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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 R 2 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. [ABSTRACT FROM AUTHOR]

Although soils have a high potential to offset CO 2 emissions through its conversion into soil organic carbon (SOC) with long turnover time, it is widely accepted that there is an upper limit of soil stable C storage, which is referred to SOC saturation. In this study we estimate SOC saturation in French topsoil (0–30 cm) and subsoil (30–50 cm), using the Hassink equation and calculate the additional SOC sequestration potential (SOC sp ) by the difference between SOC saturation and fine fraction C on an unbiased sampling set of sites covering whole mainland France. We then map with fine resolution the geographical distribution of SOC sp over the French territory using a regression Kriging approach with environmental covariates. Results show that the controlling factors of SOC sp differ from topsoil and subsoil. The main controlling factor of SOCsp in topsoils is land use. Nearly half of forest topsoils are over-saturated with a SOC sp close to 0 (mean and standard error at 0.19 ± 0.12) whereas cropland, vineyard and orchard soils are largely unsaturated with degrees of C saturation deficit at 36.45 ± 0.68% and 57.10 ± 1.64%, respectively. The determinant of C sequestration potential in subsoils is related to parent material. There is a large additional SOC sp in subsoil for all land uses with degrees of C saturation deficit between 48.52 ± 4.83% and 68.68 ± 0.42%. Overall the SOCsp for French soils appears to be very large (1008 Mt C for topsoil and 1360 Mt C for subsoil) when compared to previous total SOC stocks estimates of about 3.5 Gt in French topsoil. Our results also show that overall, 176 Mt C exceed C saturation in French topsoil and might thus be very sensitive to land use change. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 1 m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R 2 = 0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.67 kg m − 2 and ME: − 0.33 to − 0.29 kg m − 2 ). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.41 Tg in the upper 30 cm, 113.15 Tg in the upper 50 cm and 190.30 Tg in the upper 1 m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas. [ABSTRACT FROM AUTHOR]

The soil organic carbon (SOC) stock is an important indicator in ecosystem service assessments. Even though a considerable fraction of the total stock is stored in the subsoil, current assessments often consider the topsoil only. Furthermore, mapping efforts are hampered by the limited spatial density of these topsoil measurements. The aim of this study was to assess the SOC stock in the upper 100 cm of soil in 30,556 ha of Low-Input High-Diversity systems, such as nature reserves, in Flanders (Belgium) and compare this estimate with the stock found in the topsoil (upper 15 cm). To this end, we combined depth extrapolation of 139 measurements limited to the topsoil with four digital soil mapping techniques: multiple linear regression, boosted regression trees, artificial neural networks and least-squares support vector machines. Particular attention was given to vegetation characteristics as predictors. For both the stock in the upper 15 cm and 100 cm, a boosted regression trees approach was most informative as it resulted in the lowest cross-validation errors and provided insights in the relative importance of predictors. The predictors of the stock in the upper 100 cm were soil type, groundwater level, clay fraction and community weighted mean (CWM) and variance (CWV) of plant height. These predictors, together with the CWM of specific leaf area, aboveground biomass production, CWV and CWM of rooting depth, terrain slope, CWM of mycorrhizal associations and species diversity also explained the topsoil stock. Our total stock estimates show that focusing on the topsoil (1.63 Tg OC) only considers 36% of the stock in the upper 100 cm (4.53 Tg OC). Given the magnitude of subsoil OC and its dependency on typical ecosystem characteristics, it should not be neglected in regional ecosystem service assessments. [ABSTRACT FROM AUTHOR]