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

  title={The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters},
  author={Martin Hieronymi and Dagmar M{\"u}ller and Roland Doerffer},
  journal={Frontiers in Marine Science},
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… 

Figures and Tables from this paper

Spectral band adaptation of ocean color sensors for applicability of the multi-water biogeo-optical algorithm ONNS.

A spectral band-shifting procedure is introduced, which allows exploitation of atmospherically corrected input from SeaWiFS, MODIS, MERIS, OCM-2, VIIRS, SGLI, GOCI- 2, EnMAP, or PACE/OCI and corresponding utilization of ONNS or other ocean color algorithms.

A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef

Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a

Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

A machine-learning approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters is developed and it is shown how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters.

An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance

Chlorophyll-a (Chl) retrieval from ocean colour remote sensing is problematic for relatively turbid coastal waters due to the impact of non-algal materials on atmospheric correction and standard Chl

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

The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM, whereas contribution by CDOM plays only a minor role.

CHLNET: A novel hybrid 1D CNN-SVR algorithm for estimating ocean surface chlorophyll-a

Developing a unified chlorophyll-a (Chla) inversion algorithm for cross-water types is a significant challenge owing to the insufficiency of input features and training samples. Although machine

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

A sensitivity analysis revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a, which was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model.



Phytoplankton determination in an optically complex coastal region using a multilayer perceptron neural network

The study showed a high accuracy in determining a/sub ph/(443) and, thus, further confirmed the possibility of computing the inherent optical properties of seawater significant components from the R/sub rs/ spectra.

Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS.

The process the authors adopt utilizes two NNs in parallel to relate the remote sensing reflectance at available MODIS-visible wavelengths to the absorption and backscatter coefficients at 442 nm (peak of chlorophyll absorption) and outputting the ratio of algal-to-nonalgal absorption.

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

Retrieval of water constituents, or its optical properties, requires inversion of the water leaving reflectance spectrum, measured at top of atmosphere by ocean colour satellites. The Case 2 Regional

Ocean Color Remote Sensing of Atypical Marine Optical Cases

Investigation of the remote sensing of ocean color (OC) products in typical case-1 waters and optically complex cases shows that a disproportion of samples representing different marine optical cases influences the MLP learning and hence also the data product retrieval.

Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters

[1] In the present paper, we report on a method to retrieve the pigment concentration in Case I waters from ocean color. The method is derived from radiative transfer (RT) simulations and subsequent

Development of a Neural Network Algorithm for Retrieving Concentrations of Chlorophyll, Suspended Matter and Yellow Substance from Radiance Data of the Ocean Color and Temperature Scanner

An algorithm is presented to retrieve the concentrations of chlorophyll a, suspended pariclulate matter and yellow substance from normalized water-leaving radiances of the Ocean Color and Temperature