The data are results from radiative transfer simulations from 390 to 1020 nm in 1nm resolution. They can be convoluted to any ocean colour instrumental spectral response function and therefore represent satellite based aircraft- or groundbased measurements of the remote sensing reflectance. The data is simulated with the radiative transfer code MOMO (Matrix Operator Model), which simulates the full radiative transfer in atmosphere and ocean. The code is hosted at the institute of space sciences at Freie Universität Berlin and is not pubicly available. In addition to molecular Rayleigh scattering one maritime aerosol scatterer is considered. The data is available for 9 solar, 9 viewing zenith and 25 azimuth angles. The remote sensing reflectance is simulated in dependency of IOPs representing pure water with different salinities and 5 water constituents (Chlorophyll-a-pigment, Detritus, Yellow substance, a ’big’ and a ’small’ scatterer) in a global range of concentrations. The IOPs are varied independently. The grid points for each IOP where choosen in order to reproduce the full relation between this particular IOP and the resulting remote sensing reflectance.

The data are results from radiative transfer simulations from 390 to 1020 nm in 1nm resolution. They can be convoluted to any ocean colour instrumental spectral response function and therefore represent satellite based aircraft- or groundbased measurements of the remote sensing reflectance. The data is simulated with the radiative transfer code MOMO (Matrix Operator Model), which simulates the full radiative transfer in atmosphere and ocean. The code is hosted at the institute of space sciences at Freie Universität Berlin and is not pubicly available. In addition to molecular Rayleigh scattering one maritime aerosol scatterer is considered. The data is available for 9 solar, 9 viewing zenith and 25 azimuth angles. The remote sensing reflectance is simulated in dependency of IOPs representing pure water with different salinities and 5 water constituents (Chlorophyll-a-pigment, Detritus, Yellow substance, a ’big’ and a ’small’ scatterer) in a global range of concentrations. The IOPs are varied independently. The grid points for each IOP where choosen in order to reproduce the full relation between this particular IOP and the resulting remote sensing reflectance.

Sentinel-3 OLCI images processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution. Information about the variables are given in the attached Additional Info. Version 2 of the data has the license and some metadata corrected. Please use and refer only to Version 2 (see link below).

Satellite remote sensing enables global monitoring of water quality in freshwater and marine ecosystems. However, consistent data quality is a challenge due to variations in the performance of used algorithms for different waters. In this exemplary dataset, we use a novel approach for atmospheric correction and retrieval for water quality characteristics in inland waters, coastal areas, and the open sea. Copernicus Sentinel-3 OLCI satellite images are processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution. An overview of the variables in the dataset can be found in the Additional Information; a detailed description of the contents and background, as well as an optical analysis of the waters, can be found in Hieronymi et al. [2025]. Version 2 of the dataset has the license and some metadata corrected. Data itself remains unchanged.