VALUING RADIOMETRIC QUALITY OF REMOTE SENSING DATA FOR DECISIONS

High resolution, high frequency, multi-spectral remotely-sensed Earth Observation (EO) data is increasingly available. Challenges of ensuring radiometric data quality and consistency of data products, however, have not been widely recognized. As new products and applications rapidly emerge, and decisions linked to public safety, well-being, and environmental assessments are at stake, the issue of EO data quality and quantification of errors and their impacts has gained increasing salience. This paper presents a generalizable decision-theoretic framework for quantifying the value of data quality (partly through improved calibration), and demonstrates its application for a water quality monitoring case. Results from the particular case of freshwater monitoring show that after 30% or more probability of occurrence of a harmful algal bloom, remote sensing is useful to have regardless of the data quality level. However, factors such as cost of remote sensing data (in this case, comparing internal processing vs. external processing of tasked satellite imagery) will affect the threshold at which remote sensing value is positive regardless of data quality level.