Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations

Satellite constellations often launch platforms over several years, increasing observational frequency and capturing additional, potentially more extreme, events. Consequently, reported changes in satellite-derived data may inadvertently capture variations in observational frequency rather than true environmental trends. This study used the Sentinel-3 Cyanobacteria Index (CI-cyano) to assess impacts of varying observational frequency on data distributions and trends. Daily CI-cyano was temporally aggregated into weekly composites using maximum, mean, and median values as both continuous and ordinal observations. Sentinel-3A, Sentinel-3B, and combined Sentinel-3A & -3B were compared using the Wilcoxon signed-rank test. For continuous observations, temporally aggregating via the maximum value showed a large 9% increase for combined Sentinel-3A & -3B versus Sentinel-3A or Sentinel-3B individually, compared to a small 1% decrease for temporal aggregation via the mean and negligible differences via the median. For ordinal observations, temporal aggregation via the maximum and mean showed large increases of up to 25% for combined Sentinel-3A & -3B, while the median showed small decreases of up to 5%. The seasonal Mann-Kendall trend test was then applied to Sentinel-3 imagery from 2016 to 2023, with and without observations from Sentinel-3B. Temporal aggregation via the maximum showed a moderate 20% increase with Sentinel-3B compared to a small 8% increase without Sentinel-3B; mean and median showed negligible trends. An abbreviated assessment using Sentinel-2 had similar results, with large increases for combined Sentinel-2A & -2B via the maximum, but small and moderate decreases via mean and median. Results suggest that temporal aggregation impacts multi-platform datasets. For more consistent summaries, continuous datasets should be temporally aggregated using the mean or median, and ordinal datasets using the median. Results are applicable to any satellite-derived water quality datasets with varied observational frequency. This study addresses a critical gap in the remote sensing community, ensuring relevant statistical concepts are appropriately applied in multi-platform analyses.