Assessing Remote Sensing Data Quality and Alignment with In-Situ Measurements in Ireland: A Cloud Coverage Analysis Using Multi-Dataset Integration

The accurate assessment of water quality (WQ) indicators in transitional and coastal waters (TrC) is crucial for environmental monitoring and management. Remote sensing (RS) technology offers valuable insights into these indicators, but cloud coverage can significantly affect data quality. In this study, we conducted a comprehensive analysis of data quality by integrating four diverse datasets: Sentinel-2 A B MSI, Landsat 8 9 OLI, and Sentinel-3 A B OLCI acquired for four bays in Ireland (e.g., Cork Harbour, Galway Bay, Mulroy Bay, and Dublin Bay). Our focus was on evaluating the impact of cloud coverage on data reliability and its alignment with in-situ measurements. By leveraging these datasets, we assessed the spatial and temporal distribution of cloud coverage, identifying periods of optimal data acquisition. Additionally, we examined the concordance between RS data and in-situ measurements for six optically inactive WQ indicators: dissolve oxygen, pH, biological oxygen demand, total nitrogen, total phosphorus, and dissolve inorganic nitrogen. This alignment analysis enabled us to gauge the suitability of RS data for monitoring different bays WQ. The findings of the research suggest that Sentinel-3 A B OLCI could be effective for estimating WQ in Irish waterbodies during the spring, summer, and autumn seasons. Our findings provide valuable insights into the challenges posed by cloud coverage in RS-based WQ assessment and offer strategies for improving data quality and integration with in-situ measurements. This research contributes to enhancing the reliability of RS technology for environmental monitoring in TrC waters, ultimately supporting effective decision-making and resource management.