High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Highlights: • Remote sensing covariates improved the estimation of SOC stocks. • Prediction accuracy of tree-based models was superior to support vector machine. • Digital soil mapping for SOC was practical and cost-effective in semi-arid rangelands. • Fractional cover data influenced SOC stock at the soil surface. Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0–5 cm and 0–30 cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R² of 0.32 for SOC stock at 0–5 cm and 0.44 at 0–30 cm, RMSE of 3.51 Mg C ha⁻¹ at 0–5 cm and 9.16 Mg C ha⁻¹ at 0–30 cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4–12.7% at 0–5 cm, and by 2.8–5.9% at 0–30 cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.