The retrieval and analysis of sea surface suspended sediment movement based on multi-source remote sensing data

2011 ◽  
Author(s):  
Yin Cai
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Sensing Data ◽  
Sediment Movement

Research on the Coastal Suspended Sediment Movement of Matakong Island in Guinea, Africa Based on Remote Sensing Data

2013 ◽  
Vol 333-335 ◽  
pp. 275-279
Author(s):  
Yin Cai ◽  
Hong Bo Zhao ◽  
Shu Hua Zuo
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Remote Sensing Data ◽  
Sediment Concentration ◽  
Concentration Data ◽  
Sediment Distribution ◽  
Sensing Data ◽  
Sediment Movement ◽  
Wide Range

A wide range of suspended sediment concentration can be obtained by satellite remote sensing. According to the multi-temporal remote sensing data and quasi-simultaneously surface sediment concentration data, research works on the surface suspended sediment distribution and movement trends of Matakong coastal area, Africa were carried out. The results showed that the suspended sediment concentration of the studied area is not large, and the sediment movement is not active. The sediment source comes from the nearshore shallow flats, where they could be entrained by the wind waves and then diffuses by the tidal currents.

scholarly journals Remote radiological assessment in the marine environment: A pilot study based on Cs-137 measurements and satellite observations in the Aegean Sea

2019 ◽  
Vol 24 ◽  
pp. 191
Author(s):  
G. Mavrokefalou ◽  
H. Florou ◽  
O. Sykioti
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Data ◽  
Sea Water ◽  
Remote Sensing Data ◽  
Aegean Sea ◽  
137Cs Activity ◽  
Sea Surface ◽  
Sensing Data ◽  
Activity Concentrations

A program concept has been developed to utilize sea parameters like sea surface temperature (SST), ocean colour (OC) and sea surface salinity (SSS), in order to explore their potential relations with 137Cs activity concentrations in sea water. These relations are expected to lead to the creation of an innovative tool based on remote sensing data and in real time 137Cs measurements, for the remote radioactivity detection of the Greek marine ecosystem both for routine control and emergency recordings. The presented results are a preliminary effort of the tool’s development. Remote sensing data have been acquired from MIRAS and MODIS instruments on-board ESA-SMOS and NASA-TERRA/AQUA satellites respectively. Satellite data comprise of SST and OC measurements. The ERL’s data of 137Cs activity concentrations (204 measurements) in seawater have been used for the period March 2012 to February 2015. Therefore, a) map analyses in a GIS including interpolation and integration of 83 real time measurements corrected with the effective half live of 7.2 y according to the monthly data of satelites and spatial linear regression have been implemented for the Aegean Sea, b) additional temporal analyses using linear and polynomial regression have been performed for the area of Souda- Crete, for which the most frequent measurements of 137Cs activity concentration in sea water have been measured in ERL. In this study, the first derived results on the correlation between SST measurements with 137Cs activity concentrations are presented, whereas the respective correlation with OC is being under invstigation. Further investigations include multivariate polynomial analyses into the Geographic Information System (GIS) platform with more extensive sampling and satellite data from new systems, whereas comparative correlations of 137Cs with seawater parameters derived by conventional means will be performed.

scholarly journals Removing Striping Noise from Cloudy Level 2 Sea Surface Temperature and Ocean Color Datasets

2016 ◽  
Author(s):  
Brahim Boussidi ◽  
Ronan Fablet ◽  
Bertrand Chapron
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Color ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Discrete Wavelet ◽  
Sensing Data ◽  
Fourier Filtering

This paper introduces a new destriping algorithm for remote sensing data. The method is based on combined Haar Stationary Wavelet transform and Fourier filtering. State-of-the-Art methods based on the discrete wavelet transform (DWT) may not always be effective and may cause different artifacts. Our contribution is three-fold: i) we propose to use the Undecimated Wavelet transform (UWT) to avoid as much as possible shortcomings of the classical DWT; ii) we combine a spectral filtering and UWT using the simplest possible wavelet, the Haar basis, for a computational efficiency; iii) we handle 2D fields with missing data, as commonly observed in ocean remote sensing data due to atmospheric conditions (e.g., cloud contamination). The performances of the proposed filter are tested and validated on the suppression of horizontal strip artifacts in cloudy L2 Sea Surface Temperature (SST) and ocean color snapshots.

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