The Ocean Colour Climate Change Initiative: I. A methodology for assessing atmospheric correction processors based on in-situ measurements

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 reectance 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 t, 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 identication 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.

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

“…Uncertainties in the fluorescence quantum yield efficiency of phytoplankton are another argument. Furthermore, we observe higher uncertainties in violet and blue wavebands generally shown in atmospheric correction validations (Müller et al, 2015a), but also from in situ determinations of R rs due to the variable surface reflectance factor (Hieronymi, 2016;Zibordi, 2016). The allowance for OOR > 0 is one of the fine-tuning techniques to gain better spatial homogeneity of an OLCI scene and to adapt the algorithm to in situ observations.…”

“…Afterwards, a ranking system has been applied in order to determine the optimal nets without over-training. In principle, statistical parameters such as root-mean-square error and goodness of fit are transformed into relative scores, which evaluate the quality of individual nets (Müller et al, 2015a). The best performing neural network architectures per water class are specified in Table A1.…”

“…The atmospheric path radiance is typically >85% of the total signal in C1 waters, >60% in C2SX, and >94% in C2AX waters (IOCCG, 2010). Existing AC processors address the various modeling aspects quite differently, e.g., the treatment of subvisible cirrus clouds or aerosol properties, and therefore, have strengths and weaknesses for specific water types (Müller et al, 2015a). In view of the new algorithm ONNS, which relies on normalized reflectances, angle-dependent AC processes such as "smile correction" and sun glint handling are important as well to ensure spatial homogeneity of satellite data.…”

“…These AC yield much larger coverage of data (Müller et al, 2015b), but areas with high glint should nevertheless be considered cautiously. One of the AC processors is Polymer (Steinmetz et al, 2011), which reveals good performance in comparison with MERIS matchups (Müller et al, 2015a). Another processor is C2RCC (Case-2 Regional CoastColour; Brockmann et al, 2016), which is an evolution of the precursors "Case-2 Regional, " "ForwardNN, " and the "MERIS Case-2 water" algorithm (Doerffer and Schiller, 2007).…”

Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC

“…A scoring scheme based on Brewin et al (2015) and Müller et al (2015) was employed to rank the relative performance of the AC processors. The score was obtained by comparing all statistical metrics (R 2 , ψ, δ, Δ, S and I) for each waveband of each processor.…”