{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T17:48:28Z","timestamp":1775929708221,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council of Humanities, Sciences and Technologies (CONAHCYT)\u2014Mexico"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing has emerged as a promising tool for monitoring water quality (WQ) in aquatic ecosystems. This study evaluates the effectiveness of remote sensing in assessing WQ parameters in Cajititl\u00e1n and Zapotl\u00e1n lakes in the state of Jalisco, Mexico. Over time, these lakes have witnessed a significant decline in WQ, necessitating the adoption of advanced monitoring techniques. In this research, satellite-based remote sensing data were combined with ground-based measurements from the National Water Quality Monitoring Network of Mexico (RNMCA). These data sources were harnessed to train and evaluate the performance of six distinct categories of machine learning (ML) algorithms aimed at estimating WQ parameters with active spectral signals, including chlorophyll-a (Chl-a), turbidity, and total suspended solids (TSS). Various limitations were encountered during the study, primarily due to atmospheric conditions and cloud cover. These challenges affected both the quality and quantity of the data. However, these limitations were overcome through rigorous data preprocessing, the application of ML techniques designed for data-scarce scenarios, and extensive hyperparameter tuning. The superlearner algorithm (SLA), which leverages a combination of individual algorithms, and the multilayer perceptron (MLP), capable of handling complex and non-linear problems, outperformed others in terms of predictive accuracy. Notably, in Lake Cajititl\u00e1n, these models provided the most accurate predictions for turbidity (r2 = 0.82, RMSE = 9.93 NTU, MAE = 7.69 NTU), Chl-a (r2 = 0.60, RMSE = 48.06 mg\/m3, MAE = 37.98 mg\/m3), and TSS (r2 = 0.68, RMSE = 13.42 mg\/L, MAE = 10.36 mg\/L) when using radiometric data from Landsat-8. In Lake Zapotl\u00e1n, better predictive performance was observed for turbidity (r2 = 0.75, RMSE = 2.05 NTU, MAE = 1.10 NTU) and Chl-a (r2 = 0.71, RMSE = 6.16 mg\/m3, MAE = 4.97 mg\/m3) with Landsat-8 radiometric data, while TSS (r2 = 0.72, RMSE = 2.71 mg\/L, MAE = 2.12 mg\/L) improved when Sentinel-2 data were employed. While r2 values indicate that the models do not exhibit a perfect fit, those approaching unity suggest that the predictor variables offer valuable insights into the corresponding responses. Moreover, the model\u2019s robustness could be enhanced by increasing the quantity and quality of input variables. Consequently, remote sensing emerges as a valuable tool to support the objectives of WQ monitoring systems.<\/jats:p>","DOI":"10.3390\/rs15235505","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T03:35:06Z","timestamp":1701056106000},"page":"5505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Assessment of Machine Learning Models for Remote Sensing of Water Quality in Lakes Cajititl\u00e1n and Zapotl\u00e1n, Jalisco\u2014Mexico"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4413-6623","authenticated-orcid":false,"given":"Freddy Hern\u00e1n","family":"Villota-Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Department of Water and Energy, University of Guadalajara, Campus Tonal\u00e1, Tonal\u00e1 45425, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5758-6140","authenticated-orcid":false,"given":"Belkis","family":"Sulbar\u00e1n-Rangel","sequence":"additional","affiliation":[{"name":"Department of Water and Energy, University of Guadalajara, Campus Tonal\u00e1, Tonal\u00e1 45425, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9081","authenticated-orcid":false,"given":"Florentina","family":"Zurita-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Environmental Quality Research Center, University of Guadalajara, Campus Ci\u00e9nega, Ocotl\u00e1n 47810, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-9018","authenticated-orcid":false,"given":"Kelly Joel","family":"Gurubel-Tun","sequence":"additional","affiliation":[{"name":"Department of Water and Energy, University of Guadalajara, Campus Tonal\u00e1, Tonal\u00e1 45425, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8248-0604","authenticated-orcid":false,"given":"Virgilio","family":"Z\u00fa\u00f1iga-Grajeda","sequence":"additional","affiliation":[{"name":"Information Sciences and Technological Development, University of Guadalajara, Campus Tonal\u00e1, Tonal\u00e1 45425, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. 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