CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

Nechad, Bouchra; Ruddick, Kevin; Schroeder, Thomas; Oubelkheir, Kadija; Blondeau-Patissier, David; Cherukuru, Nagur; Brando, Vittorio E.; Dekker, Arnold G.; Clementson, Lesley; Banks, Andrew Clive; Maritorena, Stéphane; Werdell, P. Jeremy; Sá, Carolina; Brotas, Vanda; Caballero, Isabel; Ahn, Yu-Hwan; Salama, Mhd. Suhyb; Tilstone, Gavin H.; Martinez‐Vicente, Victor; Foley, David G.; McKibben, Morgaine; Nahorniak, Jasmine; Peterson, Tawnya D.; Siliò-Calzada, A.; Röttgers, Rüdiger; Lee, Z.; Peters, Marco; Brockmann, Carsten

doi:10.5194/essd-7-319-2015

Cited by 66 publications

(50 citation statements)

References 51 publications

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“…It is comprised of the following variables: remote-sensing reflectance (rrs), chlorophyll a concentration (chla), algal pigment absorption coefficient (aph), detrital and coloured dissolved organic matter absorption coefficient (adg), particle backscattering coefficient (bbp) and diffuse attenuation coefficient for downward irradiance (kd). A similar effort of compiling biooptical in situ data from different sources has been recently published by Nechad et al (2015). Given their focus on selected coastal regions, most of the data presented here are not part of their compilation.…”

Section: Preprocessing and Mergingmentioning

confidence: 99%

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A compilation of global bio-optical in situ data for ocean-colour satellite applications

Valente

Sathyendranath

Brotas

et al. 2016

Earth Syst. Sci. Data

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Abstract.A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).

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Section: Preprocessing and Mergingmentioning

confidence: 99%

“…in situ fluorescence for chlorophyll concentration); and (3) spuriously high or low data. For the latter, the following limits were imposed: for chla_fluor and chla_hplc [0.001-100] Pope and Fry (1997). Also during this stage, three metadata strings were attributed to each observation: dataset, subdataset and pi.…”

Section: Preprocessing and Mergingmentioning

confidence: 99%

A compilation of global bio-optical in situ data for ocean-colour satellite applications

Valente

Sathyendranath

Brotas

et al. 2016

Earth Syst. Sci. Data

Self Cite

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“…The selected output parameters are mostly common ocean color products (e.g., Nechad et al, 2015;Valente et al, 2016): The 12 parameters are results of three independent sets of NNs, one that computes concentrations, one gives IOPs at 440 nm, and the last provides different IOPs and AOPs (see Appendix Table A1). Concentrations can be directly derived with NNs or alternatively, they can be estimated using IOPs, e.g., Chl from a p (440) or ISM from b m (440) (Doerffer and Schiller, 2007).…”

Section: Nn Trainingmentioning

confidence: 99%

“…The "CoastColour Round Robin" (CCRR) dataset by Nechad et al (2015) compiles inputs and results from 5,000 Hydrolight simulation. Atmospheric boundary conditions and simulation setup are comparable with above mentioned simulations.…”

Section: Simulated Ccrr Datamentioning

confidence: 99%

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

2017

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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.

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“…Targeted voyages with manually operated above-water radiometers can provide comprehensive datasets of a region at a given time [10,[14][15][16], while autonomous above-water radiometers can provide more extensive spatial coverage if the near-surface effects such as sun glint, platform shadowing and spray are minimized by maintaining a suitable viewing geometry and by implementing adequate quality control and processing procedures [11,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data

et al. 2016

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Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia's ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral data can be re-sampled for comparison with current and future sensor wavebands. The continuous spectral acquisition along the ship track enables spatial resampling to match satellite footprint. This study reports spectral comparisons of the radiometer data with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua for contrasting water types in tropical waters off northern Australia based on the standard NIR atmospheric correction implemented in SeaDAS. Consistent match-ups are shown for transects of up to 50 km over a range of reflectance values. The MODIS and VIIRS satellite reflectance data consistently underestimated the in situ spectra in the blue with a bias relative to the "dynamic above water radiance and irradiance collector" (DALEC) at 443 nm ranging from 9.8ˆ10´4 to 3.1ˆ10´3 sr´1. Automated acquisition has produced good quality data under standard operating and maintenance procedures. A sensitivity analysis explored the effects of some assumptions in the data reduction methods, indicating the need for a comprehensive investigation and quantification of each source of uncertainty in the estimate of the DALEC reflectances. Deployment on a Research Vessel provides the potential for the radiometric data to be combined with other sampling and observational activities to contribute to algorithm development in the wider bio-optical research community.

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CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

Cited by 66 publications

References 51 publications

A compilation of global bio-optical in situ data for ocean-colour satellite applications

A compilation of global bio-optical in situ data for ocean-colour satellite applications

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

The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data

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