Identifying scale-specific controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform

Abstract

Soil organic matter (SOM) is an important index to evaluate soil fertility. Knowing the spatial distribution of SOM and its controlling factors at different scales is basic to sustainable farmland management. The variability was explored mostly in plain farmlands or at small scales in previous studies. In the present study, combined with anisotropy analysis (AA) and discrete wavelet transform (DWT), we examined the spatial variability of SOM and its controlling factors at various scales in a mountainous area. Transect with dominant directions (major axis and minor axis) of SOM variability was extracted using AA and then the scale-specific variability was examined using DWT. Dominant factors of SOM variability at different scales were identified using correlation coefficients between SOM at different scales and various soil environmental factors. The results showed that the major axis along which SOM varied the most was 24° south by west, consistent with the strike of Wuling Mountains. The minor axis was perpendicular to the major axis direction. DWT separated the SOM variations into nine scale components (eight details, D1 through D8, and one approximation, A8) along the major axis and into eight scale components (seven details, D1 through D7, and one approximation, A7) along minor axis. The largest-scale component (A8 in major axis and A7 in minor axis) explained the most variance of SOM along both axes, accounting for half of the total variance. Compared with the original SOM before separation of scale components (undecomposed SOM), the scale components showed significant correlation with environmental factors. Both elevation and mean annual precipitation had positive correlation with SOM at large scales. However, there was a negative correlation between SOM and mean annual temperature. This indicates that the topography and local climate may have a stronger influence in controlling SOM spatial distribution in mountain regions. The relationship provides important information on environmental covariate selection in mapping soil resource. The combination of AA and DWT shows promise quantifying SOM spatial distribution and its control factors at different scales in mountainous areas.