3n, 24404 Ponferrada, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1698-0800","authenticated-orcid":false,"given":"Miro","family":"Govedarica","sequence":"additional","affiliation":[{"name":"Faculty of Technical Science, University of Novi Sad, 2100 Novi Sad, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"ref_1","unstructured":"UN General Assembly (2022, December 05). Transforming Our World: The 2030 Agenda for Sustainable Development. Available online: https:\/\/www.refworld.org\/docid\/57b6e3e44.html."},{"key":"ref_2","unstructured":"United Nations (2018). Goal 6: Ensure Access to Water and Sanitation for All, UN."},{"key":"ref_3","unstructured":"European Parliament (2003). Directive 2000\/60\/EC\u2014Framework for Community Action in the Field of Water Policy, European Parliament."},{"key":"ref_4","unstructured":"European Communities (2003). Guidance Document n.o 7 Monitoring under the Water Framework Directive, Office for Official Publica-tions of the European Communities."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"115403","DOI":"10.1016\/j.watres.2019.115403","article-title":"Space-Time Chlorophyll-a Retrieval in Optically Complex Waters that Accounts for Remote Sensing and Modeling Uncertainties and Improves Remote Estimation Accuracy","volume":"171","author":"He","year":"2019","journal-title":"Water Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11270-010-0331-2","article-title":"An Application of Landsat-5TM Image Data for Water Quality Mapping in Lake Beysehir, Turkey","volume":"212","author":"Nas","year":"2010","journal-title":"Water Air Soil Pollut."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Govedarica, M., and Jakovljevic, G. (2019, January 18\u201321). Monitoring spatial and temporal variation of water quality parameters using time series of open multispectral data. Proceedings of the SPIE 11174 Seventh International Conference on Remote Sensing and Geoinformation of the Environment, Paphos, Cyprus.","DOI":"10.1117\/12.2533708"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1080\/01431160902973873","article-title":"Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data","volume":"31","author":"Wu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3390\/rs6010421","article-title":"Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Norther Vietnam)","volume":"6","author":"Ha","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.rse.2009.11.022","article-title":"Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters","volume":"114","author":"Nechad","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.4319\/lo.2014.59.4.1193","article-title":"Thermal structure and response to long-term climatic changes in Lake Qiandaohu, a deep subtropical reservoir in China","volume":"59","author":"Zhang","year":"2014","journal-title":"Limnol. Oceanogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.rse.2014.04.033","article-title":"Factors Affecting the Measurement of CDOM by Remote Sensing of Optically Complex Inland Waters","volume":"157","author":"Brezonik","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.jenvman.2017.12.070","article-title":"Empirical Estimation of Suspended Solids Concentration in the Indus Delta Region Using Landsat-7 ETM+ Imagery","volume":"209","author":"Shahzad","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.10.032","article-title":"Using multitemporal Landsat imagery and linear mixed models for assessing water quality parameters in R\u00edo Tercero reservoir (Argentina)","volume":"158","author":"Bonansea","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-4616-1","article-title":"Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea","volume":"187","author":"Lim","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11270-007-9373-5","article-title":"Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey","volume":"183","author":"Ekercin","year":"2007","journal-title":"Water Air Soil Pollut."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1080\/01431161.2016.1275056","article-title":"Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework","volume":"38","author":"Din","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.jhydrol.2017.11.026","article-title":"Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences","volume":"556","author":"Umar","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Y., Deng, R., Li, J., Hua, Z., Wang, J., Zhang, R., Liang, Y., and Tang, Y. (2022). Remote Sensing Retrieval of Total Nitrogen in the Pearl River Delta Based on Landsat8. Water, 14.","DOI":"10.3390\/w14223710"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hafeez, S., Wong, M.S., Ho, H.C., Nazeer, M., Nichol, J.E., Abbas, S., Tang, D., Lee, K.-H., and Pun, L. (2019). Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong. Remote Sens., 11.","DOI":"10.3390\/rs11060617"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1016\/j.scitotenv.2017.05.075","article-title":"Monitoring spatiotemporal variations in nutrients in a large drinking water reservoir and their relationships with hydrological and meteorological conditions based on Landsat 8 imagery","volume":"599","author":"Li","year":"2017","journal-title":"Sci. Total. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119134","DOI":"10.1016\/j.jclepro.2019.119134","article-title":"Determination of optically inactive water quality variables using Landsat 8 data: A case study in Geshlagh reservoir affected by agricultural land use","volume":"247","author":"Vakili","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117734","DOI":"10.1016\/j.envpol.2021.117734","article-title":"A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing","volume":"288","author":"Guo","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"979133","DOI":"10.3389\/fenvs.2022.979133","article-title":"Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir","volume":"10","author":"Qian","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1080\/01431161.2020.1846222","article-title":"A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery","volume":"42","author":"Guo","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1080\/15481603.2020.1738061","article-title":"Deep learning-based water quality estimation and anomaly detection using Land-sat-8\/Sentinel-2 virtual constellation and cloud computing","volume":"57","author":"Peterson","year":"2020","journal-title":"Giscience Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Leggesse, E.S., Zimale, F.A., Sultan, D., Enku, T., Srinivasan, R., and Tilahun, S.A. (2023). Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia. Hydrology, 10.","DOI":"10.3390\/hydrology10050110"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"117489","DOI":"10.1016\/j.envpol.2021.117489","article-title":"A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods","volume":"286","author":"Salvador","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1039\/c0an00387e","article-title":"Support vector machine regression (SVR\/LS-SVM)\u2014An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data","volume":"136","author":"Balabin","year":"2011","journal-title":"Analyst"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1080\/01431161.2018.1538584","article-title":"Water body mapping: A comparison of remotely sensed and GIS open data sources","volume":"40","author":"Jakovljevic","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.jhydrol.2015.10.025","article-title":"A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA","volume":"531","author":"Nolan","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.aca.2011.07.027","article-title":"Support vector machines in water quality management","volume":"703","author":"Singh","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11036","DOI":"10.1007\/s11356-014-3046-x","article-title":"Support vector machine\u2015An alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?","volume":"21","author":"Liu","year":"2014","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/15481603.2014.900983","article-title":"Machine learning approaches to coastal water quality monitoring using GOCI satellite data","volume":"51","author":"Kim","year":"2014","journal-title":"GIScience Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1080\/15481603.2013.819161","article-title":"Machine learning approaches for forest classification and change analysis using multitemporal Landsat TM images over Huntington Wildlife Forest","volume":"50","author":"Li","year":"2013","journal-title":"GIScience Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A., Warner, T.A., Maxwell, A.E., and Price, B.S. (2021). Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data. Remote Sens., 13.","DOI":"10.3390\/rs13030368"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zeng, W., Xu, K., Cheng, S., Zhao, L., and Yang, K. (2023). Regional Remote Sensing od Lake Water Transparency Based on Google Earth Engine: Preformance of Empircal Algorithm and Machine Learning. Appl. Sci., 13.","DOI":"10.3390\/app13064007"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pu, F., Ding, C., Chao, Z., Yu, Y., and Xu, X. (2019). Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11141674"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1007\/s11356-021-16004-9","article-title":"Deep learning\u2013based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images","volume":"29","author":"Cui","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_40","first-page":"1","article-title":"Chlorophyll-a Retrieval from Sentinel-2 Images Using Convolutional Neural Network Regression","volume":"19","author":"Aptoula","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","unstructured":"Sl Glasnik RS br 96\/2010 (2010). Pravilnik o Utvr\u0111ivanju Vodnih tela Povr\u0161inskih i Podzemnih Voda, Sl glasnik RS."},{"key":"ref_42","unstructured":"Agencija za zastitu zivotne sredine (2021). Ministarstvo za Za\u0161titu \u017divotne Sredine Status Povr\u0161inskih voda Srbije u Periodu od 2017\u20132019, Agencija za zastitu zivotne sredine."},{"key":"ref_43","unstructured":"European Environment Agency (2022, December 01). WISE. Available online: https:\/\/water.europa.eu\/#:~:text=The%20Water%20Information%20System%20for,from%20inland%20waters%20to%20marine."},{"key":"ref_44","unstructured":"European Environment Agency (2022, December 01). Eionet. Available online: https:\/\/dd.eionet.europa.eu\/tables\/11122."},{"key":"ref_45","unstructured":"USGS (2022, November 25). Landsat 4-7 Collection 1 Surface Reflectance Code LEDAPS Product Guide. Available online: https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/atoms\/files\/LSDS-1370_L4-7_C1-SurfaceReflectance-LEDAPS_ProductGuide-v3.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wulder, M.A., Roy, D.P., Radeloff, V.C., Loveland, T.R., Anderson, M.C., Johnson, D.M., Healey, S., Zhu, Z., Scambos, T.A., and Pahlevan, N. (2022). Fifty years of Landsat science and impacts. Remote Sens. Environ., 280.","DOI":"10.1016\/j.rse.2022.113195"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12619","DOI":"10.3390\/rs61212619","article-title":"Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+)","volume":"6","author":"Mishra","year":"2014","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.rse.2016.07.032","article-title":"Continuous calibration improvement in solar reflective bands: Landsat 5 through Landsat 8","volume":"185","author":"Mishra","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., and Chen, F. (2022). A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sens., 14.","DOI":"10.3390\/rs14081770"},{"key":"ref_50","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2011). Atmospheric\/Topographic Correction for Satellite Imagery: ATCOR-2\/3 UserGuide, DLR."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.5194\/acp-5-1855-2005","article-title":"Technical note: The libRadtran software package for radiative transfer calculations\u2014description and examples of use","volume":"5","author":"Mayer","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_52","unstructured":"ESA (2020, August 15). Level-2A Algorithm Overview. Available online: https:\/\/earth.esa.int\/web\/sentinel\/technical-guides\/sentinel-2-msi\/level-2a\/algorithm."},{"key":"ref_53","unstructured":"Fausset, L.V. (1993). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Pearson."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S0034-4257(00)00088-2","article-title":"A neural network multipara meter algorithm for SSM\/I ocean retrievals: Comparisons and validations","volume":"72","author":"Krasnopolsky","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.geoderma.2011.08.001","article-title":"Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy","volume":"166","author":"Vohland","year":"2011","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liang, Y., Yin, F., Xie, D., Liu, L., Zhang, Y., and Ashraf, T. (2022). Inversion and Monitoring of the TP Concentration in Taihu Lake Using the Landsat-8 and Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14246284"},{"key":"ref_57","unstructured":"S. R. b. 74\/2011 (1968). Uredba o klasifikaciji Voda, Sluzbeni glasnik RS."},{"key":"ref_58","unstructured":"S. R. b. 50\/2012 (2012). Uredba o Grani\u010dnim Vrednostima Zaga\u0111uju\u0107ih Materija u Povr\u0161inskim i Podzemnim Vodama i Sedimentu i Rokovima za Njihovo Dostizanje, Sluzbeni Glasnik."},{"key":"ref_59","unstructured":"S. R. b. 74\/2011 (2011). Pravilnik o Parametrima Ekolo\u0161kog i Hemijskog Statusa Povr\u0161inskih Voda i Parametrima Hemijskog i Kvantitativnog Statusa Podzemnih Voda, Sluzbeni Glasnik."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2004.07.012","article-title":"Using MODIS Terra 250 m Imagery to Map Concentrations of Total Suspended Matter in Coastal Waters","volume":"93","author":"Miller","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.5194\/os-8-1055-2012","article-title":"Improvement to the PhytoDOAS method for identification of coccolithophores using hyperspectral satellite data","volume":"8","author":"Sadeghi","year":"2012","journal-title":"Ocean Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Barraza-Moraga, F., Alcayaga, H., Pizarro, A., F\u00e9lez-Bernal, J., and Urrutia, R. (2022). Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14225647"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Roman, A., Tovar-Sanchez, A., Gauci, A., Deidun, A., Cabellero, I., Colica, E., D\u2019Amivo, S., and Navarro, G. (2023). Water-Quality Moni-toring with a UAV-Mounted Multispectral Camera in Coastal Waters. Remote Sens., 15.","DOI":"10.3390\/rs15010237"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, Y., Chen, Y., Qiao, X., and Qian, W. (2023). Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning. Drones, 7.","DOI":"10.3390\/drones7010002"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Quang, N.H., Dinh, N.T., Dien, N.R., and Son, L.T. (2023). Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh\u2019s Coastal Zone of Vietnam. Sustainability, 15.","DOI":"10.3390\/su15021410"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yang, H., Du, Y., Zhao, H., and Chen, F. (2022). Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14051267"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1007\/s11270-012-1243-0","article-title":"Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network","volume":"223","author":"Chebud","year":"2012","journal-title":"Water Air Soil Pollut."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Mumtaz, R., Anwar, Z., Shaukat, A., Arif, O., and Shafait, F. (2022). A Multi\u2013Step Approach for Optically Active and Inactive Water Quality Parameter Estimation Using Deep Learning and Remote Sensing. Water, 14.","DOI":"10.3390\/w14132112"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Schmidhuber, J. (2014). Deep Learning in Neural Networks: An Overview. arXiv.","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref_70","unstructured":"Prechelt, L. (2012). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_71","unstructured":"SEPA (2023, October 15). Stanje Kvaliteta Vode Vodotoka. Agencija za \u017divotnu Sredinu. Available online: http:\/\/77.46.150.213:8080\/apex\/f?p=406:2::::::."},{"key":"ref_72","first-page":"41","article-title":"Application of remote sensing techniques for water quality monitoring","volume":"20","author":"Seyhan","year":"1986","journal-title":"Aquat. Ecol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/68\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:41:04Z","timestamp":1760132464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,23]]},"references-count":72,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010068"],"URL":"https:\/\/doi.org\/10.3390\/rs16010068","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,23]]}}}