Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns

Andri Freyr Þórðarson, Andreas Baum*, Mónica García, Sergio M. Vicente-Serrano, Anders Stockmarr

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian
Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%
Original languageEnglish
Article number4007
JournalRemote Sensing
Issue number19
Number of pages23
Publication statusPublished - 2021


  • Missing data
  • Imputation
  • Data completion
  • Remote sensing
  • Tensor decomposition
  • Tucker
  • Multiway analysis
  • Machine learning


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