{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T09:47:04Z","timestamp":1791884824144,"version":"4.3.4"},"reference-count":74,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Reliable monitoring of coastal water quality requires the integration of sensors that differ in physical observation domain, spatiotemporal support, and uncertainty structure. We propose an observation-domain-aware multisensor fusion architecture in which CTD\/DO\/H2S\/Eh profiling, laboratory measurements, Sentinel-2 MSI, Sentinel-3 OLCI\/SLSTR, Landsat, and hydrometeorological context are combined only after preserving sensor identity, QA\/QC status, and the independent physical observation unit. The experimental field dataset comprises four seasonal campaigns, 32 station\u2013campaign groups, 336 depth records, 111 water samples, 1110 laboratory determinations, 16 satellite scenes, and 64 MSI\/OLCI\u2013in situ matchups. MSI\/OLCI products showed strong agreement with surface chlorophyll-a, turbidity, and TSM measurements (\u03c1 = 0.906\u20130.991). For hypoxia, nested leave-one-campaign-out (LOCO) validation yielded ROC-AUC\/Brier scores of 0.860\/0.157 for full fusion, 0.705\/0.228 for optical-only sensing, and 0.899\/0.045 for the geometry\/context baseline, demonstrating non-monotonic fusion gain. A row-wise split that violated group independence produced an optimistically inflated ROC-AUC of 1.000. These results show that an intelligent sensing system should enforce endpoint-specific sensor compatibility, preserve censoring and uncertainty information, and be evaluated using physically independent observation units. Because the same sectors were revisited, the hypoxia metrics represent cross-campaign transfer within one transect, not geometry-independent within-sector change detection.<\/jats:p>","DOI":"10.3390\/s26196228","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:36:31Z","timestamp":1790786191000},"page":"6228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Observation-Domain-Aware Multisensor Fusion for Intelligent Coastal Water-Quality Monitoring"],"prefix":"10.3390","volume":"26","author":[{"given":"Sergey I.","family":"Kondratyev","sequence":"first","affiliation":[{"name":"Rector\u2019s Office, Admiral Ushakov Maritime State University, 93 Lenin Ave., Novorossiysk 353918, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Evgeniy V.","family":"Khekert","sequence":"additional","affiliation":[{"name":"Rector\u2019s Office, Admiral Ushakov Maritime State University, 93 Lenin Ave., Novorossiysk 353918, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0620-9561","authenticated-orcid":false,"given":"Nikita V.","family":"Martyushev","sequence":"additional","affiliation":[{"name":"Department of Information Technologies, Tomsk Polytechnic University, Tomsk 634050, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-9556","authenticated-orcid":false,"given":"Boris V.","family":"Malozyomov","sequence":"additional","affiliation":[{"name":"Department of Electrotechnical Complexes, Novosibirsk State Technical University, 20 Karl Marx Avenue, Novosibirsk 630073, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Vladislav V.","family":"Kukartsev","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Siberian Federal University, Krasnoyarsk 660041, Russia"},{"name":"Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, Moscow 105005, Russia"},{"name":"Department of Applied Computer Science, Russian State Agrarian University\u2014Moscow Timiryazev Agricultural Academy, Moscow 127434, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8963-7830","authenticated-orcid":false,"given":"Yadviga Aleksandrovna","family":"Tynchenko","sequence":"additional","affiliation":[{"name":"Department of Technological Machines and Equipment for the Oil and Gas Complex, Siberian Federal University, Krasnoyarsk 660041, Russia"},{"name":"Center for Continuing Education, Bauman Moscow State Technical University, Moscow 105005, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Natalya Vladimirovna","family":"Fedorova","sequence":"additional","affiliation":[{"name":"Center for Continuing Education, Bauman Moscow State Technical University, Moscow 105005, Russia"},{"name":"Department of Advertising and Socio-Cultural Activities, Siberian Federal University, Krasnoyarsk 660041, 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