{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T13:24:49Z","timestamp":1775741089356,"version":"3.50.1"},"reference-count":133,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Estonian Research Council","award":["PRG302"],"award-info":[{"award-number":["PRG302"]}]},{"name":"Estonian Research Council","award":["PRG709"],"award-info":[{"award-number":["PRG709"]}]},{"name":"Estonian Research Council","award":["PRG1764"],"award-info":[{"award-number":["PRG1764"]}]},{"name":"Estonian Research Council","award":["MOBTP106"],"award-info":[{"award-number":["MOBTP106"]}]},{"name":"European Regional Developing Fund","award":["PRG302"],"award-info":[{"award-number":["PRG302"]}]},{"name":"European Regional Developing Fund","award":["PRG709"],"award-info":[{"award-number":["PRG709"]}]},{"name":"European Regional Developing Fund","award":["PRG1764"],"award-info":[{"award-number":["PRG1764"]}]},{"name":"European Regional Developing Fund","award":["MOBTP106"],"award-info":[{"award-number":["MOBTP106"]}]},{"name":"program Mobilitas Pluss","award":["PRG302"],"award-info":[{"award-number":["PRG302"]}]},{"name":"program Mobilitas Pluss","award":["PRG709"],"award-info":[{"award-number":["PRG709"]}]},{"name":"program Mobilitas Pluss","award":["PRG1764"],"award-info":[{"award-number":["PRG1764"]}]},{"name":"program Mobilitas Pluss","award":["MOBTP106"],"award-info":[{"award-number":["MOBTP106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.<\/jats:p>","DOI":"10.3390\/rs16030464","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T06:54:12Z","timestamp":1706165652000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-2459","authenticated-orcid":false,"given":"Kaire","family":"Toming","sequence":"first","affiliation":[{"name":"Estonian Marine Institute, University of Tartu, M\u00e4ealuse 14, EE-12618 Tallinn, Estonia"},{"name":"Chair of Hydrobiology and Fishery, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Forestry, Mississippi State University, Starkville, MS 39762, USA"}]},{"given":"Tuuli","family":"Soomets","sequence":"additional","affiliation":[{"name":"Estonian Marine Institute, University of Tartu, M\u00e4ealuse 14, EE-12618 Tallinn, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0782-6740","authenticated-orcid":false,"given":"Evelyn","family":"Uuemaa","sequence":"additional","affiliation":[{"name":"Institute of Ecology and Earth Sciences, University of Tartu, Vanemuise 46, EE-51014 Tartu, Estonia"}]},{"given":"Tiina","family":"N\u00f5ges","sequence":"additional","affiliation":[{"name":"Chair of Hydrobiology and Fishery, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, EE-51006 Tartu, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9679-1422","authenticated-orcid":false,"given":"Tiit","family":"Kutser","sequence":"additional","affiliation":[{"name":"Estonian Marine Institute, University of Tartu, M\u00e4ealuse 14, EE-12618 Tallinn, Estonia"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6396","DOI":"10.1002\/2014GL060641","article-title":"A Global Inventory of Lakes Based on High-Resolution Satellite Imagery","volume":"41","author":"Verpoorter","year":"2014","journal-title":"Geophys. 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