Evaluation of annual and semiannual total mass variation over the Mediterranean Sea from satellite data

2021 ◽  
Vol 14 (10) ◽  
Author(s):  
Nadia AbouAly ◽  
Karem Abdelmohsen ◽  
Matthias Becker ◽  
Abdel-Monem S. Mohamed ◽  
Abotalib Z. Abotalib ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Satellite Data ◽  
Total Mass ◽  
Mass Variation ◽  
The Mediterranean

New toolbox in ArcGIS for the reconstruction of missing satellite data using DINEOF algorithm: a case study of reconstruction of Chlorophyll-a gaps over the Mediterranean Sea

2014 ◽  
Author(s):  
Andreas Nikolaidis ◽  
Stavros Stylianou ◽  
Georgios Georgiou ◽  
Diofantos Hadjimitsis ◽  
Evangelos Akylas
Keyword(s):  
Mediterranean Sea ◽  
Chlorophyll A ◽  
Satellite Data ◽  
The Mediterranean

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 Phytoplankton Diversity in the Mediterranean Sea From Satellite Data Using Self‐Organizing Maps

2019 ◽  
Vol 124 (8) ◽  
pp. 5827-5843 ◽  
Author(s):  
Roy El Hourany ◽  
Marie Abboud‐Abi Saab ◽  
Ghaleb Faour ◽  
Carlos Mejia ◽  
Michel Crépon ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Satellite Data ◽  
Self Organizing Maps ◽  
Phytoplankton Diversity ◽  
The Mediterranean ◽  
Self Organizing

scholarly journals Modelling the Vertical Distribution of Phytoplankton Biomass in the Mediterranean Sea from Satellite Data: A Neural Network Approach

2018 ◽  
Vol 10 (10) ◽  
pp. 1666 ◽  
Author(s):  
Michela Sammartino ◽  
Salvatore Marullo ◽  
Rosalia Santoleri ◽  
Michele Scardi
Keyword(s):  
Neural Network ◽  
Mediterranean Sea ◽  
Satellite Data ◽  
Chemical Properties ◽  
Temporal Scales ◽  
Neural Network Approach ◽  
Spatial And Temporal Scales ◽  
Highly Nonlinear ◽  
The Mediterranean

Knowledge of the vertical structure of the bio-chemical properties of the ocean is crucial for the estimation of primary production, phytoplankton distribution, and biological modelling. The vertical profiles of chlorophyll-a (Chla) are available via in situ measurements that are usually quite rare and not uniformly distributed in space and time. Therefore, obtaining estimates of the vertical profile of the Chla field from surface observations is a new challenge. In this study, we employed an Artificial Neural Network (ANN) to reconstruct the 3-Dimensional (3D) Chla field in the Mediterranean Sea from surface satellite estimates. This technique is able to reproduce the highly nonlinear nature of the relationship between different input variables. A large in situ dataset of temperature and Chla calibrated fluorescence profiles, covering almost all Mediterranean Sea seasonal conditions, was used for the training and test of the network. To separate sources of errors due to surface Chla and temperature satellite estimates, from errors due to the ANN itself, the method was first applied using in situ surface data and then using satellite data. In both cases, the validation against in situ observations shows comparable statistical results with respect to the training, highlighting the feasibility of applying an ANN to infer the vertical Chla field from surface in situ and satellite estimates. We also analyzed the usefulness of our approach to resolve the Chla prediction at small temporal scales (e.g., day) by comparing it with the most widely used Mediterranean climatology (MEDATLAS). The results demonstrated that, generally, our method is able to reproduce the most reliable profile of Chla from synoptical satellite observations, thus resolving finer spatial and temporal scales with respect to climatology, which can be crucial for several marine applications. We demonstrated that our 3D reconstructed Chla field could represent a valid alternative to overcome the absence or discontinuity of in situ sampling.

Wave height characteristics in the Mediterranean Sea by means of numerical modeling, satellite data, statistical and geometrical techniques

2011 ◽  
Vol 33 (1) ◽  
pp. 1-15 ◽  
Author(s):  
George Galanis ◽  
Dan Hayes ◽  
George Zodiatis ◽  
Peter C. Chu ◽  
Yu-Heng Kuo ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Mediterranean Sea ◽  
Wave Height ◽  
Satellite Data ◽  
The Mediterranean

Combining model and geostationary satellite data to reconstruct hourly SST field over the Mediterranean Sea

2014 ◽  
Vol 146 ◽  
pp. 11-23 ◽  
Author(s):  
S. Marullo ◽  
R. Santoleri ◽  
D. Ciani ◽  
P. Le Borgne ◽  
S. Péré ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Satellite Data ◽  
Geostationary Satellite ◽  
The Mediterranean
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