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Abstract: Multi-Sensor Data Fusion and Challenges of Merging Earth Observations

2006 AGU Fall Meeting, December 11-15,San Francisco, CA

Multi-Sensor Data Fusion and Challenges of Merging Earth Observations

Mohan Nirala, Gregory Leptoukh, Arun Gopalan, Viktor Zubko, and William Teng

Data fusion is a process of merging spatial and temporal data sets obtained from multiple sensors in order to improve and achieve optimal results in data interpretation, resolution and coverage. Taking into account the different natures of sensor attributes and geometry, we employ the acquisition, masking, binning, filtering, integration, weighting, gridding, correlating, and synthesizing of usable data from diverse sources for the purposes of improving data quality and their utility. A combination of data from multiple sources provides enhanced information, better quality and increased coverage over those which are available from any individual source. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives numerous data products for various sensors in different formats and structures and in multi-temporal and spatial scales for atmosphere, ocean, and land. In our investigation, we use the archived atmospheric data sets from multiple instruments with either the standard Level 3 products (variables mapped on uniform space-time grid scale), or with products such as L2G (gridded geophysical variable derived from the original satellite swath data) that are between L2 and L3. We are developing an optimal technique to merge the atmospheric products (ozone, aerosol, etc.) and will integrate this technique into interactive, online, analysis tools for the user community. The inter-comparisons among different original and fused products are illustrated in several plots and images demonstrating improved spatial coverage and quality in the merged products.

Session Information
Date / TimeSessionLocation
Tuesday, 12/12/06, @ 13:40A21F-0921MCW Level 2

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