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Recommendations for temporal aggregation of water quality data from multi-platform satellite constellations
Megan M Coffer1,2, Blake A Schaeffer3, Wilson B Salls3
1NOAA, National Environmental Satellite, Data, and Information Services, Center for Satellite Applications and Research College Park, MD, USA.
International Journal of Remote Sensing
|February 25, 2026
Summary
Temporal aggregation methods significantly impact satellite data trends. Using mean or median values for continuous data, and median for ordinal data, ensures more consistent environmental trend analysis from satellite constellations.
Area of Science:
- Remote Sensing
- Environmental Science
- Data Science
Background:
- Satellite constellations increase observational frequency, potentially introducing artificial trends in derived data.
- Varying observational frequency can obscure true environmental changes, impacting scientific interpretation.
- The Sentinel-3 Cyanobacteria Index (CI-cyano) is used to analyze impacts of data aggregation methods.
Purpose of the Study:
- To assess how different temporal aggregation methods affect satellite-derived data distributions and trends.
- To compare the impacts of aggregation on single-platform versus multi-platform satellite data (Sentinel-3A, Sentinel-3B).
- To provide recommendations for consistent data summarization in multi-platform remote sensing analyses.
Main Methods:
- Temporal aggregation of daily CI-cyano data into weekly composites using maximum, mean, and median values.
- Comparison of continuous and ordinal data aggregation impacts using the Wilcoxon signed-rank test.
- Application of the seasonal Mann-Kendall trend test to Sentinel-3 imagery (2016-2023) with and without Sentinel-3B data.
Main Results:
- Temporal aggregation using the maximum value significantly inflated trends (up to 25%) in multi-platform data compared to mean or median.
- Continuous data aggregated by maximum showed a 9% increase for combined Sentinel-3A & -3B, while mean and median showed minimal changes.
- Ordinal data aggregated by maximum and mean showed large increases (up to 25%), whereas median showed small decreases (up to 5%).
Conclusions:
- Temporal aggregation methods critically influence multi-platform satellite datasets, particularly when using the maximum value.
- For consistent environmental trend analysis, continuous satellite data should be aggregated using the mean or median, and ordinal data using the median.
- These findings are crucial for accurate interpretation of water quality data from satellite constellations and address a gap in remote sensing statistical practices.
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