If the vision of GEOSS is to be achieved, Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain from collection and processing to delivery.

A QI should provide sufficient information to allow all users to readily evaluate a product's suitability for their particular application, i.e. its "fitness for purpose". To ensure that this process is internationally harmonised and consistent, the QI needs to be based on a documented and quantifiable assessment of evidence demonstrating the level of traceability to internationally agreed (where possible SI) reference standards. Such standards may be man-made, natural or intrinsic in nature. The documented evidence should include a description of the processes used, together with an uncertainty budget (or other appropriate quality performance measure).

The guidelines of QA4EO provide a template and guidance on how to achieve this in a harmonised and robust manner.

One of the key guiding principles of QA4EO is appropriateness underlain by a community desire to:

• Achieve consistency amongst peers,
• Provide advice and training for newcomers,
• Provide transparency of approach, and
• Improve efficiency.

The QA4EO process and its implementation should NOT be judgmental, bureaucratic or costly.

The Quality Assurance Framework for Earth Observation consists of ten distinct key guidelines linked through an overarching document - the GUIDELINES FRAMEWORK.

The naming convention of the guideline documentation is fully explained in guideline QA4EOQAEO- GEN-CEK-001 and collates the guidelines in terms of more specific functions, e.g. Data Quality (DQ), Data Policy (DP) and Communication and Education (CE). Guideline QA4EO-QAEO-GEN-DQK- 002 ("A guide to content of a documentary procedure to meet the Quality Assurance requirements of GEO") is essentially the core requirement for QA4EO.

If processes are carried out in full compliance of this fundamental guide, a user can have confidence in any resultant output. QA4EO-QAEO-GEN-DQK-002 provides the template to guide the user through the process, aided by the other nine key guidelines for specific technical details, but in principle this guide provides all the information needed to be compliant. In considering issues of interoperability and international harmonisation within any specific GEO community it is often helpful to start with a review of generic activities and from these define key requirements that drive the QA process. For example, in the space sector all derived information products originate from a measurement made by a satellite sensor. Thus, a set of key activities for every sensor could be defined for implementation during its development and operation. Guideline QA4EO-QAEO-GENDQK- 001 provides this satellite-based example to illustrate the process.

This example shows how the top level requirements drive the need for community references, indicate critical generic deliverables for bias evaluation through comparisons and act as a starting point for more detailed technical procedures to underpin the top level requirements. Schematic summary of the data quality aspects of the QA4EO process Key Definitions Quality Indicators: a means of providing a user of data or derived product (which is the result of a process) with sufficient information to assess its suitability for a particular application.

This information should be based on a quantitative assessment of its traceability to an agreed reference or measurement standard (ideally SI), but can be presented as numeric or a text descriptor, providing the quantitative linkage is defined. Traceability: property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations each contributing to the measurement uncertainty. Reference (measurement) standard: realisation of the definition of a given quantity, ideally with a stated uncertainty, which can be used as a reference; it can be individual or community defined. Uncertainty: non-negative parameter characterising the dispersion of the quantity values that are being attributed to a measure (quantity), based on the information used. Where possible this should be derived from an experimental evaluation but can also be an estimate based on other information, e.g. experience.