Science Inventory

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

Citation:

Coffer, M., B. Schaeffer, W. Salls, J. Minucci, AND O. Cronin-Golomb. Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations. INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, 47(1):177-199, (2026). https://doi.org/10.1080/01431161.2025.2575515

Impact/Purpose:

This study suggests best practice recommendations for temporal aggregation in multi-platform analyses. For continuous datasets, measures of central tendency, such as either mean or median data values, provide more consistent results than the maximum. For ordinal datasets, the median is more consistent. Following the recommendations presented here will ensure that data distributions and change assessments reflect true environmental change rather than variations in observational frequency, where observational frequency can change following the launch or decommissioning of additional satellite platforms. These recommendations are not relevant for analyses or products with consistent observational frequency over the period of interest. Instead, they are specific for analyses characterized by dynamic observational frequency.

Description:

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.