{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:13:33Z","timestamp":1774952013986,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CRHIAM","award":["ANID\/FONDAP\/15130015"],"award-info":[{"award-number":["ANID\/FONDAP\/15130015"]}]},{"name":"CRHIAM","award":["1221091"],"award-info":[{"award-number":["1221091"]}]},{"name":"Chilean government","award":["ANID\/FONDAP\/15130015"],"award-info":[{"award-number":["ANID\/FONDAP\/15130015"]}]},{"name":"Chilean government","award":["1221091"],"award-info":[{"award-number":["1221091"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (&lt;0.260 (\u03bcg\/L)2), RMSE (&lt;0.510 ug\/L), MaxError (&lt;0.730 \u03bcg\/L), and MAE (&lt;0.442 \u03bcg\/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.<\/jats:p>","DOI":"10.3390\/rs15174157","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"4157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0550-0253","authenticated-orcid":false,"given":"Lien","family":"Rodr\u00edguez-L\u00f3pez","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Arquitectura y Dise\u00f1o, Universidad San Sebasti\u00e1n, Lientur 1457, Concepci\u00f3n 4030000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6431-9203","authenticated-orcid":false,"given":"David Bustos","family":"Usta","sequence":"additional","affiliation":[{"name":"Facultad de Oceanograf\u00eda, Universidad de Concepci\u00f3n, Concepci\u00f3n 4030000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3290-4947","authenticated-orcid":false,"given":"Iongel","family":"Duran-Llacer","sequence":"additional","affiliation":[{"name":"H\u00e9mera Centro de Observaci\u00f3n de la Tierra, Facultad de Ciencias, Ingenier\u00eda y Tecnolog\u00eda, Universidad Mayor, Camino La Pir\u00e1mide 5750, Huechuraba 8580745, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3588-6115","authenticated-orcid":false,"given":"Lisandra Bravo","family":"Alvarez","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universidad de Concepci\u00f3n, Edmundo Larenas 219, Concepci\u00f3n 4030000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7879-7094","authenticated-orcid":false,"given":"Santiago","family":"Y\u00e9pez","sequence":"additional","affiliation":[{"name":"Department of Forest Management and Environment, Faculty of Forestry, Universidad de Concepcion, Calle Victoria, Concepci\u00f3n 4030000, Chile"}]},{"given":"Luc","family":"Bourrel","sequence":"additional","affiliation":[{"name":"G\u00e9osciences Environnement Toulouse, UMR 5563, Universit\u00e9 de Toulouse, CNRS-IRD-OMP-CNES, 31000 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-8274","authenticated-orcid":false,"given":"Frederic","family":"Frappart","sequence":"additional","affiliation":[{"name":"INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Universit\u00e9 de Bordeaux, 33604 Talence, France"}]},{"given":"Roberto","family":"Urrutia","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Ambientales, Universidad de Concepci\u00f3n, Concepci\u00f3n 4030000, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14233","DOI":"10.1007\/s11356-020-12081-4","article-title":"Review of Characterization, Factors, Impacts, and Solutions of Lake Eutrophication: Lesson for Lake Tana, Ethiopia","volume":"28","author":"Atlabachew","year":"2021","journal-title":"Environ. 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