Interpolating Soil Organic Matter Content at the Regional Scale Using Regression Kriging.

The data quality of soil properties, such as the soil organic matter (SOM) content can be improved and spatial sampling intensities may be reduced by incorporating secondary information, such as those derived from digital elevation model (DEM) and remote sensing (RS) to enhance their spatial estimates. This study adopted a generic framework for spatial interpolation using regression kriging (RK) developed by Hengl et al. (2004) to evaluate RK’s capability in improving SOM spatial interpolation using internal secondary variables (sampling coordinates) and external auxiliary information, such as soil map (SOIL), vegetation indices (VIs) derived from a Landsat5 TM image, and several terrain attributes (elevation, slope, convergence and wetness indices, and plan and profile curvatures). Meanwhile, the SOM spatial distribution was also interpolated by using ordinary (OK) and universal  (UK) kriging for comparison purpose. The results of this study indicated that the prediction accuracy of SOM by RK actually did not increase with the increasing number of auxiliary information in the regression models, but contrarily it significantly declined when DEM, VI and SOIL information were combined, in particular the last one. It was also observed that with the increase of the minimum sampling distances from 25 to 500 m or with the decrease of the sampling densities from 0.44 to 0.27 # km^-2, the RK techniques did not outperform OK and UK in improving the SOM prediction accuracy at coarse sampling resolutions. The suitability of RK implementation in the spatial interpolation was therefore discussed by considering the minimum sampling distance, the sampling density and the compatibility of spatial resolutions of target variables and auxiliary information or the spatial scales.