An integrated view of data quality in Earth observation

Area of Science:

• Data Science
• Earth Observation (EO)

Background:

• Data quality is complex and inconsistently defined across communities, leading to harmonization challenges.
• Existing data quality standards often fail to meet diverse user needs or encompass all quality concepts.
• This ambiguity hinders effective data integration and scientific research.

Purpose of the Study:

• To address the philosophical and practical challenges of data quality management.
• To identify user needs and evaluate current data quality standards.
• To propose an integrated data quality model and application mechanism for Earth observation (EO) data.

Main Methods:

• Philosophical discussion of data quality concepts.
• Identification of user requirements for data quality.
• Review of existing data quality standards and specifications.
• Development of an integrated data quality model for EO.
• Proposal of a metadata inheritance mechanism for applying the model.

Main Results:

• A comprehensive understanding of data quality issues and user needs.
• An integrated model for data quality in Earth observation (EO).
• A practical mechanism for applying quality information via metadata inheritance.
• Demonstrated applicability of the approach to EO datasets.

Conclusions:

• The proposed integrated model and metadata inheritance mechanism enhance data quality management in EO.
• The methodologies can be extended to other scientific domains to support quality-enabled research.
• Improved data quality facilitates better scientific discovery and collaboration.