{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T17:13:16Z","timestamp":1776532396058,"version":"3.51.2"},"reference-count":62,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High Resolution Earth Observation System Major Project Funding","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]},{"name":"High Resolution Earth Observation System Major Project Funding","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"High Resolution Earth Observation System Major Project Funding","award":["42171385"],"award-info":[{"award-number":["42171385"]}]},{"name":"High Resolution Earth Observation System Major Project Funding","award":["2022228"],"award-info":[{"award-number":["2022228"]}]},{"name":"National Key Research and Development Program of China","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]},{"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":["42171385"],"award-info":[{"award-number":["42171385"]}]},{"name":"National Key Research and Development Program of China","award":["2022228"],"award-info":[{"award-number":["2022228"]}]},{"name":"National Natural Science Foundation of China","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"National Natural Science Foundation of China","award":["42171385"],"award-info":[{"award-number":["42171385"]}]},{"name":"National Natural Science Foundation of China","award":["2022228"],"award-info":[{"award-number":["2022228"]}]},{"name":"Youth Innovation Promotion Association of Chinese Academy of Sciences, China","award":["30-Y60B01-9003-22\/23"],"award-info":[{"award-number":["30-Y60B01-9003-22\/23"]}]},{"name":"Youth Innovation Promotion Association of Chinese Academy of Sciences, China","award":["2021YFB3901101"],"award-info":[{"award-number":["2021YFB3901101"]}]},{"name":"Youth Innovation Promotion Association of Chinese Academy of Sciences, China","award":["42171385"],"award-info":[{"award-number":["42171385"]}]},{"name":"Youth Innovation Promotion Association of Chinese Academy of Sciences, China","award":["2022228"],"award-info":[{"award-number":["2022228"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake\u2019s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.<\/jats:p>","DOI":"10.3390\/rs15153809","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T10:00:15Z","timestamp":1690797615000},"page":"3809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Li","family":"Fu","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Geography and Environment, Liaocheng University, Liaocheng 252059, China"}]},{"given":"Yaming","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment of the People\u2019s Republic of China, Beijing 100029, China"}]},{"given":"Ge","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Kaishan","family":"Song","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Geography and Environment, Liaocheng University, Liaocheng 252059, China"}]},{"given":"Hui","family":"Tao","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Fangrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Sijia","family":"Li","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Shuqiong","family":"Shi","sequence":"additional","affiliation":[{"name":"Ministry of Ecology and Environment of China, Beijing 100029, China"}]},{"given":"Yingxin","family":"Shang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.rse.2018.08.026","article-title":"Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index","volume":"217","author":"Wang","year":"2018","journal-title":"Remote Sens. 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