Uncertainty Analysis and Quality Assurance for Earth Observation Datasets

Understanding and quantifying uncertainty is central to trustworthy science and informed decision-making. This e-learning course introduces and develops modern, metrology-based approaches for assessing the quality of Earth Observation (EO) products and has two aims:

  1. To demonstrate why a metrological framework is essential in the EO context, particularly for Fiducial Reference Measurements (FRM) and satellite data quality assurance
  2. To equip you with the terminology, concepts, and tools needed to apply best practice metrology within your own products, workflows and data-processing pipelines.

 

Throughout the course, you will build a solid theoretical foundation for understanding uncertainty, traceability, and measurement quality. You will then apply these concepts to practical, real world examples. Several case studies are included to highlight how a metrological approach adds value and how the methods developed in this project can be used to strengthen the reliability and comparability of EO products.

This course was developed as part of the Metrology for Earth Observation (Met4EO) project — an ESA funded initiative led by NPL to enhance the reliability, interoperability, and metrological traceability of EO data. The course teaches the key principles of a metrological approach to uncertainty analysis and closely follows the Quality Assurance Framework for Earth Observation (QA4EO), which aims to apply metrological concepts consistently across the EO community.

Learning Outcomes

By the end of this course, learners will:

  • Understand why a metrological framework is essential in the context of Earth observation (EO), particularly for Fiducial Reference Measurements (FRM) and satellite‑data quality assurance
  • Be equipped with the terminology, concepts, and tools needed to apply best practice metrology within their own products, workflows and data-processing pipelines