Evaluation of statistical methods for infilling missing values in high-resolution soil moisture data

Continuous time series of soil moisture data are important for water resource applications including streamflow predictions and weather forecasting. In practice, most soil moisture data have missing values which require infilling to generate complete datasets. This study investigates six methods for infilling missing values for high resolution soil moisture data. This includes multiple linear regression (MLR), weighted Pearson correlation coefficient (WPCC), station relative difference (SRD), soil layer relative difference (SLRD), monthly average replacement (MAR), and merged method. Hourly soil moisture and meteorological data at four different sites, each with nine soil moisture stations located in Halton and Hamilton conservation areas in southern Ontario, Canada were used to evaluate the accuracy levels of the six selected methods. The results showed that the merged method consistently provides the highest evaluation accuracy across all sites. An appealing property of the merged method is its capability to incorporate multiple estimates of missing soil moisture value from other methods. The decreasing order of performance for remaining methods is MAR, SLRD, SRD, MLR and WPCC. The MAR is a simple method but it performed better than complex methods such as MLR and WPCC. Both SRD and SLRD employ the concept of rank stability of soil moisture but SLRD is preferred to SRD – suggesting that similar soil layer at different spatial locations, opposed to temporal persistence of soil moisture in the vertical soil column, is a better predictor of missing soil moisture data. That means, it is more accurate to predict a missing soil moisture at a location using other locations’ soil moisture at a similar depth, than to use soil moisture at a certain depth to predict deeper or shallower soil moisture at the same location.