Abstract
Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.
Highlights
The development and refinement of algorithms to derive geophysical variables from satellite measurements of ocean color has been pursued for decades [1]
This growing demand for satellite ocean color data products has necessitated the development and expansion of algorithms to accommodate user demands and requirements that span oceans, coastal marine waters, estuaries, lakes, reservoirs, and large rivers. Accommodating this influx of new and enhanced end-user needs subsequently resulted in a growing difficulty in assessing how algorithm refinements or algorithm implementation across missions results in any meaningful or constructive improvement in the accuracy and precision of derived satellite data products. This difficulty partly results from the ocean color science community traditionally relying on a small set of statistical tools for algorithm assessment that provide metrics of overall performance that are not unequivocally interpreted or are appropriate for some, but not all, datasets or missions
This study presents an exploration of metrics to assess algorithm performance and proposes approaches to combine metrics for comprehensive algorithm evaluation
Summary
The development and refinement of algorithms to derive geophysical variables from satellite measurements of ocean color has been pursued for decades [1]. Satellite measurements of ocean color play an important role in scientific Earth system modeling [4,5,6] and resource management decision support [7] This growing demand for satellite ocean color data products has necessitated the development and expansion of algorithms to accommodate user demands and requirements that span oceans, coastal marine waters, estuaries, lakes, reservoirs, and large rivers. Accommodating this influx of new and enhanced end-user needs subsequently resulted in a growing difficulty in assessing how algorithm refinements or algorithm implementation across (new) missions results in any meaningful or constructive improvement in the accuracy and precision of derived satellite data products. This difficulty partly results from the ocean color science community traditionally relying on a small set of statistical tools for algorithm assessment that provide metrics of overall performance that are not unequivocally interpreted or are appropriate for some, but not all, datasets or missions (and, not appropriate across regions or missions)
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