Multi-Sensor Data Synergy Advisor

Advances in analysis tools and data standards are making it easier to combine remote sensing data from multiple sources. However, as the mechanics become easier, the risk of scientifically naive merging and fusion increases. Subtle differences in the data provenance, such as instrument characteristics, sampling patterns and processing algorithms (among others), can produce significant systematic differences. These differences can vary with spatial location (e.g., latitude), surface type (e.g. land vs. ocean), or local time of day of the measurements. Also, systematic differences can arise from differences in cloud screening, calibration methods and model assumptions in the processing algorithms. These differences, if not recognized and accounted for, cast doubt on the validity and usefulness of data intercomparisons, merging and fusion. The Multi- sensor Data Synergy Advisor (MDSA) is designed to characterize the differences between two datasets and advise a user (human or machine) on the advisability of combining them, as well as potential sources of bias to account for. The MDSA is driven by an ontology of the sensors, datasets and processing algorithms. The ontology is used to populate a provenance for each dataset, allowing a provenance comparison of the two and highlighting where they differ.