PMID- 27195692
OWN - NLM
STAT- MEDLINE
DCOM- 20170707
LR  - 20181101
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 11
IP  - 5
DP  - 2016
TI  - An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea 
      Surface Temperature Data.
PG  - e0155928
LID - 10.1371/journal.pone.0155928 [doi]
LID - e0155928
AB  - 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.
FAU - Ping, Bo
AU  - Ping B
AD  - School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 
      430079, China.
FAU - Su, Fenzhen
AU  - Su F
AD  - Laboratory of Resource and Environmental Information System, Institute of 
      Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 
      Beijing 100101, China.
FAU - Meng, Yunshan
AU  - Meng Y
AD  - Laboratory of Resource and Environmental Information System, Institute of 
      Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 
      Beijing 100101, China.
AD  - University of Chinese Academy of Sciences, Beijing 100049, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20160519
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Algorithms
MH  - Data Accuracy
MH  - Datasets as Topic/standards
MH  - *Oceans and Seas
MH  - Signal-To-Noise Ratio
MH  - *Temperature
PMC - PMC4873229
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2016/05/20 06:00
MHDA- 2017/07/08 06:00
PMCR- 2016/05/19
CRDT- 2016/05/20 06:00
PHST- 2015/09/28 00:00 [received]
PHST- 2016/05/07 00:00 [accepted]
PHST- 2016/05/20 06:00 [entrez]
PHST- 2016/05/20 06:00 [pubmed]
PHST- 2017/07/08 06:00 [medline]
PHST- 2016/05/19 00:00 [pmc-release]
AID - PONE-D-15-42673 [pii]
AID - 10.1371/journal.pone.0155928 [doi]
PST - epublish
SO  - PLoS One. 2016 May 19;11(5):e0155928. doi: 10.1371/journal.pone.0155928. 
      eCollection 2016.