{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:45:24Z","timestamp":1760132724034,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model\u2019s performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.<\/jats:p>","DOI":"10.3390\/s23229258","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T01:54:12Z","timestamp":1700445252000},"page":"9258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-9097","authenticated-orcid":false,"given":"Chiara","family":"Lapucci","sequence":"first","affiliation":[{"name":"National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"},{"name":"LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2013-1521","authenticated-orcid":false,"given":"Andrea","family":"Antonini","sequence":"additional","affiliation":[{"name":"LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8143-1651","authenticated-orcid":false,"given":"Emanuele","family":"B\u00f6hm","sequence":"additional","affiliation":[{"name":"National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8191-8179","authenticated-orcid":false,"given":"Emanuele","family":"Organelli","sequence":"additional","affiliation":[{"name":"National Research Council (CNR), Institute of Marine Science (ISMAR), Via Fosso del Cavaliere 100, 00133 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1136-1963","authenticated-orcid":false,"given":"Luca","family":"Massi","sequence":"additional","affiliation":[{"name":"Dipartimento di Biologia, Universit\u00e0 Degli Studi di Firenze, Via Micheli 1, 50121 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Ortolani","sequence":"additional","affiliation":[{"name":"LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"},{"name":"National Research Council (CNR), Institute for BioEconomy (IBE), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo","family":"Brandini","sequence":"additional","affiliation":[{"name":"National Research Council (CNR), Institute of Marine Science (ISMAR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"},{"name":"LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6475-4600","authenticated-orcid":false,"given":"Fabio","family":"Maselli","sequence":"additional","affiliation":[{"name":"National Research Council (CNR), Institute for BioEconomy (IBE), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hanley, T.C., and La Pierre, K.J. (2015). Trophic Ecology: Bottom-Up and Top-Down Interactions across Aquatic and Terrestrial Systems, Cambridge University Press.","DOI":"10.1017\/CBO9781139924856"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/B978-0-12-384719-5.00145-3","article-title":"Trophic Levels","volume":"Volume 5","author":"Yodzis","year":"2001","journal-title":"Encyclopedia of Biodiversity"},{"key":"ref_3","unstructured":"(2000). Directive 2000\/60\/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off. J. Eur. Communities, L327, 1\u201372."},{"key":"ref_4","unstructured":"(2008). Directive 2008\/56\/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). Off. J. Eur. Union, L164, 19\u201340."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liquete, C., Piroddi, C., Drakou, E.G., Gurney, L., Katsanevakis, S., Charef, A., and Egoh, B. (2013). Current Status and Future Prospects for the Assessment of Marine and Coastal Ecosystem Services: A Systematic Review. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0067737"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"235","DOI":"10.5194\/os-16-235-2020","article-title":"Marine monitoring in Europe: Is it adequate to address environmental threats and pressures?","volume":"16","author":"Painting","year":"2020","journal-title":"Ocean. Sci."},{"key":"ref_7","first-page":"305","article-title":"A coordinator\u2019s guide to volunteer lake monitoring methods","volume":"96","author":"Carlson","year":"1996","journal-title":"N. Am. Lake Manag. Soc. (NALMS)"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.marchem.2006.12.017","article-title":"River basin nutrient delivery to the coastal sea: Assessing its potential to sustain new production of non-siliceous algae","volume":"1\u20132","author":"Billen","year":"2007","journal-title":"Mar. Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/nature11397","article-title":"An index to assess the health and benefits of the global ocean","volume":"488","author":"Halpern","year":"2012","journal-title":"Nature"},{"key":"ref_10","unstructured":"Van De Bund, W., and Solimini, A. (2007). Ecological Quality Ratios for Ecological Quality Assessment in Inland and Marine Waters, Office for Official Publications of the European Comission. EUR 22722 EN; JRC36757."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1002\/(SICI)1099-095X(199805\/06)9:3<329::AID-ENV308>3.0.CO;2-9","article-title":"Characterization of the trophic conditions of marine coastal waters with special reference to the NW Adriatic Sea: Proposal for a trophic scale, turbidity and generalized water quality index","volume":"9","author":"Vollenweider","year":"1998","journal-title":"Environmetrics"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Parra, L., Viciano-Tudela, S., Carrasco, D., Sendra, S., and Lloret, J. (2023). Low-Cost Microcontrol-ler-Based Multiparametric Probe for Coastal Area Monitoring. Sensors, 23.","DOI":"10.3390\/s23041871"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fmars.2019.00519","article-title":"Advancing Observation of Ocean Biogeochemistry, Biology, and Ecosystems With Cost-Effective in situ Sensing Technologies","volume":"6","author":"Wang","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_14","unstructured":"Mobley, C.D. (2022). . The Oceanic Optics Book, International Ocean Colour Coordinating Group (IOCCG)."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gad, M., Saleh, A.H., Hussein, H., Farouk, M., and Elsayed, S. (2022). Appraisal of Surface Water Quality of Nile River Using Water Quality Indices, Spectral Signature and Multivariate Modeling. Water, 14.","DOI":"10.3390\/w14071131"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cornec, M., Claustre, H., Mignot, A., Guidi, L., Lacour, L., Poteau, A., d\u2019Ortenzio, F., Gentili, B., and Schmechtig, C. (2021). Deep chlorophyll maxima in the global ocean: Occurrences, drivers and characteristics. Glob. Biogeochem. Cycles, 35.","DOI":"10.1029\/2020GB006759"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Murray, C., Larson, A., Goodwill, J., Wang, Y., Cardace, D., and Akanda, A.S. (2022). Water Quality Observations from Space: A Review of Critical Issues and Challenges. Environments, 9.","DOI":"10.3390\/environments9100125"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, L., Sun, X., Gao, Y., Lan, Z., Wang, Y., Zhai, H., Li, J., Wang, W., and Chen, M. (2022). A New Method for Calculating Water Quality Parameters by Integrating Space\u2013Ground Hyperspectral Data and Spectral-In Situ Assay Data. Remote Sens., 14.","DOI":"10.20944\/preprints202205.0387.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1109\/TGRS.2003.812907","article-title":"Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality","volume":"41","author":"Brando","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6321","DOI":"10.3390\/s8106321","article-title":"A Multivariate Model for Coastal Water Quality Mapping Using Satellite Remote Sensing Images","volume":"8","author":"Su","year":"2008","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111632","DOI":"10.1016\/j.rse.2020.111632","article-title":"Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas","volume":"239","author":"Arabi","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","unstructured":"Woodman, M.L. (2023). Field-Validated Inter-Comparison of Sentinel-2 MSI and Sentinel-3 OLCI Images to Assess Water Quality in the Indian River Lagoon, Florida. [Doctoral Thesis, Kent State University]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.3390\/rs15061653","article-title":"Band Ratios Combination for Estimating Chlorophyll-a from Sentinel-2 and Sentinel-3 in Coastal Waters","volume":"15","author":"Tran","year":"2023","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lapucci, C., Maselli, F., Chini Zittelli, G., Betti, G., Vannucchi, V., Perna, M., Taddei, S., Gozzini, B., Ortolani, A., and Brandini, C. (2022). Towards the Prediction of Favourable Conditions for the Harmful Algal Bloom Onset of Ostreopsis ovata in the Ligurian Sea Based on Satellite and Model Data. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10040461"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.oceano.2019.04.001","article-title":"Reflectance spectra classification for the rapid assessment of water ecological quality in Mediterranean ports","volume":"61","author":"Massi","year":"2019","journal-title":"Oceanologia"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1080\/22797254.2021.1918582","article-title":"Evaluation of Landsat-8 OLI and Sentinel-2 MSI images for estimating the ecological quality of port waters","volume":"54","author":"Pieri","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_29","unstructured":"Lapucci, C., Massi, L., Pieri, M., Perna, M., Gozzini, B., Brandini, C., Ortolani, A., and Maselli, F. (2022, January 20\u201324). Use of sentinel-3 OLCI images for estimating the trophic status of Tuscan coastal waters. Proceedings of the Atti Convegno ASITA, Genoa, Italy."},{"key":"ref_30","unstructured":"(2010). DL 269\/2010, Decreto Legislativo 13 ottobre 2010, n.190 Attuazione della direttiva 2008\/56\/CE che istituisce un quadro per l\u2019azione comunitaria nel campo della politica per l\u2019ambiente marino. Gazz. Uff., 270, 1\u201319."},{"key":"ref_31","unstructured":"Zampoukas, N., Piha, H., Bigagli, E., Hoepffner, N., Hanke, G., and Cardoso, A.C. (2012). Monitoring for the Marine Strategy Framework Directive: Requirements and Options, Publications Office of the European Union. JRC 68179, EUR 25187 EN."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1016\/j.marpolbul.2007.05.013","article-title":"A revisitation of TRIX for trophic status assessment in the light of the European Water Framework Directive: Application to Italian coastal waters","volume":"54","author":"Pettine","year":"2007","journal-title":"Mar. Pollut. Bull."},{"key":"ref_33","unstructured":"(2023, May 02). h.Sentinels Copernicus. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-3-olci\/coverage."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2019.04.021","article-title":"Chlorophyll algorithms for ocean color sensors\u2014OC4, OC5 & OC6","volume":"229","author":"Werdell","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"20920","article-title":"Under the hood of satellite empirical chl-a algorithms: Revealing the dependencies of maximum band ratio algorithms on inherent optical properties","volume":"16","author":"Sauer","year":"2012","journal-title":"Earth and Oceanographic Science Faculty Work"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jha, C.S., Pandey, A., Chowdary, V., and Singh, V. (2022). Geospatial Technologies for Resources Planning and Management. Water Science and Technology Library, Springer.","DOI":"10.1007\/978-3-030-98981-1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"221","DOI":"10.7780\/kjrs.2016.32.3.2","article-title":"Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches","volume":"32","author":"Jang","year":"2016","journal-title":"Korean J. Remote Sens."},{"key":"ref_38","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_39","unstructured":"Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., and Harmouch, H. (2022). The effects of data quality on machine learning performance. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"199","DOI":"10.4081\/jlimnol.2004.199","article-title":"Trophic conditions of marine coastal waters: Experience in applying the Trophic Index TRIX to two areas of the Adriatic and Tyrrhenian seas","volume":"63","author":"Giovanardi","year":"2004","journal-title":"J. Limnol."},{"key":"ref_41","unstructured":"Vollenweider, R.A., and Kerekes, J. (1982). Eutrophication of Waters. Monitoring, Assessment and Control, Organization for Economic Co-Operation and Development (OECD)."},{"key":"ref_42","unstructured":"Giovanardi, F., and Tromellini, E. (1990, January 21\u201324). Statistical assessment of trophic conditions. Application of the OECD Methodology to the Marine Environment. In Proceedings of the International Conference, Bologna, Italy."},{"key":"ref_43","unstructured":"(2023, May 02). Scribbr. Available online: https:\/\/www.scribbr.com\/statistics\/interquartile-range\/."},{"key":"ref_44","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedragosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12.","DOI":"10.3390\/computers12050091"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"632","DOI":"10.2112\/SI75-127.1","article-title":"Defining the Trophic Status of Maltese (Central Mediterranean) Coastal Waters through the Computation of Water Quality Indices Based on Satellite Data","volume":"75","author":"Farrugia","year":"2016","journal-title":"J. Coast. Res. Spec. Issue"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, G., Ye, X., Xu, Q., Yin, X., and Xu, S. (2023). Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network. Remote Sens., 15.","DOI":"10.3390\/rs15143696"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Su, H., Lu, X., Chen, Z., Zhang, H., Lu, W., and Wu, W. (2021). Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040576"},{"key":"ref_50","unstructured":"Cicero, A.M., Giovanardi, F., Bacci, T., Gennaro, P., Maggi, C., Penna, M., Sante Rende, F., Tomassetti, P., and Trabucco, B. (2012). Implementazione della Direttiva 2000\/60\/CE Classificazione dello Stato Ecologico Dei Corpi Idrici Delle Acque Marino Costiere e Di Transizione, ISPRA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"379","DOI":"10.5194\/os-12-379-2016","article-title":"Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data","volume":"12","author":"Pitarch","year":"2016","journal-title":"Ocean Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Shybanov, E., Papkova, A., Korchemkina, E., and Suslin, V. (2023). Blue Color Indices as a Reference for Remote Sensing of Black Sea Water. Remote Sens., 15.","DOI":"10.3390\/rs15143658"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9258\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:12Z","timestamp":1760131512000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":52,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229258"],"URL":"https:\/\/doi.org\/10.3390\/s23229258","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}