Performance metrics for the assessment of satellite data products: an ocean color case study.
By B. Seegers, R. Stumpf, Blake A. Schaeffer, K. Loftin, P. J. Werdell. Optics Express, 2018
316 citations recorded by the source corpus.
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.
Subfields: Industrial and Manufacturing Engineering, Media Technology, Oceanography
View Paper: https://www.semanticscholar.org/paper/b5d926ef26fb58e81bfe1fe43c693900de293362
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Oceanography
Citations in the corpus
- Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach (2020, methodology): “Per recommendations in Seegers et al. (2018), we also conduct pair-wise comparisons to determine the Model Win Rate (MWR) expressed in %.”