Ocean surface wind speed retrieve from co-polarized SAR using composite surface bragg scattering model

He FANG; Jingsong YANG; Gaofeng FAN; Chao LI; Dawei SHI; Perrie WILLIAM

doi:10.11834/jrs.20210143

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Ocean surface wind speed retrieve from co-polarized SAR using composite surface bragg scattering model
Vol. 26, Issue 6, Pages: 1274-1287(2022)
Published： 07 June 2022 ，
DOI： 10.11834/jrs.20210143

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方贺，杨劲松，樊高峰，李超，史达伟，William Perrie.2022.组合表面Bragg散射模型共极化SAR海表面风速反演.遥感学报，26（6）： 1274-1287

Fang H，Yang J S，Fan G F，Li C， Shi D W and William P. 2022. Ocean surface wind speed retrieve from co-polarized SAR using composite surface bragg scattering model. National Remote Sensing Bulletin， 26（6）：1274-1287
方贺，杨劲松，樊高峰，李超，史达伟，William Perrie.2022.组合表面Bragg散射模型共极化SAR海表面风速反演.遥感学报，26（6）： 1274-1287 DOI： 10.11834/jrs.20210143.

Fang H，Yang J S，Fan G F，Li C， Shi D W and William P. 2022. Ocean surface wind speed retrieve from co-polarized SAR using composite surface bragg scattering model. National Remote Sensing Bulletin， 26（6）：1274-1287 DOI： 10.11834/jrs.20210143.

摘要

组合表面布拉格散射模型CSBS（Composite Surface Bragg Scattering）由布拉格（Bragg）散射模型和几何光学模型组成，是海洋微波散射的经典模型，可用于星载合成孔径雷达SAR（Synthetic Aperture Radar）海表面风场反演。研究指出，Bragg散射模型仅适用于中等入射角条件，几何光学模型则更适用于小入射角情形。然而，如何确定中等和小入射角的阈值，即CSBS模型最优入射角的选取目前尚无定论。基于142景成像于美国东西海岸和中国东海的RADARSAT-2精细四极化SAR影像数据和海洋浮标数据，本文提出了一种最优局地入射角查找算法，分别对VV和HH极化SAR数据进行最优入射角阈值的选取。结果显示，局地入射角

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分别为VV和HH极化影像CSBS模型反演风速最优入射角。基于最优入射角的选取，本文在

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海况区间内利用CSBS模型对VV和HH极化SAR影像开展风速反演实验，并将反演风速与浮标风速进行对比。结果显示，基于VV和HH极化数据的CSBS模型反演风速与浮标风速均方根误差分别为2.15 m/s和2.32 m/s，相关系数分别为0.79和0.75，两者具有良好的一致性。本文研究结论表明基于最优入射角设置后的CSBS模型在海面风速小于15 m/s条件下具有良好的应用性，后续研究将更加关注CSBS模型在高海况以及交叉极化SAR数据情况下的应用。

Abstract

Composite Surface Bragg Scattering (CSBS) model is a classical ocean microwave scattering model

which can describe the Normalized Radar Cross Section (NRCS) of microwave backscattering from a rough ocean surface. The CSBS model includes a Bragg model and a geometric optics model and can be used to retrieve ocean surface wind speed from spaceborne synthetic aperture radar (SAR). Compared with Geophysical Model Function (GMF) developed by methods of empirical statistics

the CSBS model works well at all microwave frequencies. Reports showed that geometric optics model is most suitable for small local incidence angles

whereas Bragg model tends to be best for moderate incidence angles. In other words

for local radar incidence angles that are smaller than a given angle setting

the two-scale backscattering mechanism of the sea surface is replaced by a geometric optic solution for specular reflection. However

determining the threshold for small and moderate local incidence angles is still an open question. The local incidence angle search algorithm is proposed and developed to find the optimal radar incidence angle at co-polarized (VV-and HH-polarized) channel. The modeling data for the local incidence angle search algorithm include wind speed data retrieved from 142 Canada RADARSAT-2 fine-beam quad-polarized SAR images in the east coast of America

the west coast of America

and the East China Sea. Ocean surface wind speed measured from the National Data Buoy Center (NDBC)

the Environment and Climate Change Canada (ECCC) and the China State Oceanic (SOA) are considered reference wind speed. The conclusion shows that the optimal setting of 14 and 16 degrees is the optimal radar incidence angle for ocean surface wind speeds retrieve from CSBS model at VV-and HH-polarized RADARSAT-2 SAR images. Based on the optimal incidence angle setting

ocean buoy-measured wind speed data are considered references

and ocean surface wind speed is retrieved from VV-and HH-polarized RADARSAT-2 SAR data using CSBS model at 0—15 m/s wind condition. Results show that the ocean surface wind speeds retrieved from RADARSAT-2 fine-beam quad-polarized SAR data using CSBS model at VV- and HH-polarized channel are in good agreement with in situ ocean buoy wind speed. The root mean square error (RMSE) of SAR-retrieved wind speed and buoy-measured wind speed are 2.15 m/s (VV-polarized channel) and 2.32 m/s (HH-polarized channel)

and the correlation coefficients are 0.79 (VV-polarized channel) and 0.75 (HH-polarized channel)

which are statistically significant at 99.9 significance level. The conclusion of this article indicated that the optimized incidence angle setting of the CSBS model found in our study has good applicability and reliability under low-to-moderate ocean surface wind speed (no higher than 15 m/s). From case comparisons of the CBS model with RADARSAT-2 SAR images

the optimized small incidence angle setting of 14 and 16 degrees of the CBS model is suitable for the microwave frequency of the C-band with co-polarization. More studies on the optimized small incidence angle setting of the CBS model and its application in other microwave frequencies

cross polarizations

or high sea states will be considered in future investigations.

关键词

遥感电磁散射模型合成孔径雷达海表面风速地球物理函数模型

Keywords

remote sensingelectromagnetic modelsynthetic aperture radarsea surface wind speedgeophysical model functions

references

Ai W H, Chen G Y, Ge S R, Yuan L F and Zhao X B. 2019. Method for ocean surface wind field retrieval from a common reflector dual-frequency SAR. Journal of Remote Sensing, 23(3): 408-417

艾未华, 陈冠宇, 戈书睿, 袁凌峰, 赵现斌. 2019. 双频共面合成孔径雷达海面风场反演. 遥感学报, 23(3): 408-417 [DOI: 10.11834/jrs.20197154http://dx.doi.org/10.11834/jrs.20197154]

Barrick D E. 1968. Rough surface scattering based on the specular point theory. IEEE Transactions on Antennas and Propagation, 16(4): 449-454 [DOI: 10.1109/TAP.1968.1139220http://dx.doi.org/10.1109/TAP.1968.1139220]

Bentamy A, Mouche A, Grouazel A, Moujane A and Mohamed A A. 2019. Using sentinel-1A SAR wind retrievals for enhancing scatterometer and radiometer regional wind analyses. International Journal of Remote Sensing, 40(3/4): 1120-1147 [DOI: 10.1080/01431161.2018.1524174http://dx.doi.org/10.1080/01431161.2018.1524174]

Cox C and Munk W. 1954. Measurement of the roughness of the sea surface from photographs of the sun’s glitter. Journal of the Optical Society of America, 44(11): 838-850 [DOI: 10.1364/JOSA.44.000838http://dx.doi.org/10.1364/JOSA.44.000838]

Donelan M A and Pierson W J. 1987. Radar scattering and equilibrium ranges in wind-generated waves with application to scatterometry. Journal of Geophysical Research: Oceans, 92(C5): 4971-5029 [DOI: 10.1029/JC092iC05p04971http://dx.doi.org/10.1029/JC092iC05p04971]

Du P J. 2002. Research on filtering of RADARSAT images. Journal of China University of Mining and Technology, 31(2): 132-137

杜培军. 2002. RADARSAT图象滤波的研究. 中国矿业大学学报, 31(2): 132-137

Fang H, Xie T, Perrie W, Zhang G S, Yang J S and He Y J. 2018. Comparison of C-band quad-polarization synthetic aperture radar wind retrieval models. Remote Sensing, 10(9): 1448 [DOI: 10.3390/rs10091448http://dx.doi.org/10.3390/rs10091448]

Fang H, Xie T, Zhou Y F, Yang J S, Zhao L, He Y J and Perrie W. 2018. Evaluation of SAR wind speed retrieved based on C-2PO model and CMOD5. N geophysical model functions. Haiyang Xuebao, 40(9): 103-112

方贺, 谢涛, 周育锋, 杨劲松, 赵立, 何宜军, Perrie W. 2018. 基于C-2PO模型和CMOD5. N地球物理模式函数的SAR风速反演性能评估. 海洋学报, 40(9): 103-112 [DOI: 10.3969/j.issn.0253-4193.2018.09.009http://dx.doi.org/10.3969/j.issn.0253-4193.2018.09.009]

Hersbach H. 2010. Comparison of C-band scatterometer CMOD5. N equivalent neutral winds with ECMWF. Journal of Atmospheric and Oceanic Technology, 27(4): 721-736 [DOI: 10.1175/2009JTECHO698.1http://dx.doi.org/10.1175/2009JTECHO698.1]

Hwang P A, Zhang B, Toporkov J V and Perrie W. 2010. Comparison of composite Bragg theory and quad-polarization radar backscatter from RADARSAT-2: with applications to wave breaking and high wind retrieval. Journal of Geophysical Research: Oceans, 115(C8): C08019 [DOI: 10.1029/2009JC005995http://dx.doi.org/10.1029/2009JC005995]

La T V, Khenchaf A, Comblet F and Nahum C. 2018. Assessment of wind speed estimation from C-band sentinel-1 images using empirical and electromagnetic models. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 4075-4087 [DOI: 10.1109/TGRS. 2018.2822876http://dx.doi.org/10.1109/TGRS.2018.2822876]

Meissner T and Wentz F J. 2004. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Transactions on Geoscience and Remote Sensing, 42(9): 1836-1849 [DOI: 10.1109/TGRS.2004.831888http://dx.doi.org/10.1109/TGRS.2004.831888]

Plant W J. 1986. A two-scale model of short wind-generated waves and scatterometry. Journal of Geophysical Research, 91(C9): 10735-10749 [DOI: 10.1029/JC091iC09p10735http://dx.doi.org/10.1029/JC091iC09p10735]

Plant W J. 2002. A stochastic, multiscale model of microwave backscatter from the ocean. Journal of Geophysical Research: Oceans, 107(C9): 3120 [DOI: 10.1029/2001JC000909http://dx.doi.org/10.1029/2001JC000909]

Ren L, Yang J S, Mouche A A, Wang H, Zheng G, Wang J, Zhang H G, Lou X L and Chen P. 2019. Assessments of ocean wind retrieval schemes used for Chinese gaofen-3 synthetic aperture radar Co-polarized data. IEEE Transactions on Geoscience and Remote Sensing, 57(9): 7075-7085 [DOI: 10.1109/TGRS.2019.2911325http://dx.doi.org/10.1109/TGRS.2019.2911325]

Shao W Z, Sheng Y X and Sun J. 2017. Preliminary assessment of wind and wave retrieval from Chinese Gaofen-3 SAR imagery. Sensors, 17(8): 1705 [DOI: 10.3390/s17081705http://dx.doi.org/10.3390/s17081705]

Thompson D R, Horstmann J, Mouche A, Winstead N S, Sterner R and Monaldo F M. 2012. Comparison of high-resolution wind fields extracted from TerraSAR-X SAR imagery with predictions from the WRF mesoscale model. Journal of Geophysical Research: Oceans, 117(C2): C02035 [DOI: 10.1029/2011JC007526http://dx.doi.org/10.1029/2011JC007526]

Valenzuela G R. 1967. Depolarization of EM waves by slightly rough surfaces. IEEE Transactions on Antennas and Propagation, 15(4): 552-557 [DOI: 10.1109/TAP.1967.1138962http://dx.doi.org/10.1109/TAP.1967.1138962]

Valenzuela G R. 1978. Theories for the interaction of electromagnetic and oceanic waves-A review. Boundary-Layer Meteorology, 13(1): 61-85 [DOI: 10.1007/BF00913863http://dx.doi.org/10.1007/BF00913863]

Xu F and Jia F. 1996. An algorithm of sea surface radar scatter applicable to wide frequency band. Remote Sensing Technology and Application, 11(3): 26-30

徐丰, 贾复. 1996. 适用于不同频率的微波海面散射计算方法. 遥感技术与应用, 11(3): 26-30

Yang J S, Huang W G, Zhou C B, Fu B, Shi A Q and Li D L. 2001. Coastal ocean surface wind retrieval from SAR imagery. Journal of Remote Sensing, 5(1): 13-16

杨劲松, 黄韦艮, 周长宝, 傅斌, 史爱琴, 厉冬玲. 2001. 合成孔径雷达图像的近岸海面风场反演. 遥感学报, 5(1): 13-16 [DOI: 10.11834/jrs.20010103http://dx.doi.org/10.11834/jrs.20010103]

Ye X M, Lin M S, Song Q T, Liao F, Liang C and Zhou X. 2019. Comparisons of composite radar backscattering model and sea surface radar backscatter from synthetic aperture radar, scatterometer and altimeter. Haiyang Xuebao, 41(7): 123-135

叶小敏, 林明森, 宋清涛, 廖菲, 梁超, 周旋. 2019. 复合雷达后向散射模型与合成孔径雷达、散射计和高度计海面雷达后向散射观测的比较分析. 海洋学报, 41(7): 123-135 [DOI: 10.3969/j.issn.0253-4193.2019.07.011http://dx.doi.org/10.3969/j.issn.0253-4193.2019.07.011]

Zhang B, Perrie W and He Y J. 2011. Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model. Journal of Geophysical Research: Oceans, 116(C8): C08008 [DOI: 10.1029/2010JC006522http://dx.doi.org/10.1029/2010JC006522]

Zhang G S, Li X F, Perrie W, Hwang P A, Zhang B and Yang X F. 2017. A hurricane wind speed retrieval model for C-band RADARSAT-2 cross-polarization ScanSAR images. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4766-4774 [DOI: 10.1109/TGRS.2017.2699622http://dx.doi.org/10.1109/TGRS.2017.2699622]

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