Applying DINEOF algorithm on cloudy sea-surface temperature satellite data over the eastern Mediterranean Sea

2013 ◽  
Author(s):  
Andreas Nikolaidis ◽  
Georgios Georgiou ◽  
Diofantos Hadjimitsis ◽  
Evangelos Akylas
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Data ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Eastern Mediterranean Sea

scholarly journals Chlorophyll in the Eastern Mediterranean Sea: Correlations with Environmental Factors and Trends

Environments ◽  
2019 ◽  
Vol 6 (8) ◽  
pp. 98 ◽  
Author(s):  
Dionysia Kotta ◽  
Dimitra Kitsiou
Keyword(s):  
Wind Speed ◽  
Environmental Factors ◽  
Surface Temperature ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Sea Level Pressure ◽  
Eastern Mediterranean Sea ◽  
Mean Sea Level

The research on marine chlorophyll concentrations, as indicators of phytoplankton abundance, their relations with environmental parameters, and their trends is of global interest. It is also crucial when referring to oligotrophic environments where maintenance or increase in primary production is vital. The present study focuses on the Eastern Mediterranean Sea that is in general oligotrophic. Its primary goal is to explore possible relations between surface chlorophyll-a concentrations and environmental factors. The involved parameters are the sea surface temperature, the wind speed, the wave height, the precipitation, and the mean sea level pressure; their relation with chlorophyll is assessed through the calculation of the relevant correlation coefficients, based on monthly satellite-derived and numerical model data for the period 1998–2016. The results show that chlorophyll relates inversely with sea surface temperature; in general positively with wind speed and wave height; positively, although weaker, with precipitation; and negatively, but area and season limited, with mean sea level pressure. These correlations are stronger over the open southern part of the study area and strongly dependent on the season. A secondary aim of the study is the estimation of chlorophyll trends for the same time interval, which is performed separately for the low and the high production periods. The statistically significant results reveal only increasing local chlorophyll trends that, for each period, mainly characterize the eastern and the western part of the area, respectively.

scholarly journals Artificial Neural Networks to reconstruct incomplete satellite data: application to the Mediterranean Sea Surface Temperature

2008 ◽  
Vol 15 (1) ◽  
pp. 61-70 ◽  
Author(s):  
E. Pisoni ◽  
F. Pastor ◽  
M. Volta
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Data ◽  
Spatial Information ◽  
Interpolation Method ◽  
Sea Surface ◽  
Noaa Avhrr ◽  
Artificial Neural ◽  
The Mediterranean

Abstract. Satellite data can be very useful in applications where extensive spatial information is needed, but sometimes missing data due to presence of clouds can affect data quality. In this study a methodology for pre-processing sea surface temperature (SST) data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN) system. The effectiveness of the procedure has been also evaluated comparing results obtained using an interpolation method. After the methodology has been identified, a validation is performed on 3 different episodes representative of SST variability in the Mediterranean sea. The proposed technique can process SST NOAA/AVHRR data to simulate severe storm episodes by means of prognostic meteorological models.

scholarly journals Downward fluxes of elemental carbon, metals and polycyclic aromatic hydrocarbons in settling particles from the deep Ionian Sea (NESTOR site), Eastern Mediterranean

2012 ◽  
Vol 9 (12) ◽  
pp. 19165-19197 ◽  
Author(s):  
C. Theodosi ◽  
C. Parinos ◽  
A. Gogou ◽  
A. Kokotos ◽  
S. Stavrakakis ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Mediterranean Sea ◽  
Aromatic Hydrocarbons ◽  
Elemental Carbon ◽  
Eastern Mediterranean ◽  
Vertical Transport ◽  
Sea Surface ◽  
Ionian Sea ◽  
Eastern Mediterranean Sea ◽  
Polycyclic Aromatic

Abstract. To assess sources and major processes controlling vertical transport of both anthropogenic and natural chemical species in deep basins of the Eastern Mediterranean Sea (SE Ionian Sea, Nestor site), we performed chemical characterization (elemental carbon, major and trace metals and polycyclic aromatic hydrocarbons) of marine sinking particles. Sediment traps were deployed at five successive depths, 700 m, 1200 m, 2000 m, 3200 m and 4300 m from the sea surface, during the period of May 2007 to October 2008. Fluxes of all measured species exhibited minimum values from January to March 2008 and maximum from April to September 2008, with an evident covariance revealing a common and rapid vertical transport mechanism from 700 m down to 4300 m depth. Crustal matter flux from atmospheric deposition plays an important role in the temporal variability of particulate matter with significant contribution from biogenic constituents namely the seasonal succession in the export of planktonic biomass, expressed by particulate organic carbon (POC), carbonates and biogenic Si fluxes (Stavrakakis et al., 2012). Tracers (elemental carbon, retene) of the devastating forest fires occurred in August 2007 in southern Greece, were detected at sediment trap material in all depths with a delay of 15 days at 4300 m, indicating a rapid and well-coupled transport of sinking particulate material between the sea-surface and deep layers of the Eastern Mediterranean Sea. Lateral inputs of pollutants at the deepest trap (4300 m) are probably of importance, due to the influence of deep Adriatic water at the study site.

Variation in biometry and population density of solitary corals with solar radiation and sea surface temperature in the Mediterranean Sea

2007 ◽  
Vol 152 (2) ◽  
pp. 351-361 ◽  
Author(s):  
Stefano Goffredo ◽  
Erik Caroselli ◽  
Elettra Pignotti ◽  
Guido Mattioli ◽  
Francesco Zaccanti
Keyword(s):  
Population Density ◽  
Solar Radiation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Sea Surface ◽  
The Mediterranean

Deep Learning for Sea Temperature Eddy signature Classification

2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Sea ◽  
Satellite Imagery ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Temperature ◽  
Cloud Coverage

<p>Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years <strong>[1]</strong>. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.</p><p>Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.</p><p>We introduce a novel Deep Learning approach to classify sea temperature eddy signatures <strong>[2]</strong>. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas <strong>[3]</strong>. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.</p><p>We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.</p><p><strong>[1]</strong> <em>Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.</em></p><p><strong>[2]</strong> <em>E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.</em></p><p><strong>[3]</strong> <em>https://www.lmd.polytechnique.fr/dyned/</em></p>

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