@misc{mueller_the_ocean_2015, 
author={Mueller, D.,Krasemann, H.,Brewin, R.J.W.,Brockmann, C.,Deschamps, P.-Y.,Doerffer, R.,Fomferra, N.,Franz, B.A.,Grant, M.G.,Groom, S.B.,Melin, F.,Platt, T.,Regner, P.,Sathyendranath, S.,Steinmetz, F.,Swinton, J.},
title={The Ocean Colour Climate Change Initiative: I. A methodology for assessing atmospheric correction processors based on in-situ measurements},
year={2015},
howpublished = {journal article},
doi = {https://doi.org/10.1016/j.rse.2013.11.026},
abstract = {The Ocean Colour Climate Change Initiative intends to provide a long-term time series of ocean colour data and investigate the detectable climate impact. A reliable and stable atmospheric correction procedure is the basis for ocean colour products of the necessary high quality. In order to guarantee an objective selection from a set of four atmospheric correction processors, the common validation strategy of comparisons between in-situ and satellite-derived water leaving reflectance spectra, is extended by a ranking system. In principle, the statistical parameters such as root mean square error, bias, etc. and measures of goodness of fit, are transformed into relative scores, which evaluate the relationship of quality dependent on the algorithms under study. The sensitivity of these scores to the selected database has been assessed by a bootstrapping exercise, which allows identification of the uncertainty in the scoring results. Although the presented methodology is intended to be used in an algorithm selection process, this paper focusses on the scope of the methodology rather than the properties of the individual processors.},
note = {Online available at: \url{https://doi.org/10.1016/j.rse.2013.11.026} (DOI). Mueller, D.; Krasemann, H.; Brewin, R.; Brockmann, C.; Deschamps, P.; Doerffer, R.; Fomferra, N.; Franz, B.; Grant, M.; Groom, S.; Melin, F.; Platt, T.; Regner, P.; Sathyendranath, S.; Steinmetz, F.; Swinton, J.: The Ocean Colour Climate Change Initiative: I. A methodology for assessing atmospheric correction processors based on in-situ measurements. Remote Sensing of Environment. 2015. vol. 162, 242-256. DOI: 10.1016/j.rse.2013.11.026}}