{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T01:10:42Z","timestamp":1783041042672,"version":"3.54.6"},"reference-count":57,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42201414"],"award-info":[{"award-number":["42201414"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42101366"],"award-info":[{"award-number":["42101366"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42171374"],"award-info":[{"award-number":["42171374"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}]},{"name":"National Key Research and Development Program of China","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"National Key Research and Development Program of China","award":["42201414"],"award-info":[{"award-number":["42201414"]}]},{"name":"National Key Research and Development Program of China","award":["42101366"],"award-info":[{"award-number":["42101366"]}]},{"name":"National Key Research and Development Program of China","award":["42171374"],"award-info":[{"award-number":["42171374"]}]},{"name":"National Key Research and Development Program of China","award":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}]},{"name":"National Key Research and Development Program of China","award":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["42201414"],"award-info":[{"award-number":["42201414"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["42101366"],"award-info":[{"award-number":["42101366"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["42171374"],"award-info":[{"award-number":["42171374"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}]},{"name":"Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project (CASPLOS-CCSI)","award":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["42201414"],"award-info":[{"award-number":["42201414"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["42101366"],"award-info":[{"award-number":["42101366"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["42171374"],"award-info":[{"award-number":["42171374"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}]},{"name":"National Natural Science Foundation of China Youth Fund","award":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}],"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":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}],"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":["42201414"],"award-info":[{"award-number":["42201414"]}],"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":["42101366"],"award-info":[{"award-number":["42101366"]}],"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":["42171374"],"award-info":[{"award-number":["42171374"]}],"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":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}],"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":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China postdoctoral science foundation","doi-asserted-by":"publisher","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}],"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":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}],"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":["42201414"],"award-info":[{"award-number":["42201414"]}],"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":["42101366"],"award-info":[{"award-number":["42101366"]}],"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":["42171374"],"award-info":[{"award-number":["42171374"]}],"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":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}],"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":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["XDA28100100"],"award-info":[{"award-number":["XDA28100100"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["42201414"],"award-info":[{"award-number":["42201414"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["42101366"],"award-info":[{"award-number":["42101366"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["42171374"],"award-info":[{"award-number":["42171374"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["2020M681056"],"award-info":[{"award-number":["2020M681056"]}]},{"name":"Research instrument and equipment develop ment project of Chinese Academy of Sciences","award":["YJKYYQ20190044"],"award-info":[{"award-number":["YJKYYQ20190044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 \u03bcg\/L, MAE = 0.57 \u03bcg\/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 \u03bcg\/L, MAE = 1.58 \u03bcg\/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.<\/jats:p>","DOI":"10.3390\/rs14205136","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Shiqi","family":"Xu","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4605-0612","authenticated-orcid":false,"given":"Sijia","family":"Li","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8369-4452","authenticated-orcid":false,"given":"Zui","family":"Tao","sequence":"additional","affiliation":[{"name":"Institute of Air and Space Information Innovation, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaishan","family":"Song","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8801-5324","authenticated-orcid":false,"given":"Zhidan","family":"Wen","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Chen","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1126\/science.aay2723","article-title":"Changing nutrients, changing rivers","volume":"365","author":"Peuelas","year":"2019","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E35","DOI":"10.1038\/s41586-021-03254-3","article-title":"Concerns about phytoplankton bloom trends in global lakes","volume":"590","author":"Feng","year":"2021","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1021\/acs.est.0c06480","article-title":"Climatic versus Anthropogenic Controls of Decadal Trends (1983\u20132017) in Algal Blooms in Lakes and Reservoirs across China","volume":"55","author":"Song","year":"2021","journal-title":"Environ. Sci. Technol"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/535349a","article-title":"Study role of climate change in extreme threats to water quality","volume":"535","author":"Michalak","year":"2016","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1007\/BF02804901","article-title":"Harmful algal blooms and eutrophication: Nutrient sources, composition, and consequences","volume":"25","author":"Anderson","year":"2002","journal-title":"Estuaries"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vera-Herrera, L., Romo, S., and Soria, J. (2022). How Agriculture, Connectivity and Water Management Can Affect Water Quality of a Mediterranean Coastal Wetland. Agronomy, 12.","DOI":"10.3390\/agronomy12020486"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153434","DOI":"10.1016\/j.scitotenv.2022.153434","article-title":"Eutrophication decrease compositional dissimilarity in freshwater plankton communities","volume":"821","author":"Li","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111974","DOI":"10.1016\/j.rse.2020.111974","article-title":"A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes","volume":"248","author":"Cao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"361","DOI":"10.4319\/lo.1977.22.2.0361","article-title":"A trophic state index for lakes","volume":"22","author":"Carlson","year":"1977","journal-title":"Limnol. Oceanogr."},{"key":"ref_10","first-page":"937","article-title":"Ocean color chlorophyll algorithms for seawifs","volume":"103","author":"Maritorena","year":"1998","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2018.06.002","article-title":"An optimized Chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters-ScienceDirect","volume":"215","author":"Smith","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","unstructured":"Fang, C. (2020). Water Quality Remote Sensing Inversion and Spatiotemporal Analysis on International Lake\u2014A Case Study of Lake Xingkai. [Ph.D. Thesis, Chinese Academy of Sciences]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"196","DOI":"10.18307\/2011.0206","article-title":"Environmental conditions and the protection counter measures for waters of Lake Xingkai","volume":"23","author":"Piao","year":"2011","journal-title":"Lake Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kang, S., Peng, X.R., Zhang, L., Liu, M., and Zhang, Y. (2009, January 11\u201313). The Assessment of the Present Eutrophication Status and Characteristic Analysis of Xingkai Lake. Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering, Beijing, China.","DOI":"10.1109\/ICBBE.2009.5163579"},{"key":"ref_16","first-page":"32","article-title":"Current Status of Management of Xingkai Lake National","volume":"02","author":"Wang","year":"2011","journal-title":"Wetl. Sci. Manag."},{"key":"ref_17","unstructured":"Vishnu Prasanth, B.R., Sivakumar, R., and Ramaraj, M. (2022, August 27). Springer. Available online: https:\/\/link.springer.com\/article\/10.1007\/s00128-022-03511-9?utm_source=xmol&utm_medium=affiliate&utm_content=meta&utm_campaign=DDCN_1_GL01_metadata."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111619","DOI":"10.1016\/j.rse.2019.111619","article-title":"Remote sensing of shallow waters\u2014A 50 year retrospective and future directions","volume":"240","author":"Kutser","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","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":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3479","DOI":"10.1016\/j.rse.2011.08.011","article-title":"Remote estimation of chl-a concentration in turbid productive waters-return to a simple two-band NIR-red model?","volume":"115","author":"Gurlin","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111648","DOI":"10.1016\/j.rse.2020.111648","article-title":"An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"146271","DOI":"10.1016\/j.scitotenv.2021.146271","article-title":"Quantification of chlorophyll-a in typical lakes across china using Sentinel-2 MSI imagery with machine learning algorithm","volume":"778","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_24","first-page":"50","article-title":"Mass concentration inversion analysis of chlorophyll a in Taihu lake based on GF- 6 satellite data","volume":"49","author":"Pan","year":"2021","journal-title":"J. Hohai Univ."},{"key":"ref_25","first-page":"12","article-title":"Technical characteristics and new mode application of GF-6 satellite","volume":"12","author":"Lu","year":"2020","journal-title":"Spacecr. Eng."},{"key":"ref_26","first-page":"1","article-title":"SeaWiFS postlaunch technical report series, volume 11, SeaWiFS postlaunch calibration and validation analyses","volume":"55","author":"Maritorena","year":"2000","journal-title":"NASA Tech. Memo. SeaWIFS Postlaunch Tech. Rep. Ser."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24109","DOI":"10.1364\/OE.18.024109","article-title":"Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands","volume":"18","author":"Gilerson","year":"2010","journal-title":"Opt. Express"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"5755","DOI":"10.1364\/AO.41.005755","article-title":"Deriving Inherent Optical Properties from Water Color:A Multiband Quasi-Analytical Algorithm for Optically Deep Waters","volume":"41","author":"Lee","year":"2002","journal-title":"Appl. Opt."},{"key":"ref_30","first-page":"473","article-title":"Research Progress in the Retrieval Algorithms for Chlorophyll-a, a Key Element of Water Quality Monitoring by Remote Sensing","volume":"36","author":"Luo","year":"2021","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2022.06.015","article-title":"Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs","volume":"190","author":"Werther","year":"2022","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ecolind.2017.07.033","article-title":"Combining multivariate statistical techniques and random forests model to assess and diagnose the trophic status of Poyang lake in China","volume":"83","author":"Li","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e01321","DOI":"10.1002\/ecs2.1321","article-title":"Modeling lake trophic state: A random forest approach","volume":"7","author":"Hollister","year":"2016","journal-title":"Ecosphere"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"370","DOI":"10.3974\/geodp.2017.03.20","article-title":"Outline data of the Khanka Lake","volume":"1","author":"Chen","year":"2017","journal-title":"J. Glob. Chang. Data Discov."},{"key":"ref_35","first-page":"21","article-title":"Hydrological characteristics of Xingkai Lake","volume":"24","author":"Sun","year":"2006","journal-title":"Water Resour. Hydropower Northeast. China"},{"key":"ref_36","first-page":"79","article-title":"Investigation and Study on Water Quality and Pollution Condition in Lake Xingkai of China","volume":"29","author":"Ji","year":"2013","journal-title":"Environ. Monit. China"},{"key":"ref_37","first-page":"46","article-title":"Analysis of ecological water level of Xingkai Lake","volume":"24","author":"Meng","year":"2008","journal-title":"Water Resour. Prod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0015-3796(17)30778-3","article-title":"New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton","volume":"167","author":"Jeffrey","year":"1975","journal-title":"Biochem. Physiol. Pflanz."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4269","DOI":"10.5194\/hess-17-4269-2013","article-title":"Spatiotemporal characterization of dissolved carbon for inland waters in semi-humid\/semi-arid region, China","volume":"17","author":"Song","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.csr.2015.11.009","article-title":"Estimation of water turbidity and analysis of its spatio-temporal variability in the danube river plume (black sea) using MODIS satellite data","volume":"112","author":"Constantin","year":"2016","journal-title":"Cont. Shelf Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.4319\/lo.1993.38.6.1321","article-title":"Quantifying absorption by aquatic particles: A multiple scattering correction for glass-fiber filters","volume":"38","author":"Cleveland","year":"1993","journal-title":"Limnol. Oceanogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDVI) in the delineation of open water features","volume":"17","author":"Mcfeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/j.envsoft.2003.03.004","article-title":"Environmental data mining and modeling based on machine learning algorithms and geostatistics","volume":"19","author":"Kanevski","year":"2004","journal-title":"Environ. Model. Softw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nazeer, M., Bilal, M., Alsahli, M.M., Shahzad, M.I., and Waqas, A. (2017). Evaluation of empirical and machine learning algorithms for estimation of coastal water quality parameters. ISPRS. Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110360"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/S0034-4257(02)00022-6","article-title":"A procedure for regional lake water clarity assessment using landsat multispectral data","volume":"82","author":"Kloiber","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.biosystemseng.2005.05.001","article-title":"Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy","volume":"91","author":"Saeys","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10329","DOI":"10.1364\/OE.453404","article-title":"Estimation of the lake trophic state index (TSI) using hyperspectral remote sensing in Northeast China","volume":"30","author":"Lyu","year":"2022","journal-title":"Opt. Express"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/ngeo2693","article-title":"Long-term accumulation and transport of anthropogenic phosphorus in three river basins","volume":"9","author":"Powers","year":"2016","journal-title":"Nat. Geosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/s41597-020-00648-2","article-title":"A database of chlorophyll and water chemistry in freshwater lakes","volume":"7","author":"Filazzola","year":"2020","journal-title":"Sci. Data"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lv, J., and Wu, H. (2010, January 18\u201320). The Effects of TN:TP Ratios on the Phytoplankton and Colonial Cyanobacteria in Eutrophic Shallow Lakes. Proceedings of the 2010 4th International Conference on Bioinformatics and Biomedical Engineering, Chengdu, China.","DOI":"10.1109\/ICBBE.2010.5516982"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41583-020-0277-3","article-title":"Backpropagation and the brain","volume":"21","author":"Lillicrap","year":"2020","journal-title":"Nat. Rev. Neuroence"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lawrence, S., and Giles, C.L. (2000, January 27). Overfitting and Neural Networks: Conjugate Gradient and Backpropagation. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy.","DOI":"10.1109\/IJCNN.2000.857823"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2016.03.002","article-title":"Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations","volume":"178","author":"Beck","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"701","DOI":"10.3390\/w13050701","article-title":"Short-Term Response of Chlorophyll a Concentration Due to Intense Wind and Freshwater Peak Episodes in Estuaries: The Case of Fangar Bay (Ebro Delta)","volume":"13","author":"Grifoll","year":"2021","journal-title":"Water"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Jiang, B., Liu, H., Xing, Q., Cai, J., Zheng, X., Li, L., Liu, S., Zheng, Z., Xu, H., and Meng, L. (2021). Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters. Water, 13.","DOI":"10.3390\/w13050650"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:05Z","timestamp":1760144045000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,14]]},"references-count":57,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205136"],"URL":"https:\/\/doi.org\/10.3390\/rs14205136","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,14]]}}}