{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:31:50Z","timestamp":1774629110291,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"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":["42330515"],"award-info":[{"award-number":["42330515"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GZC20230917"],"award-info":[{"award-number":["GZC20230917"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024M751060"],"award-info":[{"award-number":["2024M751060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["30106240003"],"award-info":[{"award-number":["30106240003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["42330515"],"award-info":[{"award-number":["42330515"]}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZC20230917"],"award-info":[{"award-number":["GZC20230917"]}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["2024M751060"],"award-info":[{"award-number":["2024M751060"]}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["30106240003"],"award-info":[{"award-number":["30106240003"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["42330515"],"award-info":[{"award-number":["42330515"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["GZC20230917"],"award-info":[{"award-number":["GZC20230917"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M751060"],"award-info":[{"award-number":["2024M751060"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["30106240003"],"award-info":[{"award-number":["30106240003"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005234","name":"Central China Normal University","doi-asserted-by":"publisher","award":["42330515"],"award-info":[{"award-number":["42330515"]}],"id":[{"id":"10.13039\/501100005234","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005234","name":"Central China Normal University","doi-asserted-by":"publisher","award":["GZC20230917"],"award-info":[{"award-number":["GZC20230917"]}],"id":[{"id":"10.13039\/501100005234","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005234","name":"Central China Normal University","doi-asserted-by":"publisher","award":["2024M751060"],"award-info":[{"award-number":["2024M751060"]}],"id":[{"id":"10.13039\/501100005234","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005234","name":"Central China Normal University","doi-asserted-by":"publisher","award":["30106240003"],"award-info":[{"award-number":["30106240003"]}],"id":[{"id":"10.13039\/501100005234","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The routine monitoring of eutrophication is an important measure for observing the variation in water quality and protecting the ecological health of lakes. However, in situ information reflects eutrophication levels within a limited distance and period. In this study, we retrieved the trophic level index (TLI) based on Landsat 8 remote sensing images and using a machine learning (ML) method in Liangzi Lake in Hubei Province, China. The results showed that random forest (RF) outperformed other ML algorithms in estimating the TLI, evaluated by its higher fitness through the Monte Carlo method (median values of R2, RMSE, and MAE are 0.54, 0.047, and 0.037, respectively). In general, 8% of the areas of Liangzi Lake presented an increasing eutrophication level from 2014 to 2022, and 20.1% of the areas reached a mild eutrophication level in 2022. In addition, we found that temperature and anthropogenic activities may impact the eutrophication conditions of the lake. This work uses remote sensing imagery and a ML method to monitor the dynamics of the lake\u2019s eutrophication status, thereby providing a valuable reference for pollution control measures and enhancing the efficiency of water resource management.<\/jats:p>","DOI":"10.3390\/rs16224192","type":"journal-article","created":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T11:34:11Z","timestamp":1731324851000},"page":"4192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spatiotemporal Dynamic Analysis of Eutrophication Status Based on Machine Learning-Based Retrieval Algorithm: Case Study in Liangzi Lake, Hubei, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Peifeng","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Fanghua","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Hanjiang","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province\/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2021EF002289","DOI":"10.1029\/2021EF002289","article-title":"Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects","volume":"10","author":"Chen","year":"2022","journal-title":"Earths Future"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115701","DOI":"10.1016\/j.envpol.2020.115701","article-title":"Spatial Patterning of Chlorophyll a and Water-Quality Measurements for Determining Environmental Thresholds for Local Eutrophication in the Nakdong River Basin","volume":"268","author":"Kim","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.envpol.2018.11.058","article-title":"Quantifying the Trophic Status of Lakes Using Total Light Absorption of Optically Active Components","volume":"245","author":"Wen","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1016\/j.watres.2010.09.018","article-title":"Controlling Harmful Cyanobacterial Blooms in a Hyper-Eutrophic Lake (Lake Taihu, China): The Need for a Dual Nutrient (N & P) Management Strategy","volume":"45","author":"Paerl","year":"2011","journal-title":"Water Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"139013","DOI":"10.1016\/j.jclepro.2023.139013","article-title":"Landscape Patterns Are the Main Regulator of Pond Water Chlorophyll \u03b1 Concentrations in Subtropical Agricultural Catchments of China","volume":"425","author":"Xiao","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165963","DOI":"10.1016\/j.scitotenv.2023.165963","article-title":"Multi-Sensor and Multi-Platform Retrieval of Water Chlorophyll a Concentration in Karst Wetlands Using Transfer Learning Frameworks with ASD, UAV, and Planet CubeSate Reflectance Data","volume":"901","author":"Fu","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.jes.2021.06.003","article-title":"Evidence on the Causes of the Rising Levels of CODMn along the Middle Route of the South-to-North Diversion Project in China: The Role of Algal Dissolved Organic Matter","volume":"113","author":"Wang","year":"2022","journal-title":"J. Environ. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"S49","DOI":"10.1002\/lno.11095","article-title":"Eutrophication Drives Divergent Water Clarity Responses to Decadal Variation in Lake Level","volume":"64","author":"Lisi","year":"2019","journal-title":"Limnol. Oceanogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1016\/j.scitotenv.2018.09.137","article-title":"Assessment of Eutrophication and Water Quality in the Estuarine Area of Lake Wuli, Lake Taihu, China","volume":"650","author":"Wang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114380","DOI":"10.1016\/j.envres.2022.114380","article-title":"Spatiotemporal Dynamics and Anthropologically Dominated Drivers of Chlorophyll-a, TN and TP Concentrations in the Pearl River Estuary Based on Retrieval Algorithm and Random Forest Regression","volume":"215","author":"Yuan","year":"2022","journal-title":"Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0167-5648(08)70781-0","article-title":"Uncertainty in Water Quality Data","volume":"Volume 27","author":"Kwiatkowski","year":"1986","journal-title":"Developments in Water Science"},{"key":"ref_12","first-page":"103026","article-title":"Trophic State Assessment of Optically Diverse Lakes Using Sentinel-3-Derived Trophic Level Index","volume":"114","author":"Liu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117618","DOI":"10.1016\/j.watres.2021.117618","article-title":"Retrieval of Water Quality Parameters from Hyperspectral Images Using a Hybrid Feedback Deep Factorization Machine Model","volume":"204","author":"Zhang","year":"2021","journal-title":"Water Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2019.03.002","article-title":"Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 Using Machine Learning Methods Trained with Radiative Transfer Simulations","volume":"225","author":"Wolanin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111562","DOI":"10.1016\/j.rse.2019.111562","article-title":"Application of Sentinel 3 OLCI for Chl-a Retrieval over Small Inland Water Targets: Successes and Challenges","volume":"237","author":"Kravitz","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1109\/TGRS.2009.2015658","article-title":"Validation of a Quasi-Analytical Algorithm for Highly Turbid Eutrophic Water of Meiliang Bay in Taihu Lake, China","volume":"47","author":"Le","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dallosch, M.A., and Creed, I.F. (2021). Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types. Remote Sens., 13.","DOI":"10.3390\/rs13224607"},{"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","first-page":"41","DOI":"10.1016\/j.isprsjprs.2013.11.016","article-title":"Estimation of Higher Chlorophylla Concentrations Using Field Spectral Measurement and HJ-1A Hyperspectral Satellite Data in Dianshan Lake, China","volume":"88","author":"Zhou","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"122546","DOI":"10.1016\/j.watres.2024.122546","article-title":"Application of Remote Sensing Technology in Water Quality Monitoring: From Traditional Approaches to Artificial Intelligence","volume":"267","author":"Sun","year":"2024","journal-title":"Water Res."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2019.05.001","article-title":"Effects of Broad Bandwidth on the Remote Sensing of Inland Waters: Implications for High Spatial Resolution Satellite Data Applications","volume":"153","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Hafeez, S., Wong, M.S., Ho, H.C., Nazeer, M., Nichol, J., 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_25","doi-asserted-by":"crossref","first-page":"124084","DOI":"10.1016\/j.jhydrol.2019.124084","article-title":"Machine Learning Methods for Better Water Quality Prediction","volume":"578","author":"Ahmed","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109898","DOI":"10.1016\/j.ecolind.2023.109898","article-title":"Trophic Status Observations for Honghu Lake in China from 2000 to 2021 Using Landsat Satellites","volume":"146","author":"Yang","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhou, Y., He, B., Xiao, F., Feng, Q., Kou, J., and Liu, H. (2019). Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China. Remote Sens., 11.","DOI":"10.3390\/rs11040457"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1071\/EN14135","article-title":"Comparative Characterisation of Two Fulvic Acids from East Lake and Liangzi Lake in Central China","volume":"12","author":"Wang","year":"2015","journal-title":"Environ. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13603","DOI":"10.1038\/ncomms13603","article-title":"Estimating the Volume and Age of Water Stored in Global Lakes Using a Geo-Statistical Approach","volume":"7","author":"Messager","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105605","DOI":"10.1016\/j.catena.2021.105605","article-title":"Revealing Anthropogenic Effects on Lakes and Wetlands: Pollen-Based Environmental Changes of Liangzi Lake, China over the Last 150 Years","volume":"207","author":"Ge","year":"2021","journal-title":"CATENA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"361","DOI":"10.4319\/lo.1977.22.2.0361","article-title":"A Trophic State Index for Lakes 1","volume":"22","author":"Carlson","year":"1977","journal-title":"Limnol. Oceanogr."},{"key":"ref_33","unstructured":"(2023, November 29). Estimation of Secchi Depth from Turbidity Data in the Willamette River at Portland, OR, Available online: https:\/\/or.water.usgs.gov\/will_morrison\/secchi_depth_model.html."},{"key":"ref_34","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 Landsat-8\/Sentinel-2 Virtual Constellation and Cloud Computing","volume":"57","author":"Peterson","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing Leaf Pigment Content and Activity with a Reflectometer","volume":"143","author":"Gamon","year":"1999","journal-title":"New Phytol."},{"key":"ref_36","unstructured":"Hewson, R.D., Cudahy, T.J., and Huntington, J.F. (2001, January 9\u201313). Geologic and Alteration Mapping at Mt Fitton, South Australia, Using ASTER Satellite-Borne Data. Proceedings of the IGARSS 2001\u2014Scanning the Present and Resolving the Future, Sydney, Australia."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TGRS.2011.2163199","article-title":"Estimation of Chlorophyll a Concentration Using NIR\/Red Bands of MERIS and Classification Procedure in Inland Turbid Water","volume":"50","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel Algorithms for Remote Estimation of Vegetation Fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/0034-4257(79)90004-X","article-title":"Monitoring Corn and Soybean Crop Development with Hand-Held Radiometer Spectral Data","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A Review of Vegetation Indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.biombioe.2011.02.028","article-title":"A Review of Remote Sensing Methods for Biomass Feedstock Production","volume":"35","author":"Ahamed","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431160210163074","article-title":"Vegetation Indices Derived from High-Resolution Airborne Videography for Precision Crop Management","volume":"24","author":"Metternicht","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.2135\/cropsci2007.01.0031","article-title":"Relationships between Blue- and Red-Based Vegetation Indices and Leaf Area and Yield of Alfalfa","volume":"47","author":"Hancock","year":"2007","journal-title":"Crop Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","article-title":"New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice","volume":"14","author":"Wang","year":"2007","journal-title":"Rice Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.isprsjprs.2011.08.001","article-title":"An Investigation into Robust Spectral Indices for Leaf Chlorophyll Estimation","volume":"66","author":"Main","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Anderson, J.A. (1995). An Introduction to Neural Networks, MIT Press.","DOI":"10.7551\/mitpress\/3905.001.0001"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support Vector Machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_50","unstructured":"Freitas, J., Ribeiro, J., Baldewijns, D., Oliveira, S., and Braga, D. (2018). Machine Learning Powered Data Platform for High-Quality Speech and NLP Workflows. Proc. Interspeech., 1962\u20131963."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"169","DOI":"10.14257\/ijdta.2015.8.1.18","article-title":"A Comprehensive Survey on Support Vector Machine in Data Mining Tasks: Applications & Challenges","volume":"8","author":"Nayak","year":"2015","journal-title":"Int. J. Database Theory Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"137374","DOI":"10.1016\/j.scitotenv.2020.137374","article-title":"Monitoring Dissolved Organic Carbon by Combining Landsat-8 and Sentinel-2 Satellites: Case Study in Saginaw River Estuary, Lake Huron","volume":"718","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_53","unstructured":"(2017). Copernicus Climate Change Service (C3S) ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate. Copernic. Clim. Chang. Serv. Clim. Data Store CDS, 15, 2020."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.rser.2015.05.024","article-title":"Effect of Temperature and Light on the Growth of Algae Species: A Review","volume":"50","author":"Singh","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.scitotenv.2016.03.127","article-title":"Effect of Variable Annual Precipitation and Nutrient Input on Nitrogen and Phosphorus Transport from Two Midwestern Agricultural Watersheds","volume":"559","author":"Kalkhoff","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0269-7491(99)00091-3","article-title":"Eutrophication: Impacts of Excess Nutrient Inputs on Freshwater, Marine, and Terrestrial Ecosystems","volume":"100","author":"Smith","year":"1999","journal-title":"Environ. Pollut."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Akinnawo, S.O. (2023). Eutrophication: Causes, Consequences, Physical, Chemical and Biological Techniques for Mitigation Strategies. Environ. Chall., 100733.","DOI":"10.1016\/j.envc.2023.100733"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.2166\/wst.2020.254","article-title":"Analysis of Eutrophication Potential of Municipal Wastewater","volume":"81","author":"Preisner","year":"2020","journal-title":"Water Sci. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"The 30m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, X., Jiang, Y., Deng, X., Zheng, Y., and Yue, Z. (2020). Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987\u20132018) of Donghu Lake in Wuhan Using Landsat Images. Water, 12.","DOI":"10.3390\/w12082192"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s12524-018-0891-y","article-title":"Ship Classification in SAR Images Using a New Hybrid CNN\u2013MLP Classifier","volume":"47","author":"Sharifzadeh","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6208","DOI":"10.1007\/s11356-014-3806-7","article-title":"Prediction of water quality index in constructed wetlands using support vector machine","volume":"22","author":"Mohammadpour","year":"2015","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:29:50Z","timestamp":1760113790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":64,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224192"],"URL":"https:\/\/doi.org\/10.3390\/rs16224192","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,11]]}}}