Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 30, Issue -, Pages 139-142Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2011.10.015
Keywords
Remote sensing; Soil moisture; Gap filling; Penalized least square regression; Discrete cosine transform
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Funding
- Netherlands Organization for Scientific Research (NWO) [854.00.026]
- ESAs STSE [22086/08/I-EC]
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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