TY - JOUR
T1 - A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations
AU - Wang, G.
AU - Garcia, D.
AU - Liu, Y.
AU - de Jeu, R.A.M.
AU - Dolman, A.J.
PY - 2012
Y1 - 2012
N2 - 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. © 2011 Elsevier Ltd.
AB - 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. © 2011 Elsevier Ltd.
U2 - 10.1016/j.envsoft.2011.10.015
DO - 10.1016/j.envsoft.2011.10.015
M3 - Article
SN - 1364-8152
VL - 30
SP - 139
EP - 142
JO - Environmental Modelling & Software
JF - Environmental Modelling & Software
ER -