scholarly journals An Improved Local Gradient Method for Sea Surface Wind Direction Retrieval from SAR Imagery

2017 ◽  
Vol 9 (7) ◽  
pp. 671 ◽  
Author(s):  
Lizhang Zhou ◽  
Gang Zheng ◽  
Xiaofeng Li ◽  
Jingsong Yang ◽  
Lin Ren ◽  
...  
Keyword(s):  
Gradient Method ◽  
Wind Direction ◽  
Surface Wind ◽  
Sea Surface ◽  
Sea Surface Wind ◽  
Sar Imagery ◽  
Local Gradient

scholarly journals LG-Mod: A Modified Local Gradient (LG) Method to Retrieve SAR Sea Surface Wind Directions in Marine Coastal Areas

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Fabio M. Rana ◽  
Maria Adamo ◽  
Guido Pasquariello ◽  
Giacomo De Carolis ◽  
Sandra Morelli
Keyword(s):  
Wind Direction ◽  
Periodic Structures ◽  
Surface Wind ◽  
Estimation Method ◽  
Residual Effects ◽  
Envisat Asar ◽  
Sea Surface Wind ◽  
Sar Imagery ◽  
Complex Phenomena ◽  
Local Gradient

This paper describes a novel SAR wind direction estimation method based on the computation of local gradients over quasi-linear and quasi-periodic structures detected by SAR imagery. The method relies upon the standard LG method for the part relevant to the computation of the local gradients. The novelty is that the dominant local wind direction and related accuracy are estimated using results derived from the Directional Statistics. The LG-Mod is validated against in situ coastal wind measurements provided by instrumented buoys with 63 ENVISAT ASAR images. Results show an overall agreement with RMSE values obtained for off-shore areas, but residual effects due to the complex phenomena occurring in the proximity of shoreline may degrade the performance when running in automated mode.

Effectiveness of WRF wind direction for retrieving coastal sea surface wind from synthetic aperture radar

Wind Energy ◽  
2012 ◽  
Vol 16 (6) ◽  
pp. 865-878 ◽  
Author(s):  
Yuko Takeyama ◽  
Teruo Ohsawa ◽  
Katsutoshi Kozai ◽  
Charlotte Bay Hasager ◽  
Merete Badger
Keyword(s):  
Synthetic Aperture Radar ◽  
Wind Direction ◽  
Surface Wind ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Sea Surface Wind ◽  
Coastal Sea ◽  
Aperture Radar

scholarly journals Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

2017 ◽  
Vol 34 (9) ◽  
pp. 2001-2020 ◽  
Author(s):  
Yukiharu Hisaki
Keyword(s):  
Wind Direction ◽  
Surface Wind ◽  
Hf Radar ◽  
Sea Surface ◽  
Wave Parameter ◽  
Simple Method ◽  
Wind Speeds ◽  
Wave Prediction ◽  
Sea Surface Wind ◽  
The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

scholarly journals Multi-Scale LG-Mod Analysis for a More Reliable SAR Sea Surface Wind Directions Retrieval

2021 ◽  
Vol 13 (3) ◽  
pp. 410
Author(s):  
Fabio Michele Rana ◽  
Maria Adamo
Keyword(s):  
Gulf Of Maine ◽  
Surface Wind ◽  
Sea Surface ◽  
Sar Images ◽  
Multi Scale ◽  
Sea Surface Wind ◽  
Single Scale ◽  
Local Direction ◽  
High Resolution Images ◽  
Local Gradient

An improved version of the Local-Gradient-Modified (LG-Mod) algorithm for Sea Surface Wind (SSW) directions retrieval by means of Synthetic Aperture Radar (SAR) images is presented. A “local” multi-scale analysis of wind-aligned SAR patterns is introduced to improve the LG-Mod sensitivity to SAR backscattering modulations, occurring locally with various spatial wavelengths. The Marginal Error parameter is redefined, and the adoption of the Directional Accuracy Maximization Criterion (DAMC) allows for the novel Multi-Scale (MS) LG-Mod to automatically select the local processing scale that may be regarded as optimal for pattern enhancement, once a discrete set of scales has been already fixed. Hence, this optimal scale successfully gives evidence to guarantee the best achievable local direction estimation. The assessment of the MS LG-Mod is carried on both simulated SAR images and a Sentinel-1 (S-1) dataset, consisting of 350 Interferometric Wide Swath Ground Range Multi-Look Detected High-Resolution images, which cover the region of the Gulf of Maine. In the latter case, the removal of artifacts and non-wind features from SAR amplitudes is mandatory before directional estimations. In situ wind observations gathered by the National Oceanic and Atmospheric Administration National Data Buoy Center (NOAA NDBC) are exploited for validation. The findings obtained from S-1 data confirm the ones from simulated patterns. The MS LG-Mod analysis performs better than each single-scale one in terms of both percentages of reliable directions and directional Root Mean Square Error (RMSE) values achieved.

scholarly journals Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4451
Author(s):  
Yun Zhang ◽  
Xu Chen ◽  
Wanting Meng ◽  
Jiwei Yin ◽  
Yanling Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wind Direction ◽  
Surface Wind ◽  
Sea Surface ◽  
Support Vector ◽  
Navigation Satellite ◽  
Feature Parameters ◽  
Sea Surface Wind ◽  
Global Navigation ◽  
Global Navigation Satellite

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

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