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Accurate understanding of spatial distribution and variability of soil total nitrogen (TN) is critical for the site-specific nitrogen management. Based on 4337 newly obtained soil observations and 33 covariates, this study applied the random forest (RF) algorithm and modified regression kriging (RF combined with residual kriging: RFK, hereafter) model to spatially predict and map topsoil TN content in agricultural areas of Henan Province, central China. According to the RFK prediction, topsoil TN content ranged from 0.52 to 1.81 g kg−1, and the farmland with the topsoil TN contents of 1.00–1.23 g kg−1 and 0.80–1.23 g kg−1 accounted for 48.2% and 81.2% of the total farmland area, respectively. Spatially, the topsoil TN in the study area was generally higher in the west and lower in the east. By using the Boruta variable selection algorithm, soil organic matter (SOM) and available potassium contents in topsoil, nitrogen deposition, average annual precipitation, livestock discharges, and topsoil pH were identified as the main factors driving the spatial distribution and variation of soil TN in the study area. The RF and RFK models used showed the expected performance and achieved acceptable TN prediction accuracy. In comparison, RFK performed slightly better than the RF model. The R2 and RMSE achieved by the RFK model were improved by 4.5% and 4.5%, respectively, compared with that by the RF model. However, the results suggest that RFK was inferior to the RF model in quantifying prediction uncertainty and thus may have a slight disadvantage in model reliability. [ABSTRACT FROM AUTHOR]

Spatial prediction and mapping of soil properties are synergistically influenced by models and environmental covariates. However, selecting the best modeling technique for a given landscape has been a challenge for mapping soil property. Typically, the conventional variable set (such as soil factors, climatic factors, terrain factors, and organism factors) is widely used to predict soil pH. However, certain covariates are difficult to obtain for some regions with poor data. Additionally, the effectiveness of vegetation indices (such as normalized difference vegetation index, NDVI) and phenological variables that can monitor vegetation growth dynamics based on satellite remote sensing for soil pH mapping are largely unclear. Accordingly, this study aimed to set up five environmental variable sets, such as NDVI, phenological variables, conventional variable set, conventional variable set + NDVI (C + N), and conventional variable set + phenological variables (C + P). The random forest (RF) model and convolutional neural network (CNN) model were used to compare the performance of these five environmental variable sets for spatial prediction for topsoil pH. In total, 4332 soil samples were applied and randomly divided into calibration (80%) and validation sets (20%) in this study. The model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that among the five environmental variable sets, the conventional variable set was the best to predict soil pH (R2 = 0.85, RMSE = 0.41). Further, among the remote sensing variables, the phenological variables outperformed NDVI, explaining up to 67% of the variation in topsoil pH. The RF model could achieved higher accuracy than the CNN model regardless of the variables selected. Moreover, the conventional variable set could predict topsoil pH, and exhibited the potential of phenological variables. The results indicated when the conventional variable set was difficult to obtain or was missing, phenological variables can be used to predict soil properties and perform digital mapping instead of the conventional variable set. • Prediction of pH distribution in topsoil using different covariates and combinations of them. • SCORPAN set could explain the 85% of the topsoil pH variation. • The RF model is superior to the CNN model for limited sample size • Human intervention is becoming a key factor in the change of pH in topsoil [ABSTRACT FROM AUTHOR]

• A large range in cropland soil acidification occurred in Henan from 2008 to 2018. • Topsoil pH decline was more widespread and serious in the south than in the north. • Nitrogen fertilization was the most important factor related to soil acidification. • Croplands in Henan face the risk of accelerating soil acidification. Soil acidification can negatively impact the sustainability of agricultural systems. Spatially explicit estimations of soil acidification and the identification of local factors driving soil pH change are prerequisites for formulating and implementing site-specific measures to prevent and mitigate cropland soil acidification. Based on pairwise comparisons between 1443 pairs of topsoil pH observations at colocated sites in 2008 and 2018, this study revealed the spatial variability of cropland soil acidification and its influencing factors across Henan Province, central China. The results showed that the topsoil pH of cropland in Henan Province decreased by an average of 0.36 units, and more than 94% of the cropland in the study region suffered from varying degrees of topsoil pH decline from 2008 to 2018, demonstrating a large range in soil acidification. The most serious soil acidification of croplands occurred in the southern part of the province, while in the northern part of the province, the topsoil pH decreased to a lesser extent or even slightly increased. The topsoil pH shift of cropland was found to be closely associated with the N fertilizer application rate, base cation removal by crops, and factors influencing the soil response to acidification, i.e., soil calcareousness, initial acidity, clay content, moisture regimes and temperature regimes. Among the various factors, the N fertilizer application rate was identified as the most important factor related to soil acidification. Croplands face the risk of accelerating soil acidification due to continuous excessive nitrogen application, increasing the removal of base cations while promoting crop yield, and decreasing or even disappearing carbonate material in the soil. [ABSTRACT FROM AUTHOR]

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties. [ABSTRACT FROM AUTHOR]