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Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and total nitrogen (TN) influence physical, chemical and biological properties and process in soils that determine soil fertility and enhance or maintain agricultural productivity and food security. Knowledge on the spatial distribution of SOC and TN is necessary for sustainable soil resource management. In this study, we applied boosted regression trees (BRT) model to predict and map the spatial distribution of SOC and TN in the northeastern coastal areas of China, and C:N ratio map was generated from the predicted maps of SOC and TN. A total of 149 topsoil (0–20 cm) samples and 12 environmental variables that included topography, landuse and remote sensing indices, and climatic variables were selected. The performance of the models was evaluated based on cross-validation using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ). The BRT model was run for 100 iterations where the average of 100 SOC and TN maps was considered as final predicted maps and its standard deviation as prediction uncertainty. The BRT model showed a good predictive performance due to its higher R 2 and lower MAE, and RMSE indices. The model explained approximately 64% and 56% of the total SOC and TN variability. Topographic variables showed a maximum influence in the prediction of SOC and TN, followed by vegetation and climate. The SOC and TN contents were reduced from the northwest towards the southeast of the study area; average predicted SOC and TN contents were 15.4 g kg −1 and 1.01 g kg −1 , respectively. The spatial distribution of C:N ratio was closely related to landuse types in a decreasing order: woodland > orchard > cultivated land > grassland. We found topographic variables as main environmental indicators of SOC and TN distribution, and recommend to include them in future SOC and TN mapping studies in the coastal agroecosystems in China. [ABSTRACT FROM AUTHOR]