Observation-Domain-Aware Multisensor Fusion for Intelligent Coastal Water-Quality Monitoring

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-campaign groups, 336 depth records, 111 water samples, 1110 laboratory determinations, 16 satellite scenes, and 64 MSI/OLCI-in situ matchups. MSI/OLCI products showed strong agreement with surface chlorophyll-a, turbidity, and TSM measurements (ρ = 0.906-0.991). 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.