An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data

PLoS One. 2016 May 19;11(5):e0155928. doi: 10.1371/journal.pone.0155928. eCollection 2016.

Authors

Bo Ping¹, Fenzhen Su², Yunshan Meng^{2

3}

Affiliations

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
² Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
³ University of Chinese Academy of Sciences, Beijing 100049, China.

Abstract

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Data Accuracy
Datasets as Topic / standards
Oceans and Seas*
Signal-To-Noise Ratio
Temperature*

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China (41271409) (http://www.nsfc.gov.cn) and the National High Technology Research and Development program of China (2012AA12A408) (http://www.most.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.