Soil organic carbon mapping and prediction based on depth intervals using kriging technique: A case of study in alluvial soil from Sudan

Soil organic carbon plays a vital role in the arid and semiarid regions. This study aimed to predict and map soil organic carbon content at soil depth intervals of 0-0.3 m, 0.3-0.6 m, 0.6-0.9 m, and 0.9-1.2 m in alluvium soils along Blue Nile and River Nile, Sudan. Ordinary kriging (OK) technique was used as a geostatistical tool and applied to model the spatial variability of soil organic carbon in the study area. A total of 38 soil profiles were excavated in the study area and 152 samples from the four depths intervals were collected for determining organic carbon content. Results revealed that, the spatial autocorrelation for the different soil layers was moderate to weak with a nugget to sill ratios ranging from 0.21 to 0.86 suggesting their controlled by both intrinsic and extrinsic factors. The root mean square error standardized (RMSE) of the predictions ranging from 0.79 to 0.83 indicating that the model which generated by ordinary kriging was correctly estimating the variability of soil organic carbon in the study area

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. A methodology was developed to identify soil salinity in Sudan by utilizing optical and radar-based satellite data as well as variables obtained from digital elevation models that are known to indicate variations in soil salinity. The methodology includes the transfer of models to areas where similar conditions prevail. A geographically coordinated database was established, incorporating a variety of environmental variables based on Google Earth Engine (GEE) and Electrical Conductivity (EC) measurements from the saturation extract of soil samples collected at three different depths (0–30, 30–60, and 60–90 cm). Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. The producer’s accuracy, user’s accuracy, and Tau index parameters were used to evaluate the model’s accuracy, and spatial confusion indices were computed to assess uncertainty. At different soil depths, Tau index values for the reference area ranged from 0.38 to 0.77, whereas values for target area samples ranged from 0.66 to 0.88, decreasing as the depth increased. Clay normalized ratio (CLNR), Salinity Index 1, and SAR data were important variables in the modeling. It was found that the subsoils in the middle and northwest regions of both the reference and target areas had a higher salinity level compared to the topsoil. This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. This approach can be instrumental in identifying alternative areas suitable for agricultural activities at a regional level