Low-cost sensors and multitemporal remote sensing for operational turbidity monitoring in an East African wetland environment

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cg.contributor.affiliationRuhr University Bochumen_US
cg.contributor.affiliationUniversity of Twenteen_US
cg.contributor.affiliationDedan Kimathi University of Technologyen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.donorFederal Ministry of Education and Research, Germanyen_US
cg.contributor.donorRuhr University Bochumen_US
cg.contributor.initiativeAquatic Foodsen_US
cg.coverage.regionEastern Africaen_US
cg.creator.identifierSander J. Zwart: 0000-0002-5091-1801en_US
cg.identifier.doihttps://doi.org/10.1109/jstars.2024.3381756en_US
cg.identifier.iwmilibraryH053348en_US
cg.isijournalISI Journalen_US
cg.issn2151-1535en_US
cg.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensingen_US
cg.reviewStatusPeer Reviewen_US
cg.volume17en_US
dc.contributor.authorSteinbach, S.en_US
dc.contributor.authorRienow, A.en_US
dc.contributor.authorChege, M. W.en_US
dc.contributor.authorDedring, N.en_US
dc.contributor.authorKipkemboi, W.en_US
dc.contributor.authorThiong’o, B. K.en_US
dc.contributor.authorZwart, Sander Jaapen_US
dc.contributor.authorNelson, A.en_US
dc.date.accessioned2024-12-31T22:34:45Zen_US
dc.date.available2024-12-31T22:34:45Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/168457en_US
dc.titleLow-cost sensors and multitemporal remote sensing for operational turbidity monitoring in an East African wetland environmenten_US
dcterms.abstractMany wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R2 of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R2 =0.83) and with low-cost measurements (R2 = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-03-27en_US
dcterms.bibliographicCitationSteinbach, S.; Rienow, A.; Chege, M. W.; Dedring, N.; Kipkemboi, W.; Thiong’o, B. K.; Zwart, Sander Jaap; Nelson, A. 2024. Low-cost sensors and multitemporal remote sensing for operational turbidity monitoring in an East African wetland environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17:8490-8508. [doi: https://doi.org/10.1109/JSTARS.2024.3381756]en_US
dcterms.extent8490-8508en_US
dcterms.issued2024-03en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherIEEEen_US
dcterms.subjectwetlandsen_US
dcterms.subjectturbidityen_US
dcterms.subjectmonitoringen_US
dcterms.subjectremote sensingen_US
dcterms.subjectwater qualityen_US
dcterms.subjectagricultural water managementen_US
dcterms.subjectsatellite observationen_US
dcterms.typeJournal Articleen_US

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