Predicting the spatial distribution of SOC using remotely sensed data and vegetation data in southern Queensland’s grasslands

Soil organic carbon (SOC) plays an important role in sequestering CO2 and assists in reducing atmospheric greenhouse gases in addition plays a critical role in maintaining the sustainability of grasslands. The valuable roles of SOC, make its accurate measurement critical however temporal changes in SOC are small and spatially vary.  Therefore, a large number of samples are required to detect the SOC changes which makes it a complex and costly task. Stratification is capable of improving the efficiency of sampling by reducing the number of samples and increasing the accuracy of SOC measurement. Stratification relies on assessing the relationship between SOC and environmental factors. Vegetation has the potential to be used as a proxy to spatially predict SOC.

This experiment aimed to assess the relationship between SOC and vegetation characteristics as a key factor in small areas with uniform climate and soil type. The three study sites were located in southern Queensland with subtropical climate. Short-term data was collected using the BOTANAL method and biomass harvesting over two years period in different seasons which included biomass, pasture composition, and vegetation type. Long-term data was extracted from various satellite images for up to 30 years which indicate the long-term effect of vegetation on SOC. Remote sensing data contained vegetation and soil indices.

The kriging method was applied to both soil and vegetation data to interpolate unsampled points for the study areas, then K-means clustering was used to cluster the data. Spearman rank-order correlation coefficient was used to assess the correlation between SOC clusters and vegetation factor clusters.

While some of the vegetation parameters have a significant correlation with SOC, the correlation is not consistent between different sites and different seasons. It can be concluded from this study that vegetation factors are not capable of using landscape clustering for SOC sampling on small scale.