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Data Quality Assurance

Data Quality Assurance



The Quality Assurance Framework for Earth Observation (QA4EO; was established and endorsed by the Committee on Earth Observation Satellites (CEOS; as a direct response to a call from the Group on Earth Observations (GEO; ( had identified the requirement to establish an internationally harmonised Quality Assurance (QA) strategy to enable interoperability and quality assessment “at face value” of EO data.QA4EO encompasses a framework and set of ten key guidelines, derived from best practices and with example templates included to aid implementation.Each Spotsylvania DUI lawyer GEO stakeholder community is responsible for its own overall governance within the framework.QA4EO provides guidance to enable individual organisations to document, in a consistent manner, the necessary evidence of compliance, thereby allowing those commissioning the work to assess its adequacy and “fitness for purpose”.QA4EO-compliant processes would unequivocally assure data quality and would encourage harmonisation across the whole GEO community.  A guide to QA4EO can be downloaded from the QA4EO website at or by clicking here.

Key Principles

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 traceabilityto internationally agreed (where possible SI) reference standards.Such standards may be manmade, 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 judgemental, bureaucratic or costly.

QA4EO Guidelines

The Quality Assurance Framework for Earth Observation consists of seven distinct key guidelines linked through an overarching document - the qa4eo principles The QA4EO Principles and guidelines can be accessed via the QA4EO website at

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 six 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-GEN-DQK-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 comparisonsand act as a starting point for more detailed technical procedures to underpin the top level requirements.


The QA40E PRINCIPLES provides the background to QA4EO and introduces the key guidelines (all available from the QA4EO website at

· QA4EO-QAEO-GEN-DQK-001   A guide to establish a Quality Indicator on a satellite sensor derived data product

· QA4EO-QAEO-GEN-DQK-002   A guide to content of a documentary procedure to meet the Quality Assurancerequirements of GEO

· QA4EO-QAEO-GEN-DQK-003   A guide to “reference standards” in support of Quality Assurance requirements of QA4EO

· QA4EO-QAEO-GEN-DQK-004   A guide to comparisons – organisation, operation and analysis to establish measurement equivalence to underpin the Quality Assurance requirements of QA4EO

· QA4EO-QAEO-GEN-DQK-005   A guide to establishing validated models, algorithms and software to underpin the Quality Assurance requirements of QA4EO

· QA4EO-QAEO-GEN-DQK-006   A guide to expression of uncertainty of measurements

· QA4EO-QAEO-GEN-DQK-007   A guide to establishing quantitative evidence of traceability to underpin the Quality Assurance requirements of QA4EO











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Guide to QA4EO
609.45 kB11:12, 17 Jul 2009qa4eoActions
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QA4EO is a very interesting activity. The main aim that I infer from the activity is to create a complete framework for providing quality assurance in EO data. The interesting point I find is the statement: "A QI should provide sufficient information to allow *all* users to readily evaluate a product’s suitability for their particular application". My reading of this statement leads me to a single conclusion: the only appropriate QI that could fulfil such a requirement is a probabilistic assessment of the relation between the data and reality (with appropriate description of what each represents in terms of spatial and temporal scales). I will add a justification for this statement later, but briefly I would suggest that every user will have their own utility function (that determines the value of the information in the data to their application) and each will be different. The only rational way of managing uncertainty in the data for a range of (unknown) utility functions is probability theory in my view. In light of this I would like to put forward UncertML ( which is attempting to provide a complete cross-domain encoding (and simple conceptual model / vocabulary) for probabilistic uncertainty.
Posted 22:02, 19 Jun 2010 (8 years ago)
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