Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning

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cg.contributor.affiliationUniversity of Twenteen
cg.contributor.affiliationRuhr University Bochumen
cg.contributor.affiliationDedan Kimathi University of Technologyen
cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.donorFederal Ministry of Education and Research, Germanyen
cg.contributor.initiativeAquatic Foodsen
cg.coverage.countryKenyaen
cg.coverage.iso3166-alpha2KEen
cg.creator.identifierSander J. Zwart: 0000-0002-5091-1801en
cg.identifier.doihttps://doi.org/10.1016/j.jag.2025.104390en
cg.identifier.iwmilibraryH053566en
cg.isijournalISI Journalen
cg.issn1569-8432en
cg.journalInternational Journal of Applied Earth Observation and Geoinformationen
cg.reviewStatusPeer Reviewen
cg.volume136en
dc.contributor.authorSteinbach, S.en
dc.contributor.authorBartels, A.en
dc.contributor.authorRienow, A.en
dc.contributor.authorKuria, B. T.en
dc.contributor.authorZwart, Sander Jaapen
dc.contributor.authorNelson, A.en
dc.date.accessioned2025-02-16T10:10:28Zen
dc.date.available2025-02-16T10:10:28Zen
dc.identifier.urihttps://hdl.handle.net/10568/173072
dc.titlePredicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learningen
dcterms.abstractSmall reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that shortand longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management.en
dcterms.accessRightsOpen Accessen
dcterms.available2025-02-06en
dcterms.bibliographicCitationSteinbach, S.; Bartels, A.; Rienow, A.; Kuria, B. T.; Zwart, Sander Jaap; Nelson, A. 2025. Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning. International Journal of Applied Earth Observation and Geoinformation, 136:104390. [doi: https://doi.org/10.1016/j.jag.2025.104390]en
dcterms.extent104390en
dcterms.issued2025-02en
dcterms.languageenen
dcterms.licenseCC-BY-4.0en
dcterms.publisherElsevieren
dcterms.subjectturbidityen
dcterms.subjectpredictionen
dcterms.subjectwater reservoirsen
dcterms.subjectremote sensingen
dcterms.subjectmachine learningen
dcterms.subjectmodellingen
dcterms.subjectwater qualityen
dcterms.subjectagricultural water managementen
dcterms.subjectsatellite observationen
dcterms.typeJournal Articleen

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