Data Quality and Validation White Paper Kick-off, GSFC

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White Paper on Data Quality and Validation Framework for RS data: Best practices and user perspective[edit | edit source]

  1. Objective:

Capture, harmonize and provide useful data quality for RS data

  1. Current Status:
  • No coordinate approach ... besides QA4EO, WGCV and some discipline-specific efforts
  • Duplication of efforts across missions - inefficient utilization of funding
  • Validation:
    • L2: validate in some areas and extrapolate globally. Issues: filtering by QC flag doesn't necessarily lead to good product
    • L3: what is L3 validation?
  • Fitness for purpose: quality need should depend on data usage but the current quality is based on validation that has the specific purpose of validating instrument and retrieval algorithms
  1. Suggested approach:
  • Collect best practices from various communities and known efforts
  • Develop consistent terminology and metrics, e.g., completeness, consistency, representativeness
  • Identify main purpose classes and establish a high-level Q-metrics levels for different purposes