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Soil organic carbon (SOC) is a critical measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, SOC can be significantly influenced by anthropogenic land use change, with intensive and extensive disturbances resulting in considerable SOC loss. Consequently, understanding the spatial distribution of SOC across different land uses, particularly at national level characterised by different biomes, is vital for integrated land-use planning and climate change mitigation. Remote sensing and deep learning (DL) offer a reliable largescale mapping of SOC by leveraging on their big data provision and powerful analytical prowess, respectively. This study modelled SOC stocks across South Africa's major land uses using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Based on 1936 soil samples and 31 spectral predictors, results show a relatively high accuracy with an R2 and RMSE value of 0.685 and 10.15 t/h (26% of the mean), respectively. From the seven land uses evaluated, grasslands (31.36%) contributed the most to the overall SOC stocks while urban vegetation (0.04%) contributed the least. Moreover, although SOC stock was found to be relatively proportional to land coverage, commercial (46.06 t/h) and natural (44.34 t/h) forests showed a higher carbon sequestration capacity. These findings provide an important guideline to managing SOC stocks in South Africa, useful in climate change mitigation through sustainable land-use practices. Whereas landscape restoration, and other relevant interventions are encouraged to improve SOC storage, care must be taken within land-use decision making to maintain an appropriate balance between carbon sequestration, biodiversity, and general ecosystem functions. [ABSTRACT FROM AUTHOR]

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens current mitigation approaches and can jeopardise carbon stock assimilation. To effectively manage and protect SOC stocks, large-scale projections that accurately model both current and future SOC pools are necessary. Hence, this study modelled the effects of simulated climate and land-cover change on SOC inventories across South Africa up to the year 2050. A digital soil mapping strategy in concert with a deep neural network (DNN) was used to model current SOC stocks distribution. Subsequently, WorldClim general circulation models and a space-for-time substitution (SFTS) method were used to derive future SOC stocks under four shared socio-economic emission pathways. Results show a relatively high accuracy with RMSE of 7.44 t/h for current stocks, while future stocks ranged from 11.37 to 13.56 t/h. Depending on emission rates, results showed a reduction in SOC inventories, with overall SOC stocks declining from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. Meanwhile, forests, which account for approximately 1.2 Pg of total SOC in South Africa, were found to have lost more than 1% of their total coverage by 2050. These findings provide a glimpse into the state of South Africa's current and future SOC stock inventories and the influence of climate and land-use change. These findings are valuable to among others policymakers, land use managers and climate change experts in assessing the long-term feasibility of South Africa's existing SOC management protocols and land-use planning agenda. However, to adequately protect future SOC stocks, current land-use planning frameworks need to be re-adjusted to prioritize pressing environmental concerns. • First national scale projection (2050) of SOC stocks in South Africa. • South Africa SOC stocks inventory declined from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. • The extent of SOC decrease varies across different shared socio-economic emission pathways. • SOC stocks are proportional to land cover size, but forests have the highest sequestration rate. [ABSTRACT FROM AUTHOR]