Short communication: A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations

  • Authors:

  • Guojie Wang;Damien Garcia;Yi Liu;Richard de Jeu;A. Johannes Dolman

  • Affiliations:

  • Department of Earth Sciences, VU University Amsterdam, 1085 HV Amsterdam, The Netherlands;CRCHUM-Research Center, University of Montreal Hospital, Montreal, Canada;Climate Change Research Centre, University of New South Wales, Sydney, Australia and CSIRO Land and Water, Black Mountain Laboratories, Canberra, Australia;Department of Earth Sciences, VU University Amsterdam, 1085 HV Amsterdam, The Netherlands;Department of Earth Sciences, VU University Amsterdam, 1085 HV Amsterdam, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2012

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Abstract

The presence of data gaps is always a concern in geophysical records, creating not only difficulty in interpretation but, more importantly, also a large source of uncertainty in data analysis. Filling the data gaps is a necessity for use in statistical modeling. There are numerous approaches for this purpose. However, particularly challenging are the increasing number of very large spatio-temporal datasets such as those from Earth observations satellites. Here we introduce an efficient three-dimensional method based on discrete cosine transforms, which explicitly utilizes information from both time and space to predict the missing values. To analyze its performance, the method was applied to a global soil moisture product derived from satellite images. We also executed a validation by introducing synthetic gaps. It is shown this method is capable of filling data gaps in the global soil moisture dataset with very high accuracy.