Association with reactive metal (hydr)oxides is critical for soil organic carbon (SOC) stabilization. However, direct evidence is lacking on how shifting dominance of different metal-bound organic carbon within soil profile mediates SOC stability. Here we leverage a natural experiment comprising 16 soil profiles (~200 cm) sampled along a 1,500 km climatic gradient from semi-humid to semi-arid. We indicate that the response of SOC chemical stability (acid hydrolysis for recalcitrant organic carbon determination) to metal-bound organic carbon is asymmetric, meaning that metal-bound organic carbon dominating recalcitrant organic carbon does not strictly follow the pattern of greater content equating to stronger effect. Soil pH mediates the shift in dominance from iron to calcium phases over the range of 7.2 to 7.6, thereby regulating their respective impact on recalcitrant organic carbon. Our findings elucidate the key behavior of metal-bound organic carbon (especially calcium-bound organic carbon), in maintaining SOC stability under soil pH regulation.

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R² of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

Readily available environmental covariates in current digital soil mapping usually do not indicate the spatial differences between deep soil attributes. This, to a large extent, leads to a decrease in the accuracy of 3D soil mapping with depth, which seriously affects the quality of soil information generated. This study tested the hypothesis that spatialized laboratory soil spectral information can be used as environmental covariates to improve the accuracy of 3D soil attribute mapping and proposed a new type of environmental covariable. In the first step, with soil-forming environmental covariates and independent soil profiles, laboratory vis-NIR spectral data of soil samples resampled into six bands in Anhui province, China, were spatially interpolated to generate spatial distributions of soil spectral measurements at multiple depths. In the second step, we constructed three sets of covariates using the laboratory soil spectral distribution maps at multiple depths: conventional soil-forming variables (C), conventional soil-forming variables plus satellite remote sensing wavebands (C+SRS) and conventional soil-forming variables plus spatialized laboratory soil spectral information (C+LSS). In the third step, we used the three sets of environmental covariates to develop random forest models for predicting soil attributes (pH; CEC, cation exchange capacity; Silt; SOC, soil organic carbon; TP, total phosphorus) at multiple depths. We compared the 3D soil mapping accuracies between these three sets of covariates based on another dataset of 132 soil profiles (collected in the 1980s). The results show that the use of spatialized laboratory soil spectral information as additional environmental covariates has a 50% improvement in prediction accuracy compared with that of only conventional covariates, and a 30% improvement in prediction accuracy compared with that of the satellite remote sensing wavebands as additional covariates. This indicates that spatialized laboratory soil spectral information can improve the accuracy of 3D digital soil mapping.