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以星载微波遥感的辐射传输方程为基础,利用SMOS(土壤湿度海洋盐度)卫星的L1C级亮温数据,通过与辐射传输模型模拟的亮温进行对比,评估及验证亮温的数据质量,建立了海洋盐度反演算法。通过分析2012年7月东南太平洋区域(45°~5°S,140°~90°W)的下降轨道数据,发现MIRAS亮温与模型模拟亮温之间总是存在几K的系统偏差,即OTT,因此提出了两种反演盐度的方法:一种是修正OTT偏差,使用入射角0°~55°的数据反演盐度;另一种是不修正OTT偏差,使用大入射角范围35°~55°的数据进行盐度反演。再通过利用MIRAS多角度信息,对亮温作二阶多项式拟合,减少随机噪声对反演的影响。最后采用最小二乘法,使得MIRAS的二阶拟合亮温与模型仿真亮温最接近,迭代反演盐度值。并将反演结果分别与欧空局的L2级盐度数据产品和Argo盐度数据进行比较,来验证反演算法。结果表明:修正OTT之后全角度数据反演的盐度值在50km×50km范围内、卫星过境前后5d,与Argo浮标盐度匹配比较的均值为1.38pss,标准差为0.35pss;不修正OTT,直接利用大入射角范围35°~55°的MIRAS亮温反演盐度,与Argo盐度误差均值为0.03pss,标准差为0.33pss;同时欧空局的L2级盐度与Argo盐度误差均值为0.26pss,标准差为0.38pss。可见利用大入射角范围的反演方法很好地反演了海洋盐度。
Based on the radiative transfer equation of satellite-borne microwave remote sensing, L1C-level bright temperature data of SMOS (Soil Moisture and Marine Salinity) satellites were used to evaluate and verify the data quality of bright temperature by comparing with the bright temperature simulated by radiative transfer model. The ocean salinity inversion algorithm was established. By analyzing the descending orbit data from July, 2012 to the southeast Pacific region (45 ° ~ 5 ° S, 140 ° ~90 ° W), it is found that there is always a systematic deviation of several K between the MIRAS bright temperature and the model simulated bright temperature OTT. Therefore, two methods to retrieve salinity are proposed: one is to correct the OTT deviation, and the salinity is retrieved by the data with the incident angle of 0 ° ~ 55 °; the other is to correct the OTT deviation without using the large incident angle range 35 ° ~ 55 ° data for salinity inversion. By using MIRAS multi-angle information, the second-order polynomial fitting is performed on the bright temperature to reduce the influence of random noise on the inversion. Finally, the least squares method is adopted to make the second-order fitting bright temperature of MIRAS be closest to the model temperature and the iterative inversion salinity value. The results of the inversion are compared respectively with ESA’s L2-level salinity data and Argo salinity data to verify the inversion algorithm. The results show that the salinity value of the data retrieved after the OTT correction is within 50km × 50km and that of the Argo buoy salinity is 1.38pss and the standard deviation is 0.35pss before and after satellite transit. The OTT, Directly using MIRAS bright temperature with large incident angle range of 35 ° ~ 55 ° to retrieve salinity, the mean error of salinity with Argo is 0.03 pss and the standard deviation is 0.33 pss; at the same time, the ESA L2 level salinity and Argo salinity error The mean is 0.26pss and the standard deviation is 0.38pss. It can be seen that the inversion method using a large range of incidence angles well inverts the salinity of the ocean.