This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been f...This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been found to be the most suitable for noisy ocean colour images. In the attempted technique, partial removal of the noise in the images is performed and the edges are detected using entropic method. In our approach, Jhensen-Shannon divergence for the images is calculated, and the divergence image is arrived at after applying an appropriate threshold and filter to estimate the gradients. An attempted case study on retrieving chlorophyll front edges using this technique indicates that entropic method is far superior to conventional edge-enhancement tools, in terms of its insensitivity to impulsive noises and, capability in detecting meso- and micro-scale changes. This procedure would largely decrease the ambiguities associated with the ocean colour edges and hence has promising application potential in targeting fishing zones, sediment dispersion modeling and climate related studies.展开更多
Ocean-colour remote-sensing products have been used to estimate Chl-a trends in European seas with the aim to develop a new indicator based on ocean-colour data for the European Environment Agency (EEA). The new indic...Ocean-colour remote-sensing products have been used to estimate Chl-a trends in European seas with the aim to develop a new indicator based on ocean-colour data for the European Environment Agency (EEA). The new indicator, called CSI023(+), derived from satellite ocean-colour products from the MyOcean Marine Core Service (www.myocean.eu) has been defined and calculated. In our analysis, we have used 3 MyOcean satellite products: 2 global satellite products (SeaWiFS and a merged product) and one regional (adjusted to specific regional Mediterranean conditions) ocean-colour product. We have evaluated the differences among the 3 different products in estimating Chl-a trends. CSI023(+) complements the EEA CSI023 indicator for eutrophication based on chlorophyll-a (Chl-a) in-situ observations. Analysis has revealed the potential of ocean colour as a CSI023(+) indicator to detect large-scale, and in some cases, even local-scale, changes and decreasing trends of Chl-a were observed throughout the Black Sea, the Eastern Mediterranean, the southern part of the Western Mediterranean, the English Channel and the north part of the North Sea. Large areas with increasing trends were observed in the Bay of Biscay, in the North-East Atlantic regions of Ireland and the UK, in the northern part of the North Sea, in the Kattegat and in the Baltic. Specific analysis has been performed in the Mediterranean coastal areas using regional products to investigate local scale results. Validation of ocean-colour products has been carried out through comparison with observations of the Eionet EEA database. The validation results highlight that regional products produced with regional algorithms are recommended for the future.展开更多
In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been...In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case Ⅱ waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM.展开更多
Atmospheric correction over turbid and productive waters continues to be problematic and often results in excessive errors in retrieved water-leaving radiance and bio-geo-physical products. This may be likely due to o...Atmospheric correction over turbid and productive waters continues to be problematic and often results in excessive errors in retrieved water-leaving radiance and bio-geo-physical products. This may be likely due to overestimation of La + Lra (atmospheric signal due to aerosol and Rayleigh scattering) in the visible and thus reduction in Lw which appears as an elevated pigment concentration. Spectral models for reconstructing the atmospherically distorted data are developed and their results are thoroughly validated with in-situ data from a wide range of waters. Good agreement was found between the retrieved products and in-situ data. Our results indicate that the new spectral models have the potential to improve the accuracy of ocean colour retrievals in optically complex waters, and can become an important part of the processing of data from ocean colour sensors (e.g., Ocean colour monitor OCM2) with only two near-infrared bands (i.e., 765 and 865 nm).展开更多
An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitor...An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor （SeaWiFS） data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by pieoplankton. Results, however, are seen to vary depending on season and ocean basin.展开更多
文摘This paper discusses a robust technique using entropy-based detection for delineating edges in ocean colour images. The detection process relies on Jhensen-Shannon divergence based image segmentation, which has been found to be the most suitable for noisy ocean colour images. In the attempted technique, partial removal of the noise in the images is performed and the edges are detected using entropic method. In our approach, Jhensen-Shannon divergence for the images is calculated, and the divergence image is arrived at after applying an appropriate threshold and filter to estimate the gradients. An attempted case study on retrieving chlorophyll front edges using this technique indicates that entropic method is far superior to conventional edge-enhancement tools, in terms of its insensitivity to impulsive noises and, capability in detecting meso- and micro-scale changes. This procedure would largely decrease the ambiguities associated with the ocean colour edges and hence has promising application potential in targeting fishing zones, sediment dispersion modeling and climate related studies.
文摘Ocean-colour remote-sensing products have been used to estimate Chl-a trends in European seas with the aim to develop a new indicator based on ocean-colour data for the European Environment Agency (EEA). The new indicator, called CSI023(+), derived from satellite ocean-colour products from the MyOcean Marine Core Service (www.myocean.eu) has been defined and calculated. In our analysis, we have used 3 MyOcean satellite products: 2 global satellite products (SeaWiFS and a merged product) and one regional (adjusted to specific regional Mediterranean conditions) ocean-colour product. We have evaluated the differences among the 3 different products in estimating Chl-a trends. CSI023(+) complements the EEA CSI023 indicator for eutrophication based on chlorophyll-a (Chl-a) in-situ observations. Analysis has revealed the potential of ocean colour as a CSI023(+) indicator to detect large-scale, and in some cases, even local-scale, changes and decreasing trends of Chl-a were observed throughout the Black Sea, the Eastern Mediterranean, the southern part of the Western Mediterranean, the English Channel and the north part of the North Sea. Large areas with increasing trends were observed in the Bay of Biscay, in the North-East Atlantic regions of Ireland and the UK, in the northern part of the North Sea, in the Kattegat and in the Baltic. Specific analysis has been performed in the Mediterranean coastal areas using regional products to investigate local scale results. Validation of ocean-colour products has been carried out through comparison with observations of the Eionet EEA database. The validation results highlight that regional products produced with regional algorithms are recommended for the future.
文摘In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case Ⅱ waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM.
文摘Atmospheric correction over turbid and productive waters continues to be problematic and often results in excessive errors in retrieved water-leaving radiance and bio-geo-physical products. This may be likely due to overestimation of La + Lra (atmospheric signal due to aerosol and Rayleigh scattering) in the visible and thus reduction in Lw which appears as an elevated pigment concentration. Spectral models for reconstructing the atmospherically distorted data are developed and their results are thoroughly validated with in-situ data from a wide range of waters. Good agreement was found between the retrieved products and in-situ data. Our results indicate that the new spectral models have the potential to improve the accuracy of ocean colour retrievals in optically complex waters, and can become an important part of the processing of data from ocean colour sensors (e.g., Ocean colour monitor OCM2) with only two near-infrared bands (i.e., 765 and 865 nm).
基金funded by the National Environmental Research Council, UK, through a PhD studentship at the Centre for observation of Air-Sea Interactions & fluXes (CASIX)the National Centre for Earth Observation and NERC Oceans 2025 programme (Themes 6 and 10)
文摘An important goal in ocean colour remote sensing is to accurately detect different phytoplankton groups with the potential uses including the validation of multi-phytoplankton carbon cycle models; synoptically monitoring the health of our oceans, and improving our understanding of the bio-geochemical interactions between phytoplankton and their environment. In this paper a new algorithm is developed for detecting three dominant phytoplankton size classes based on distinct differences in their optical signatures. The technique is validated against an independent coupled satellite reflectance and in situ pigment dataset and run on the 10-year NASA Sea viewing Wide Field of view Sensor （SeaWiFS） data series. Results indicate that on average 3.6% of the global oceanic surface layer is dominated by microplankton, 18.0% by nanoplankton and 78.4% by pieoplankton. Results, however, are seen to vary depending on season and ocean basin.
