ASAR海洋锋面图像性质与海洋要素的关系

Relationship between Nature of ASAR Image and Marine Variable Profiles over Ocean Fronts

本研究利用ENVISAT卫星搭载的先进的合成孔径雷达(ASAR)的波模式数据,结合AVHRRSST海表面温度数据,以及NCDC-SEAWINDS海表面风速数据,查找包含温度锋面的ASAR图像,建立标准化的后向散射截面(σ_c)、海表面温度(SST)、海表面风速(SSW)时空匹配的数据集,对各要素进行统计,得出SST、SSW对σ_c影响的定性分析。结果显示,约有60%的锋面样本SST高的一侧σ_c值高,约有70%的锋面样本SSW高的一侧后σ_c高。本研究对锋面两侧温度、风速强弱关系均符合理想状态(SST)高的一侧σ_c值高,SSW高的一侧后σ_c高)的锋面样本进行统计分析。在锋面两侧海洋要素差异对锋面SAR图像的影响方面,发现海表面温度差(ΔSST)、海表面风速差(ΔSSW)高的锋面,两侧表现出高的后向散射截面差(Δσ_c)。在锋面处海洋要素平均水平对SAR图像的影响方面,平均海表面温度(SST)高的锋面,两侧表现出高的,相反,平均风速(SSW)高的锋面,两侧表现出低的Δσ_c。将统计样本的Δσ_c以及锋面两侧风速代入后向散射截面的幂指函数,计算出每个锋面样本的风速系数(B),对B和我们假设的海洋要素系数(B′)进行多项式拟合后发现,B与锋面的B′有较好的相关性,由此可以推断理想状态下SST通过SST和ΔSST产生的风影响后向散射截面,样本统计与公式推导的结论一致。上述研究结果为SAR的海洋锋要素反演打下了基础。

In this study, we combined the wave mode data from Advanced Synthetic Aperture Radar （ASAR） boarded on ENVISAT satellite and AVHRR sea surface temperature （SST）and NCDC sea surface wind （SSW） data. The datasetsincluding normalized hackscattering cross section （σ0）, SST and SSW were established on the thermal fronts we screened. After gathering statistics of the variable pro- files, we carried out qualitative analysis on the influence of SST and SSW on σ0. The results showed that about 60% of the front samples had a higher σ0 in the side of higher SST and about 70% of the front samples had a higher in the side of higher SSW. In this study, we only analyzed the front samples whose SST and SSW situations conformed to the ideal state （higher SST corresponding higher , and SSW is the same）. We found that marine variable profiles differenceinfluencedSAR image, and great temperature （ASST） and wind speed （ASSW） differences always corresponded with great σ0 differences （△σ0） in image. The mean level of marine variable profiles also influenced Aσo, a higher mean sea surface temperature （SST）in the front will cause a higher △σO, but a higher mean wind speed （SSW）will lead to a lower △σ0. We used the power law exponent which defines to calculating the wind speed coefficient （B） data of our samples with Aσo and wind speeds in two sides of a front. The result which was given by polynomial fitting method showed that B andthe marine variable profiles coefficient we assumed hada goodcorrelation. We can infer that SST influences in an ideal state by and ASSW which is causedby ASST, and sample statistics are accorded with the power law exponent. Our results provided abasis for ocean front profiles inversion with SAR data.