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

Abstract

The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m −3 , non-algae particle concentrations up to 1,500 g m −3 , and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m −1 . The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters.

Dissolved organic matter at the fluvial–marine transition in the Laptev Sea using in situ data and ocean colour remote sensing

“…To discuss processes that cause differences between satellite images, we extracted reanalysis surface wind data (four times daily) from the National Centers for Environmental Prediction (NCEP). Hieronymi et al (2017) developed the OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) in-water algorithm for the retrieval of OCRS products, among them a CDOM (440). This algorithm is designed for broad concentration ranges of different water constituents, including extremely high absorbing waters.…”

“…This will lead to a cascade of effects on the physical, chemical and biological environment of Arctic shelf waters (Stedmon et al, 2011). These include an increase of radiative heat trans-fer into surface waters, changes in carbon sequestration and reductions of sea-ice extent and thickness (Hill, 2008;Matsuoka et al, 2011).…”

“…Algorithms for Case 1 water do not provide reasonable estimates of water constituents in optically complex waters (Antoine et al, 2013). Hieronymi et al (2017) use a novel algorithm for the retrieval of OCRS products such as a CDOM (440). This algorithm is specifically designed for a broad range of concentrations of different water constituents including extremely high absorbing waters with a CDOM (440) of up to 20 m −1 .…”

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

“…Thus, they have some similarities but are quasi-independent. The so-called C2X dataset (from ESA's Case-2 Extreme Water Project) that is based on five phytoplankton absorption spectra representing five spectral (taxonomic) groups is used as the standard database and the second one, which comprises optical situations based on 128 phytoplankton absorption spectra from cultures, is for testing; detailed descriptions of the datasets are provided in Hieronymi et al (2017) and Xi et al (2015), respectively (the test dataset used here contains more different CDOM absorption and nonalgal particles, NAP, concentrations than in the previous study of Xi et al, 2015). Basic information about the HydroLight input for the two datasets is provided in Table 1.…”

“…The five water cases are however not exhaustive. According to Hieronymi et al (2017), 13 different water optical classes in total are classified by a fuzzy logic classification approach, but they are not completely included here as this study is not focusing on water type interpretation. Only examples of five water cases are chosen to show spectral variations in different scenarios.…”