Tran Vu La

Exploitation of C-Band Sentinel-1 Images for High-Resolution Wind Field Retrieval in Coastal Zones (Iroise Coast, France)

Synthetic aperture radar (SAR) is one of the favorite tools for earth observation applications, i.e., oceanography, land use mapping, climate change since this device can offer the data at a high spatial resolution and in most meteorological conditions. This is more significant when the data acquired by the Sentinel-1, a new C-band satellite, are exploited. For high-resolution wind field extraction, two different approaches are proposed. In the scatterometry-based approach, wind direction is first extracted by the local gradient method at different scales, i.e., 1–5-km wind resolutions. It is then applied to the empirical geophysical model functions, i.e., CMOD (C-band), for surface wind speed estimation. The advantage of this approach is to deliver accurate wind speed estimates in the range of 2–25 m/s from different SAR data. However, it requires wind direction as an input parameter. This can lead to errors in wind speed estimation due to uncertain wind directions. Therefore, for comparison, in the second approach, we propose the use of the model without wind direction input proposed by Komarov et al. In general, the obtained wind fields based on two proposed approaches are quite similar, and they have good agreement with in situ measurements from the meteorological stations along the Iroise coast.

Study of inversion EM models for wind speed retrieval from Sentinel-1 data

Sea surface wind speed plays a key parameter in the studies of many oceanic applications, i.e. meteorological forecasting, oil slick observation, ship detection, and wind turbine installation recently. It can be obtained from many available wind sources, i.e. measured data, numeric weather models, etc. However, one of the most well-known ways is the retrieval of wind speed from Synthetic Aperture Radar (SAR) data. For this approach, the studies are based on two principal ways: one uses empirical models and the other is based on electromagnetic calculations. In both indicated approaches, the Geophysical Model Functions (GMFs) are used to describe the dependency of radar scattering from sea surface on surface wind speed and the geometry of observations. By knowing radar scattering and geometric parameters from SAR data, it is possible to invert the GMFs to retrieve wind speed. Then, estimated wind speed by two studied models is compared and evaluated with measured data. Based on the comparisons, the advantages and limits of the studied models are analyzed and discussed.

Comparison of empirical and electromagnetic geophysical model function for near-surface wind speed retrieval

Despite based on different approaches and objectives, it is reasonable to compare near-surface wind speed estimated by the empirical (EP) and electromagnetic (EP) geophysical model function (GMF), since both of them describe the relation between radar scattering and wind vector (directly for EP GMF and via surface roughness spectrum for EM GMF). In general, the EP and EM models give quite similar normalized radar cross section (NRCS) for radar incidence angle below 40°. Consequently, wind speed estimated by the EP and EM GMF is very close. However, for incidence angles above 40°, the EM models show poor performance of wind speed estimation.

Pirical approach for C-band VV-polarization wind vector retrieval from Sentinel-1 images

Based on an empirical model without wind direction input for the retrieval of C-band HH-polarization wind speed, we propose a modified model for wind speed estimation in VV-polarization. The obtained wind speed is then applied for the CMOD5.N to estimate wind directions. The comparisons with the scatterometry-based approach demonstrate that the estimated wind speed by the proposed model is closer to in situ measurements than that obtained with the CMOD5.N. Likewise, the extracted wind directions from the CMOD5.N are more accurate that those obtained with the local gradient method.

Overview of surface wind speed retrieval from C-band SAR images: Empirical and electromagnetic approaches

In spite of the difference in description, it is reasonable to compare sea surface wind speed estimates based on empirical (EP) and electromagnetic (EM) approaches, since both of them describe the relation between radar backscattering and wind parameters, directly for EP models and via sea surface roughness spectrum for EM models. For EP approach, two methods are presented for wind speed estimation: scatterometry and model without wind direction input. For EM approach, the approximation models, i.e. small-slope approximation (SSA) and resonant curvature approximation (RCA), are presented since they can calculate radar backscattering close to that given by the EP models. The comparison between EP and EM models demonstrate that estimated wind speeds by two approaches are quite similar, especially for radar incidence angles below 40°.

Sensitivity of sea wind direction retrieval from Sentinel-1 data with regard to spatial resolution and speckle noise

Wind direction is a crucial parameter in many inversion models to estimate wind speed from Synthetic Aperture Radar (SAR) data. Compared to the other available wind sources, i.e. measured data, numeric weather data, etc., the retrieval of wind directions from SAR data is more widely used, since it can give wind directions at different scales. Nevertheless, there are not a lot of studies which report about the sensitivity of wind direction retrieval, particularly with regard to the spatial resolution (or acquisition mode) of SAR images, speckle noise, and wind regimes. In order to investigate this issue, the Local Gradient method is selected to retrieve wind directions from the Sentinel-1 images at different scales, since it can give high wind resolution cells. Then, the impact of speckle noise and wind regimes on retrieved wind directions is assessed.

Comparison of inversion models of wind speed retrieval from C-band Sentinel-1 and X-band TerraSAR-X data

Retrieval of sea surface wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give higher resolutions than the other available surface wind sources. For this approach, two principal methods can be found: one is based on electromagnetic (EM) models and the other is based on empirical (EP) ones. In both indicated ways, the Geophysical Model Functions (GMFs) are used to describe the dependency of radar scattering on wind speed and the geometry of observations. By knowing radar scattering and geometric parameters from SAR data, it is possible to invert the GMFs to retrieve sea wind speed. Wind speed estimated by two studied models is compared together and evaluated with measured data. Based on the comparisons, the advantages and limits of the studied models are analyzed and discussed.

Retrieval of Surface Wind Fields at High Spatial Resolutions from C-band Sentinel-1 Data

Sea surface wind is a key parameter in the studies of many oceanic applications, i.e. meteorological forecasting, oil slick observation, ship detection, wind turbine installation recently, etc. Among available wind sources, i.e. in situ measurement, numeric weather models, etc., the retrieval of wind fields from Synthetic Aperture Radar (SAR) data is selected in this study, since it may give winds at high spatial resolutions. For this approach, wind directions are first retrieved from SAR images by the Local Gradient method. They are then used in the Geophysical Model Functions (GMFs) to estimate wind speed. The GMFs are used to describe the dependency of radar scattering from sea surface on surface wind speed and the geometry of observations. Therefore, by knowing radar scattering and geometric parameters, it is possible to invert GMFs to estimate wind speed. There are two principal approaches to model GMFs: based on empirical (EP) descriptions (so called EP GMFs), and based on electromagnetic (EM) calculations (so called EM GMFs). For an overview, wind speeds are estimated using both EP and EM models. They are then compared with in situ measurements to evaluate. Based on the comparisons, the advantages and inconveniences of the GMFs are analyzed.

Study of sensitivity in wind direction retrieval from Sentinel-1 images

Retrieval of sea wind vector from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cell. For this approach, wind direction is normally the first retrieved parameter, since it plays a crucial role in many inversion models (CMOD, XMOD) to estimate wind speed. In spite of the huge studies of wind field retrieval, little has been reported about the sensitivity of wind direction retrieval at different scales, especially at a wind resolution cell of 1 km × 1 km. This is particularly significant for C-band Sentinel-1 images which have generally high spatial resolutions. In order to investigate this issue, the Local Gradient method is selected to retrieve wind directions from the Sentinel-1 data at different scales, with regard to the spatial resolution (or acquisition mode) of SAR images, speckle noise, and wind regimes.

Assessment of inversion models for sea surface wind speed retrieval from Sentinel-1 data

Among the different available wind sources, i.e. in situ measurements, numeric weather models, the retrieval of wind speed from Synthetic Aperture Radar (SAR) data is one of the most widely used methods, since it can give high wind resolution cell. For this purpose, one can find two principal methods: via electromagnetic (EM) models and empirical (EP) ones. In both approaches, the Geophysical Model Functions (GMFs) are used to describe the relation of radar scattering, wind speed, and the geometry of observations. By knowing radar scattering and geometric parameters, it is possible to invert the GMFs to retrieve wind speed. In this paper, wind speed retrieved by the two different models is compared together and evaluated with measured data. Based on the comparisons, some ideas will be proposed to improve the performance of wind speed retrieval.