Some needed standards and best practices for calibration and validation of remote sensing data

Quality assurance for Earth observation (EO) data requires reference standards and best practices for evaluating and communicating the errors and biases within remote sensing data products. These products, which come from a variety of data sources, utilize different calibration and validation (Cal/Val) methods. Reference standards play an important role in the development of Quality Indicators (QIs) that serve as measures of reliability for end-users of remote sensing data. This paper presents the results of an initial effort to identify reference standards and related best practices relevant to the calibration and validation of EO data. The Group on Earth Observations (GEO) is also concerned with establishing confidence in specific data products that can support one or more of GEO\´s nine societal-benefit areas. We present a proposal to establish a "fitness for purpose" quality measure based on both Cal/Val error-budget requirements and on the required levels of error set by societal-benefit users.