SSS SMOS/SMAP OI L4 maps

10 years of l-band remote sensing sea surface salinity (sss) measurements have proven the capability of satellite sss to resolve large scale to mesoscale sss features in tropical to subtropical ocean. in mid to high latitude, l-band measurements still suffer from large scale and time varying biases. here, a simple method is proposed to mitigate the large scale and time varying biases. first, in order to estimate these biases, an optimal interpolation (oi) using a large correlation scale is used to map smos and smap l3 products and is compared to equivalent mapping of in situ observations. then, a second mapping is performed on corrected sss at scale of smos/smap resolution (~45 km). this procedure allows to correct and merge both products, and to increase signal to noise ratio of the absolute sss estimates. using thermodynamic equation of state (teos-10), the resulting l4 sss product is combined with microwave satellite sst products to produce sea surface density and spiciness, useful to fully characterize the surface ocean water masses. the new l4 sss products is validated against independent in situ measurements from low to high latitudes. the l4 products exhibits a significant improvement in mid-and high latitude in comparison to the existing smos and smap l3 products. however, in the arctic ocean, l-band sss retrieval issues such as sea ice contamination and low sensitivity in cold water are still challenging to improve l-band sss data.

历经十年之久的L波段遥感海面盐度（SSS）测量数据已充分证实，卫星SSS在热带至副热带海洋中能够解析出从大型到中型的SSS特征。在中高纬度区域，L波段测量仍受限于大规模及时间变化的偏差。本研究提出了一种简便的方法以减轻这些偏差。首先，为了估算这些偏差，采用了一种具有大相关性尺度的最优插值（OI）方法，将SMOS和SMAP L3产品进行映射，并与同等的现场观测映射结果进行对比。随后，在SMOS/SMAP分辨率尺度（约45公里）上对校正后的SSS进行了第二次映射。此过程允许校正并合并两种产品，提高绝对SSS估计的信噪比。利用热力学状态方程（TEOS-10），所得的L4 SSS产品与微波卫星海面温度产品相结合，以生成海面密度和辛辣度，这对于全面表征表面海洋水体具有重要意义。新的L4 SSS产品经与从低纬度至高纬度的独立现场测量结果进行验证。与现有的SMOS和SMAP L3产品相比，L4产品在中高纬度区域显示出显著的改进。然而，在北极海洋中，L波段SSS提取问题，如海冰污染和冷水中的低灵敏度，仍然是一项挑战，有待进一步改善L波段SSS数据。