{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:01:41Z","timestamp":1777604501191,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,6]],"date-time":"2021-02-06T00:00:00Z","timestamp":1612569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971384, 41630963"],"award-info":[{"award-number":["41971384, 41630963"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian\u2019s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 \u00b5g\/L, MAE of 0.22 \u00b5g\/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.<\/jats:p>","DOI":"10.3390\/rs13040576","type":"journal-article","created":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T14:07:19Z","timestamp":1612706839000},"page":"576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3926","authenticated-orcid":false,"given":"Hua","family":"Su","sequence":"first","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National &amp; Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemei","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National &amp; Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3654-9658","authenticated-orcid":false,"given":"Zuoqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National &amp; Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-9442","authenticated-orcid":false,"given":"Hongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Hong Kong, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1303-9820","authenticated-orcid":false,"given":"Wenfang","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National &amp; Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenting","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National &amp; Local Joint Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.rse.2014.11.017","article-title":"Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters","volume":"158","author":"Harvey","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Blix, K., Li, J., Massicotte, P., and Matsuoka, A. (2019). Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI. Remote Sens., 11.","DOI":"10.3390\/rs11182076"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1006\/ecss.2001.0917","article-title":"Toward a Predictive Understanding of Primary Productivity in a Temperate, Partially Stratified Estuary","volume":"55","author":"Harding","year":"2002","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"045005","DOI":"10.1088\/1748-9326\/4\/4\/045005","article-title":"Estimation of chlorophyll- a concentration in case II waters using MODIS and MERIS data\u2014successes and challenges","volume":"4","author":"Moses","year":"2009","journal-title":"Environ. Res. Lett."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1364\/AO.22.000020","article-title":"Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparison of ship determinations and CZCS estimates","volume":"22","author":"Gordon","year":"1983","journal-title":"Appl. Opt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0048-9697(00)00694-X","article-title":"Using a compact airborne spectrographic imager to monitor phytoplankton biomass in a series of lakes in north Wales","volume":"268","author":"George","year":"2001","journal-title":"Sci. Total Environ."},{"key":"ref_9","first-page":"138","article-title":"A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake","volume":"74","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","first-page":"52","article-title":"Estimation of chlorophyll-a concentrations in diverse water bodies using ratio-based NIR\/Red indices","volume":"6","author":"Yang","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ecoinf.2013.04.005","article-title":"Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters","volume":"16","author":"Tao","year":"2013","journal-title":"Ecol. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.asr.2018.04.024","article-title":"High performance of chlorophyll- a prediction algorithms based on simulated OLCI Sentinel-3A bands in cyanobacteria-dominated inland waters","volume":"62","author":"Watanabe","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.rse.2011.10.016","article-title":"Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters","volume":"117","author":"Mishra","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1080\/014311699212470","article-title":"Interpretation of the 685nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS","volume":"20","author":"Gower","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"El-Habashi, A., Ioannou, I., Tomlinson, M.C., Stumpf, R.P., and Ahmed, S. (2016). Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques. Remote Sens., 8.","DOI":"10.3390\/rs8050377"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Molkov, \u0410.\u0410., Fedorov, S.V., Pelevin, V.V., and Korchemkina, E.N. (2019). Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101215"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1093\/plankt\/fbr079","article-title":"Time series analysis of algal blooms in Lake of the Woods using the MERIS maximum chlorophyll index","volume":"33","author":"Binding","year":"2011","journal-title":"J. Plankton Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1016\/j.rse.2009.05.012","article-title":"A novel ocean color index to detect floating algae in the global oceans","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.rse.2019.03.038","article-title":"High-frequency observation of floating algae from AHI on Himawari-8","volume":"227","author":"Chen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1029\/2012JC008292","article-title":"Quantification of floating macroalgae blooms using the scaled algae index","volume":"118","author":"Garcia","year":"2013","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2011.11.013","article-title":"Review of constituent retrieval in optically deep and complex waters from satellite imagery","volume":"118","author":"Odermatt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3582","DOI":"10.1016\/j.rse.2008.04.015","article-title":"A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation","volume":"112","author":"Gitelson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1029\/2011JC007395","article-title":"Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference","volume":"117","author":"Hu","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2014.03.012","article-title":"Assessment of NIR-red algorithms for observation of chlorophyll-a in highly turbid inland waters in China","volume":"93","author":"Huang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2014.08.035","article-title":"Influence of a red band-based water classification approach on chlorophyll algorithms for optically complex estuaries","volume":"155","author":"Sun","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1016\/j.rse.2009.02.005","article-title":"A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China","volume":"113","author":"Le","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2015.03.019","article-title":"Retrieving the evolution of vertical profiles of Chlorophyll-a from satellite observations using Hidden Markov Models and Self-Organizing Topological Maps","volume":"163","author":"Charantonis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ruescas, A., Hieronymi, M., Mateo-Garcia, G., Koponen, S., Kallio, K., and Camps-Valls, G. (2018). Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data. Remote Sens., 10.","DOI":"10.3390\/rs10050786"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Blix, K., P\u00e1lffy, K., T\u00f3th, V.R., and Eltoft, T. (2018). Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI. Water, 10.","DOI":"10.3390\/w10101428"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"134524","DOI":"10.1016\/j.scitotenv.2019.134524","article-title":"An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze","volume":"700","author":"Lei","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(02)00009-3","article-title":"Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data","volume":"81","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.marpolbul.2017.04.022","article-title":"Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm","volume":"119","author":"Kong","year":"2017","journal-title":"Mar. Pollut. Bull."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.jhydrol.2019.04.085","article-title":"Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions","volume":"574","author":"Huang","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Su, H., Yang, X., Lu, W., and Yan, X.-H. (2019). Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations. Remote Sens., 11.","DOI":"10.3390\/rs11131598"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2015.02.001","article-title":"Aquatic color radiometry remote sensing of coastal and inland waters: Challenges and recom-mendations for future satellite missions","volume":"160","author":"Mouw","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2015.11.037","article-title":"Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal waters of Hong Kong","volume":"532","author":"Nazeer","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_39","first-page":"68","article-title":"Evaluation of the suitability of MODIS, OLCI and OLI for mapping the distribution of total suspended matter in the Barra Bonita Reservoir (Tiet\u00ea River, Brazil)","volume":"4","author":"Bernardo","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecss.2018.05.031","article-title":"Spatial downscaling of MODIS Chlorophyll-a using Landsat 8 images for complex coastal water monitoring","volume":"209","author":"Fu","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_41","first-page":"54","article-title":"Evolution of the C2RCC neural network for sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters","volume":"740","author":"Brockmann","year":"2016","journal-title":"ESASP"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Toming, K., Kutser, T., Uiboupin, R., Arikas, A., Vahter, K., and Paavel, B. (2017). Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea. Remote Sens., 9.","DOI":"10.3390\/rs9101070"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kyryliuk, D., and Kratzer, S. (2019). Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea. Sensors, 19.","DOI":"10.3390\/s19163609"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:20:24Z","timestamp":1760160024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,6]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040576"],"URL":"https:\/\/doi.org\/10.3390\/rs13040576","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,6]]}}}