The Case 2 Regional CoastColour (C2RCC) atmospheric correction is a full spectrum version using a set of neural networks which are trained on simulated top-of-atmosphere reflectance. The radiative transfer simulations include the full ocean and atmosphere system, i.e., a specific water model is included in the simulations. C2RCC is used to generate the Case 2 water products in Sentinel 3 OLCI standard ESA products, as well as in the upcoming MERIS 4th Reprocessing. Products generated are the inherent optical properties (IOPs), absorption and scattering of the different constituents; the three major optically relevant concentrations, i.e., phytoplankton pigments, total suspended matter and yellow substance; and their related uncertainties. It supports several sensors: starting from Sentinel-3 OLCI over Sentinel-2 MSI to Landsat-8 OLI and the heritage sensor MERIS, both in 3rd and 4th reprocessing versions. MODIS, VIIRS and SeaWiFS are supported as well.

Algorithm Specification

The C2RCC processor is based on deep learning approaches. Neural networks are trained in order to perform the inversion of spectrum for the atmospheric correction, i.e., the determination of the water leaving radiance from the top of atmosphere radiances, as well as the retrieval of inherent optical properties of the water body. The C2RCC processor relies on a large database of simulated water leaving reflectances, and related top-of- atmosphere radiances. A careful characterisation of optically complex waters through its IOPs as well as of coastal atmospheres is used to parameterise radiative transfer models for the water body and the atmosphere. Covariances between the water constituents are taken into account and a large database of reflectances at the water surface is calculated. These reflectances are further used as lower boundary conditions for the radiative transfer calculation in the atmosphere.

Finally, a database of 5 million cases is generated, which is the basis for training neural nets. For example, the top-of-atmosphere full spectrum is input to a neural net, and the water leaving reflectance in the visible and near-infrared bands is the output. The training can be understood as a nonlinear multiple regression.

The input spectra are corrected for gaseous absorption. Air pressure, and thus a proper altitude correction, is inherent part of the neural network processing. The main output of the atmosphere part is directional water leaving reflectances produced by the atmospheric correction neural net. The atmosphere part contains out-of-range tests and out-of-scope tests of the TOA reflectances, resulting in corresponding quality flags. Optionally the output of the auto-associative neural net used of the out-of-scope test can be written to the output file in the SNAP version of the processor. The output from the transmittance NN is also used to raise a cloud-risk flag. The in-water part gets as input the directional water leaving reflectances from the atmosphere part.

Related publications

  1. Brockmann, Carsten; Doerffer, Roland; Peters, Marco; Kerstin, Stelzer; Embacher, Sabine; Ruescas, Ana (2016). 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.
  2. Doerffer, Roland; Brockmann, Carsten; Stelzer, Kerstin; Ruescas, Ana B. (in prep.). Improved Case 2 Regional CoastColour Neural Network for Complex Waters.
  3. Schiller, Helmut; Doerffer, Roland (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, https://doi.org/10.1080/014311699212443.
  4. Kyryliuk, Dmytro; Kratzer, Susanne (2019). Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea, https://www.mdpi.com/1424-8220/19/16/3609
  5. Soriano-González, J.; Urrego, E.P.; Sòria-Perpinyà, X.; Angelats, E.; Alcaraz, C.; Delegido, J.; Ruíz-Verdú, A.; Tenjo, C.; Vicente, E.; Moreno, J. (2022). Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas. Remote Sens. 2022, 14, 1124. https://doi.org/10.3390/rs14051124
  6. Pereira-Sandoval, M.; Ruescas, A.B.; García-Jimemez, J.; Blix, K. (in prep.). Machine Learning Supervised Classifications of Optical Water Types in Spanish Lake Waters.
  7. Doerffer, Roland (2015). Algorithm Theoretical Bases Document (ATBD) for L2 processing of MERIS data of case 2 waters, 4th reprocessing

Training material