Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information

Abstract

This study aimed to improve the accuracy of spatial prediction for soil organic matter, potential mineralizable carbon (PMC) and soil organic carbon (SOC), using secondary information, namely topographic and vegetation information, in northern Kazakhstan. Secondary information included elevation (ELEV), mean curvature (MEANC), compound topographic index (CTI) and slope (SLOPE) obtained from a digital elevation model, and enhanced vegetation index (VI) values obtained from a moderate resolution imaging spectroradiometer (MODIS). The prediction methods were statistical (multiple linear regression between soil organic matter and secondary information) and geostatistical algorithms (regression-kriging Model-C and simple kriging with varying local means [SKlm]). The VI, ELEV and MEANC were selected as the independent variables for predicting PMC and SOC. However, MEANC showed an opposite effect on PMC and SOC accumulation patterns. Model validity revealed that SKlm was the most appropriate method for predicting PMC and SOC spatial patterns because model validity revealed the smallest errors for this method. Maps from the kriged estimates showed that a combination of secondary information and geostatistical techniques can improve the accuracy of spatial prediction in study areas.