Abstract A novel interpolation technique is applied to assessment of the quality of sea surface temperature (SST) observations and quantitative analysis of the subpixel variability within satellite footprints of different size. Using retrieved satellite data as input, the new, global, multistage interpolation technique generates a trigonometric polynomial, providing a representation of the underlying physical SST field in functional form. The resulting interpolating function can be efficiently and accurately evaluated anywhere within the domain over which it was derived and its moments calculated to estimate the mean and variance of the field over desired sub-regions. Application of the technique is demonstrated for SST retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), Spinning Enhanced Visible and Infrared Imager (SEVIRI), and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensors. Comparison of the functional form with the data from which it was derived demonstrates how the technique can potentially help to identify small observational artifacts such as MODIS scan striping and residual cloud contamination. Integrals of the interpolating functions over successively larger spatial scales successfully emulate the retrieved SST at the different effective spatial resolutions and the second moments are consistent with the direct sample variances, and hence representative of the spatial SST variability of the available finer-resolution observations over the coarser scales. Using the approach, the variability of 1-km-resolution SST observations on open ocean grids of both 5- and 25-km resolution is found to be ~0.07 K. In regions of sharper gradients such as associated with strong localized diurnal warming, the variability within 25-km-resolution grids increases to as much as 0.4 K for sampling at 1-km resolution. The variability of 1-km observations on a 25-km-resolution grid is about 2.4 times greater than that on a 5-km-resolution grid. Broader application of the technique globally could help better quantify regional variations in the spatial variability, which would subsequently improve uncertainty estimates for existing satellite-based SST products.

中文翻译：

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使用新型高分辨率多级光谱插值 (HRMSI) 技术对卫星海面温度数据进行亚像素变异和质量评估

摘要 一种新的插值技术被应用于评估海面温度（SST）观测的质量和不同大小卫星足迹内亚像素变异性的定量分析。使用检索到的卫星数据作为输入，新的全局多级插值技术生成三角多项式，以函数形式提供底层物理 SST 场的表示。由此产生的内插函数可以在其导出的域内的任何地方进行有效和准确的评估，并计算其矩以估计所需子区域上的场的均值和方差。该技术在中分辨率成像光谱仪 (MODIS)、旋转增强型可见光和红外成像仪 (SEVIRI) 的 SST 反演中的应用得到了证明，和高级微波扫描辐射计 - 地球观测系统 (AMSR-E) 传感器。函数形式与派生数据的比较表明该技术如何潜在地帮助识别小的观测伪像，例如 MODIS 扫描条纹和残余云污染。插值函数在连续更大的空间尺度上的积分成功地模拟了在不同有效空间分辨率下检索到的 SST，并且二阶矩与直接样本方差一致，因此代表了可用更高分辨率观测的空间 SST 变异性。较粗的鳞片。使用该方法，发现 5 公里和 25 公里分辨率的开阔海洋网格上 1 公里分辨率 SST 观测的变异性约为 0.07 K。在梯度更陡峭的区域，例如与强烈局部昼夜变暖相关的区域，25 公里分辨率网格内的变异性增加到 0.4 K，以 1 公里分辨率采样。25 公里分辨率网格上 1 公里观测值的可变性是 5 公里分辨率网格上的 2.4 倍。在全球范围内更广泛地应用该技术可以帮助更好地量化空间变异性的区域变化，这将随后改善现有基于卫星的 SST 产品的不确定性估计。