X-band Azimuth cut-off for wind speed retrieval by means of COSMO-SkyMed SAR data

2012 ◽  
Author(s):  
M. Migliaccio ◽  
A. Montuori ◽  
F. Nunziata
Keyword(s):  
Wind Speed ◽  
X Band ◽  
Sar Data

scholarly journals X-band COSMO-SkyMed wind field retrieval, with application to coastal circulation modeling

Ocean Science ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 121-132 ◽  
Author(s):  
A. Montuori ◽  
P. de Ruggiero ◽  
M. Migliaccio ◽  
S. Pierini ◽  
G. Spezie
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Circulation Model ◽  
Coastal Circulation ◽  
Wind Fields ◽  
Sea Surface Wind ◽  
X Band ◽  
Sar Data ◽  
Ecmwf Model

Abstract. In this paper, X-band COSMO-SkyMed© synthetic aperture radar (SAR) wind field retrieval is investigated, and the obtained data are used to force a coastal ocean circulation model. The SAR data set consists of 60 X-band Level 1B Multi-Look Ground Detected ScanSAR Huge Region COSMO-SkyMed© SAR data, gathered in the southern Tyrrhenian Sea during the summer and winter seasons of 2010. The SAR-based wind vector field estimation is accomplished by resolving both the SAR-based wind speed and wind direction retrieval problems independently. The sea surface wind speed is retrieved by means of a SAR wind speed algorithm based on the azimuth cut-off procedure, while the sea surface wind direction is provided by means of a SAR wind direction algorithm based on the discrete wavelet transform multi-resolution analysis. The obtained wind fields are compared with ground truth data provided by both ASCAT scatterometer and ECMWF model wind fields. SAR-derived wind vector fields and ECMWF model wind data are used to construct a blended wind product regularly sampled in both space and time, which is then used to force a coastal circulation model of a southern Tyrrhenian coastal area to simulate wind-driven circulation processes. The modeling results show that X-band COSMO-SkyMed© SAR data can be valuable in providing effective wind fields for coastal circulation modeling.

scholarly journals X-band COSMO-SkyMed wind field retrieval, with application to coastal circulation modeling

2012 ◽  
Vol 9 (5) ◽  
pp. 3251-3279 ◽  
Author(s):  
A. Montuori ◽  
P. de Ruggiero ◽  
M. Migliaccio ◽  
S. Pierini ◽  
G. Spezie
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Surface Wind ◽  
Coastal Circulation ◽  
Wind Fields ◽  
Sea Surface Wind ◽  
X Band ◽  
Circulation Modeling ◽  
Sar Data ◽  
Ecmwf Model

Abstract. In this paper, X-band COSMO-SkyMed© SAR wind field retrieval is investigated to force coastal circulation modeling. The SAR data set consists of 60 X-band Level 1B Multi-Look Ground Detected ScanSAR Huge Region COSMO-SkyMed© SAR data, gathered in the Southern Tyrrhenian Sea during the Summer and Winter seasons of 2010. The SAR-based wind vector field estimation is accomplished by resolving both the SAR-based wind speed and wind direction retrieval problems independently. The sea surface wind speed is retrieved by means of a SAR wind speed algorithm based on the Azimuth cut-off procedure, while the sea surface wind direction is provided by means of a SAR wind direction algorithm based on the Discrete Wavelet Transform Multi-Resolution Analysis. The obtained wind fields are compared with ground truth data provided by both ASCAT scatterometer and ECMWF model wind fields. SAR-derived wind vector fields and ECMWF model wind data are used to construct a blended wind product regularly sampled in both space and time, which is then used to force a coastal circulation model of a Southern Tyrrhenian coastal area to simulate wind-driven circulation processes. The modeling results clearly show that X-band COSMO-SkyMed© SAR data can be valuable in providing effective wind fields for coastal circulation modeling.

scholarly journals A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

2021 ◽  
Vol 13 (3) ◽  
pp. 360
Author(s):  
Wensheng Wang ◽  
Martin Gade ◽  
Kerstin Stelzer ◽  
Jörn Kohlus ◽  
Xinyu Zhao ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Wadden Sea ◽  
Relative Difference ◽  
Polarimetric Synthetic Aperture Radar ◽  
Intertidal Flats ◽  
Sediment Types ◽  
X Band ◽  
Good Potential ◽  
L Band ◽  
Sar Data

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

scholarly journals Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

2017 ◽  
Vol 11 (2) ◽  
pp. 755-771 ◽  
Author(s):  
Ane S. Fors ◽  
Dmitry V. Divine ◽  
Anthony P. Doulgeris ◽  
Angelika H. H. Renner ◽  
Sebastian Gerland
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
First Year ◽  
Backscatter Signal ◽  
Noise Floor ◽  
Ice Melt ◽  
Wind Speeds ◽  
Melt Pond ◽  
Melt Ponds ◽  
X Band

Abstract. In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6. 2 m s−1), with a Spearman's correlation coefficient of 0. 46. At low wind speeds (0. 6 m s−1), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of −0. 53. To further investigate these relations, regression fits were made both for the intermediate (R2fit = 0. 21) and low (R2fit = 0. 26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4 % from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51 × 51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above  ∼  40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future.

X-band Terrasar-X and COSMO-SkyMed SAR data for bare soil parameters estimation

2014 ◽  
Author(s):  
Azza Gorrab ◽  
Mehrez Zribi ◽  
Nicolas Baghdadi ◽  
Bernard Mougenot ◽  
Zohra Lili Chabaane
Keyword(s):  
Bare Soil ◽  
Parameters Estimation ◽  
Soil Parameters ◽  
X Band ◽  
Sar Data
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