Data fusion system for monitoring water quality: Application to chlorophyll-a in Baltic sea coast. (September 2022)

• Record Type: Journal Article
• Authors: Gunia, M.; Laine, M.; Malve, O.; Kallio, K.; Kervinen, M.; Anttila, S.; Kotamäki, N.; Siivola, E.; Kettunen, J.; Kauranne, T.
• Is Part Of: Environmental modelling & software. Volume 155(2022)
• Publication Date: 2022-09
• Subjects: Water quality -- Coastal -- Data fusion -- Data assimilation -- Spatio-temporal interpolation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105465
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present an operational system for multi-sensor data fusion implemented at the Finnish Environment Institute. The system uses Ensemble Kalman filter and smoother algorithms, which are often used for probabilistic analysis of multi-sensor data. Uncertainty and spatial and temporal correlations present in the available observation data are accounted for to obtain accurate and realistic results. To test the data fusion system, daily chlorophyll-a concentration has been modelled across northern shoreline of Gulf of Finland over the period of August 1st – October 31st 2011. Chlorophyll-a data from routine monitoring stations, ferrybox measurements, and data derived from Medium Resolution Imaging Spectrometer (MERIS) instrument on board the ENVISAT satellite has been used as input. The data fusion system demonstrates the use of existing and well-known Ensemble Kalman filtering and smoothing methods for improving water quality monitoring programs and for ensuring compliance with ecological standards. Highlights: Operational data fusion system for coastal water quality monitoring was implemented. Remote sensing and in-situ data sources are combined using ensemble Kalman smoother. Result uncertainty is quantified to improve future data collection. Simple process model captures relevant dynamics in presence of significant data gaps.