{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:36:15Z","timestamp":1768836975320,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T00:00:00Z","timestamp":1693267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Special Projects of Jiangxi Province","award":["20213AAG01012"],"award-info":[{"award-number":["20213AAG01012"]}]},{"name":"Key Science and Technology Special Projects of Jiangxi Province","award":["2019YFE0109900"],"award-info":[{"award-number":["2019YFE0109900"]}]},{"name":"Key Science and Technology Special Projects of Jiangxi Province","award":["202003251"],"award-info":[{"award-number":["202003251"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["20213AAG01012"],"award-info":[{"award-number":["20213AAG01012"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE0109900"],"award-info":[{"award-number":["2019YFE0109900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["202003251"],"award-info":[{"award-number":["202003251"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research funding of China Three Gorges Corporation","award":["20213AAG01012"],"award-info":[{"award-number":["20213AAG01012"]}]},{"name":"Research funding of China Three Gorges Corporation","award":["2019YFE0109900"],"award-info":[{"award-number":["2019YFE0109900"]}]},{"name":"Research funding of China Three Gorges Corporation","award":["202003251"],"award-info":[{"award-number":["202003251"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, key meteorological and environmental factors from in situ monitoring were combined with remotely sensed reflectance data and statistical analysis was used to determine the main factors influencing the trophic state. Second, a trophic state index (TSI) inversion model was constructed using a machine learning algorithm, and this was then optimized using the sparrow search algorithm (SSA) based on a backpropagation neural network (BP-NN) to establish an SSA-BP-NN model. Third, a typical lake in China (Hongze Lake) was chosen as the case study. The application results show that, when the key environmental factors (pH, temperature, average wind speed, and sediment content) and the band combination data from Sentinel-2\/MSI were used as input variables, the performance of the model was improved (R2 = 0.936, RMSE = 1.133, MAPE = 1.660%, MAD = 0.604). Compared with the performance prior to optimization (R2 = 0.834, RMSE = 1.790, MAPE = 2.679%, MAD = 1.030), the accuracy of the model was improved by 12.2%. It is worth noting that this framework could accurately identify water bodies in different trophic states. Finally, based on this framework, we mapped the spatial distribution of TSI in Hongze Lake in different seasons from 2019 to 2020 and analyzed its variation characteristics. The framework can combine regional special feature factors influenced by a complex environment with S-2\/MSI data to achieve an assessment accuracy of over 90% for TSI in sensitive waters and has strong applicability and robustness.<\/jats:p>","DOI":"10.3390\/rs15174238","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T08:51:14Z","timestamp":1693299074000},"page":"4238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Integrated Framework for Remote Sensing Assessment of the Trophic State of Large Lakes"],"prefix":"10.3390","volume":"15","author":[{"given":"Dinghua","family":"Meng","sequence":"first","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China"}]},{"given":"Jingqiao","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1212-4345","authenticated-orcid":false,"given":"Weifeng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Lab of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Academy of Sciences, No.18 Suangqing Rd., Beijing 100085, China"}]},{"given":"Shijie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, The University of Hong Kong, Hongkong, China"}]},{"given":"Huan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"903","DOI":"10.4319\/lo.1996.41.5.0903","article-title":"Climate Change Scenarios for Great Lakes Basin Ecosystem Studies","volume":"41","author":"Mortsch","year":"1996","journal-title":"Limnol. 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