Significant wave height estimation using azimuth cutoff of C-band RADARSAT-2 single-polarization SAR images

This paper proposes two simple models, look-up table（LUT） model and empirical model, to directly retrieve significant wave height（Hs） using synthetic aperture radar（SAR） azimuth cutoff（λc）. Both models aim at C-band VV, HH, VH, and HV single-polarization SAR images. The LUT model relates Hs to λc, while the empirical model relates Hs to both λc and SAR range-to-velocity（β）. The LUT model coefficients are derived by simulation under different sea states and observation conditions, which depend on incidence angle（θ）, wave direction（dw）, and βbut are independent of polarization. The empirical model coefficients are obtained by fitting the collocated data,which only depend on polarization. To fit empirical model coefficients and validate the two models, C-band RADARSAT-2 fine quad-polarization（VV＋HH＋VH＋HV） single-look complex（SLC） SAR images and collocated buoy data are collected. Retrieved Hs, using Yang model and the two models proposed in this paper from four kinds of polarization SAR data, are compared with buoy Hs. Results show that both LUT and empirical models have the capacity of retrieving Hs from C-band RADARSAT-2 co-polarization SAR data, while Yang model is not suitable for these kinds of SAR data. Moreover, the empirical model is also valid for cross-polarization SAR data showing clear ocean wave stripes.

SAR Azimuth Cut-off to Estimate Wind Speed under High Wind Regimes

In this study,the azimuth cut-off method,typically used for SAR moderate wind speed estimation purposes,is analyzed under high wind regimes.Firstly,the importance of the pixel spacing,the size of the boxes selected for Synthetic Aperture Radar(SAR)image partitioning and the image texture in terms of homogeneities are discussed by considering their influence on the azimuth cut-off(λc)estimation.Secondly,a quality control analysis of the reliability ofλc is carried out by evaluating the distance between the autocorrelation functions(ACF)and their correspondent fittings.This analysis points out the importance of filtering out the unreliable and unfeasibleλc values in order to improve the wind speed estimation.The quality control procedure is based on a x2 test,applied on a large Sentinel-1 A dataset.The soundness of the test is verified by an increment in terms of correlation betweenλc estimations and wind speed values.This approach is,then,applied under high wind regimes,i.e.,tropical cyclones.

基于Sentinel-1A的全球有效波高的反演研究

本文利用神经网络的技术手段,针对Sentinel-1A二级波模式数据提出一种用于海浪有效波高(Hs)反演的模型--N_N模型。该模型在基于ERS2 SAR波模数据开发的双参数模型的基础上,加入经度、纬度、方位向截断波长(λ_c)、图像偏斜(skewness,skew)、图像峰度(kurtosis,kurt)、卫星平台距目标物的距离与卫星飞行速度之比(β)等其他参数信息,根据不同输入参数的组合,建立了14个模型用于Hs反演,旨在分析各参数对有效波高反演的影响。通过分析表明,14个N_N模型相关系数都在0.8以上。随着λ_c、β参数的加入,N_N模型性能均大幅上升,且λ_c参数对模型性能的改善作用更加明显,相关系数提升0.06左右,均方根误差(Root Mean Squared Error,RMSE)下降0.12m左右。另外,skew与kurt的加入也使N_N模型性能有所改善,RMSE下降0.03m左右,相关系数提升0.01左右。其中,N_N10模型效果最佳且性能最稳定,与欧洲中程天气预测中心(the European Centre for Medium-Range Weather Forecasts,ECMWF)数据对比,相关系数(CORR)达到0.905,散射指数(Scattering Index,SI)与RMSE最低,分别为18.74%、0.502m,与独立测量的浮标数据的相关系数达到了0.894。