Performance of OLCI Sentinel-3A satellite in the Northeast Pacific coastal waters

Giannini, F.; Hunt, Brian P. V.; Jacoby, Derek; Costa, Maycira

doi:10.1016/j.rse.2021.112317

Cited by 43 publications

(75 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although no other evaluation of Polymer applied to hyperspectral satellite data has been carried out, we can compare our results of the Rrs match-up analysis to the literature on the performance of Polymer applied to multispectral data in optically complex waters. The uncertainties observed here were lower or close to those reported in recent studies evaluating OLCI (Ocean and Land Colour Instrument on board Sentinel-3 satellites) Polymer radiometric retrievals in inland and coastal waters [ 12 , 29 ]. For instance, ref.…”

Section: Resultssupporting

confidence: 83%

“…[ 29 ] reported MAPD ranging from 22 to 43% in the blue bands (412–490 nm) for Estonian inland and Baltic Sea coastal waters; our results showed differences between 16 and 36%. Except for the red band 668 nm, the biases were also much lower than those reported by [ 12 ] for the coastal waters of British Columbia and Southeast Alaska.…”

Section: Resultscontrasting

confidence: 59%

“…Polymer is a spectral matching algorithm which uses the full spectra available, together with an iterative optimisation scheme, to separate the radiometric contribution of water from the atmospheric and surface contributions. Evaluation of Polymer applied to multispectral data showed improved performance over other existing AC methods and increased data coverage, due to its ability to handle thin clouds and sun glint [ 2 , 7 , 8 , 9 , 10 , 11 , 12 ]. Another strength of Polymer, compared with other ocean colour AC algorithms, is that it can be applied to several sensors, thus maximising the consistency between products [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Soppa

Silva

Steinmetz³

et al. 2021

Sensors

View full text Add to dashboard Cite

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

show abstract

Section: Resultssupporting

confidence: 83%

Section: Resultscontrasting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Soppa

Silva

Steinmetz³

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Measurements of the top 10 m have been averaged to compare well with the remote sensing data. Note that the satellite data is calibrated with Chl-a estimated by high-performance liquid chromatography (HPLC), which slightly differs from Chla estimated by fluorometers available in this study (Neveux et al, 1990;Reynolds et al, 2001;Giannini et al, 2021).…”

Section: Satellite Chlorophyll-a Validationmentioning

confidence: 72%

Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas

Silva¹,

Counillon

Brajard³

et al. 2021

Front. Mar. Sci.

View full text Add to dashboard Cite

Phytoplankton blooms provide biomass to the marine trophic web, contribute to the carbon removal from the atmosphere and can be deadly when associated with harmful species. This points to the need to understand the phenology of the blooms in the Barents, Norwegian, and North seas. We use satellite chlorophyll-a from 2000 to 2020 to assess robust climatological and the interannual trends of spring and summer blooms onset, peak day, duration and intensity. Further, we also correlate the interannual variability of the blooms with mixed layer depth (MLD), sea surface temperature (SST), wind speed and suspended particulate matter (SPM) retrieved from models and remote sensing. The climatological spring blooms start on March 10th and end on June 19th. The climatological summer blooms begin on July 13th and end on September 17th. In the Barents Sea, years of shallower mixed layer (ML) driven by both calm waters and higher freshwaters input keeps the phytoplankton in the euphotic zone, causing the spring bloom to start earlier and reach higher biomass but end sooner due to the lack of nutrients upwelling from the deep. In the Norwegian Sea, a correlation between SST and the spring blooms is found. Here, warmer waters are correlated to earlier and stronger blooms in most regions but with later and weaker blooms in the eastern Norwegian Sea. In the North Sea, years of shallower ML reduces the phytoplankton sinking below the euphotic zone and limits the SPM increase from the bed shear stress, creating an ideal environment of stratified and clear waters to develop stronger spring blooms. Last, the summer blooms onset, peak day and duration have been rapidly delaying at a rate of 1.25-day year–1, but with inconclusive causes based on the parameters assessed in this study.

show abstract

“…In that study, NNv2 exhibited better results than the other atmospheric correction algorithms, although affected by low accuracy at the blue spectral bands. Giannini et al [5] documented large underestimates in the blue and also a saturation of remote sensing reflectance RRS() for values above 0.008 sr −1 in the Northeast Pacific coastal waters. Finally, Vanhellemont and Ruddick [6] indicated general underestimates of radiometric products from NNv2 in the Belgian coastal turbid waters.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of OLCI Neural Network Radiometric Water Products

Cazzaniga

Zibordi

Mélin

et al. 2022

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

Radiometric water products from the Neural Network (NNv2) in the Alternative Atmospheric Correction (AAC) processing chain of Ocean and Land Colour Instruments (OLCI) were assessed over different marine regions. These products, not included among the operational ones, were custom produced from the Copernicus Sentinel-3 OLCI Baseline Collection 3. The assessment benefitted of in situ reference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of different water types. These included clear waters in the Western Mediterranean Sea, optically-complex waters characterized by varying concentrations of total suspended matter and chromophoric dissolved organic matter (CDOM) in the northern Adriatic Sea, and optically-complex waters characterized by very high concentrations of CDOM in the Baltic Sea. The comparison of the water-leaving radiances 𝑳 𝑾𝑵 () derived from OLCI data on board Sentinel-3A and Sentinel-3B with those from AERONET-OC confirmed consistency between the products from the two satellite sensors. However, the accuracy of satellite data products exhibited dependence on the water type. A general underestimate of 𝑳 𝑾𝑵 () was observed for clear waters. Conversely, overestimates were observed for data products from opticallycomplex waters with the worst results obtained for CDOMdominated waters. These findings suggest caution in exploiting NNv2 radiometric products, especially for highly absorbing and clear waters.

show abstract

Performance of OLCI Sentinel-3A satellite in the Northeast Pacific coastal waters

Cited by 43 publications

References 94 publications

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas

Evaluation of OLCI Neural Network Radiometric Water Products

Contact Info

Product

Resources

About