Geostatistical Spatial Interpolation of Soil Water Retention Curve Coupled with Ptf Based on Particle Size Distribution.

Predicting soil water retention curves (SWRC) or their model parameters at any unsampled locations using a geostatistical spatial interpolation technique requires a number of high quality retention data. Obtaining SWRC is, however, generally tedious, time consuming, and sometimes expensive. Therefore, pedotransfer functions (PTF), which allow one to predict soil hydraulic parameters from easily measured soil properties, have been developed. In this study a geostatistical spatial interpolation technique was coupled with the PTF to predict water retention curves at unsampled locations from available particle size distribution (PSD) data. Two PTFs are used in this study, one is the Arya and Paris (AP) model which predicts water retention curves from PSD and dry bulk densities, the other is based on the k-nearest neighbor (k-NN) algorithm which is a nonparametric method used in data mining. There are two approaches considered: (1) First, SWRC are predicted from PSD at given observed locations using one of the PTFs. SWRC are then predicted at given unsampled locations through the geostatistical spatial interpolation technique. (2) First, PSD and the bulk densities are predicted at given unsampled locations using the geostatistical spatial interpolation technique. Then, SWRCs are predicted at the unsampled locations by the PTFs from the interpolated PSD and bulk densities. The performance of these two approaches to predict SWRC at any given unsampled location were compared. The data used in this study were obtained from the Las Cruces trench site database, which contains water retention data for 447 soil samples. The dataset was then split into two sets, prediction and validation sets. The results used AP model showed that the performances of the both approaches were comparable. On the other hand, the results based on k-NN showed that perofming PTF before interpolation is better .