An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method

Abstract

Ocean remote-sensing satellite data have been widely applied in the areas of oceanography, meteorology, the environment, and many more fields in science and engineering. However, missing data due to cloud cover, equipment failure, etc., limit its application. Therefore, reconstruction of the missing data through an appropriate method is essential. The data-interpolating empirical orthogonal function (DINEOF) algorithm proposed by Beckers and Rixen (2003) is currently the most commonly used method for the reconstruction of missing data in large areas. However, the existing DINEOF algorithm adopts a random method to select the cross−validation points, which may underutilize effective information around the missing value points. In addition, the cross-validation points may be too concentrated in an area, thus being unable to reflect the overall characteristics of the data. This paper optimizes the method to select the cross-validation points so that the information around the missing values can be effectively utilized and to avoid the cross-validation points being too concentrated. On this basis, an improved validation-point DINEOF algorithm (IV−DINEOF) is proposed. An ideal dataset and a reanalysis dataset based on sea surface temperature (SST) are used to test the performance of the improved algorithm. Statistical analysis of the results shows that the data reconstruction performance of the IV−DINEOF algorithm is better than that of the DINEOF algorithm, and the computational efficiency is also improved. The VE−DINEOF algorithm has the highest computing efficiency, but its reconstruction accuracy is lower than that of IV−DINEOF.

Keywords

• cross-validation points
• DINEOF algorithm
• missing data filling
• satellite remote sensing