Algorithms for Atmospheric Correction (AC) and retrieval of water constituents from ocean colour satellite imagery have advanced considerably during the last decade but still they are subject to ongoing improvements. These improvements take place on many fronts, comprising the characterization of the atmosphere’s optical properties, i.e., aerosols, the characterization of the optical properties of the water body, i.e., scattering and absorption properties of optically active substances as well as the water itself, and the mathematical methods to invert the radiative transfer equation.
In the 1990’s, Schiller and Doerffer (1999) have introduced a methodology to resolve the problem of AC and in-water retrieval based on machine learning (ML) with artificial neural nets trained on large datasets derived by radiative transfer modelling. It was implemented in the Case2Regional processor, which later evolved into the C2RCC processor (Brockmann et al., 2016). While the ML aspects are most often mentioned when the C2RCC approach is discussed, the proper optical characterization of the water and the atmosphere is probably one of the most important parts. This consists of the parameterization of the radiative transfer models, and the specification of the optical model for the training dataset (co-variance of water constituents, their ranges and frequency distribution). Specific optical properties of algae assemblages, sediments, and coloured dissolved organic matter (CDOM) were used to derive special neural nets for extreme scattering or extreme absorbing waters (C2X nets) or for regional waters such as the Baltic Sea (Baltic+ nets).
The C2RCC processor is open source (https://github.com/senbox-org/s3tbx/tree/master/s3tbx-c2rcc) and publicly available through the SNAP toolbox (https://step.esa.int/main/toolboxes/snap/). The neural nets of C2RCC are included in the Sentinel-3 OLCI ground segment processor, but it is also capable of processing data from Sentinels–2 MSI, MERIS, VIIRS, MODIS, and Landsat-8 OLI.
In the past, the C2RCC development benefitted from community feedback and contributions by various scientists through collaborations in various international projects.
With the beginning of 2022 the water colour community will be entrusted (completely) with C2RCC as a community project. C2RCC offers several options to modify or further develop it. The elements will be made available to the community, so that C2RCC will hopefully evolve by the inputs provided by the community:
• optical properties of aerosols and atmospheric gases
• specific inherent optical properties of water constituents (SIOP, ranges)
• models that link optical with biogeochemical parameters
• optical water type classification (OWT)
• phytoplankton diversity
• validation data (link to in-situ databases such as Limnades, EUM OCDB, NASA SeaBASS)
• training datasets derived by RT modelling (well referenced to above IOPs)
• trained neural nets for
o atmosphere inversion (water leaving reflectance)
o path reflectance
o atmospheric transmittance
o auto-associated nets for out-of-scope testing
o water forward
o water inversion (IOP retrieval)
o kd spectral
o uncertainties of IOPs
• applicability to new sensors
Documentation for the parameters, the models used, and training of the nets will be provided. Webspace for the community to exchange their results and present their finding will be another part of the community project. Four main partners (Brockmann Consult Germany, Helmholtz-Zentrum Hereon, Brockmann Geomatics Sweden, and Ocean Obs Norway) will drive and maintain the project. However, it will be an unfunded activity whose success will depend on the uptake and contribution by the water colour science and application community. Of course, this platform may help the community to develop ideas for further innovative R&D work and projects.
Brockmann, Carsten; Doerffer, Roland; Peters, Marco; Kerstin, Stelzer; Embacher, Sabine; Ruescas, Ana. Evolution of the C2RCC Neural Network for Sentinel 2 and 3 for the Retrieval of Ocean Colour Products in Normal and Extreme Optically Complex Waters. Living Planet Symposium, Proceedings of the conference held 9-13 May 2016 in Prague, Czech Republic. Edited by L. Ouwehand. ESA-SP Volume 740, ISBN: 978-92-9221-305-3, p.54
Helmut Schiller & Roland Doerffer (1999) Neural network for emulation of an inverse model operational derivation of Case II water properties from MERIS data, International Journal of Remote Sensing, 20:9, 1735-1746, DOI: 10.1080/014311699212443