Coal mining activities have destroyed natural landscapes and surface vegetation, resulting in a large loss of soil organic carbon (SOC) storage in farmlands. Therefore, farmland SOC mapping in mining areas is of great significance to low-carbon reclamation and ecological compensation. Natural factors such as topography, climate, vegetation, and soil properties are key variables for SOC mapping. Human activities, including crop rotation and farmland damage caused by coal mining, also severely interfere with farmland SOC in mining areas. Here, we use take mining areas of the Changhe watershed as a case study to explore the effects of these activities on farmland SOC storage. Farmland damage was quantified using mining area geological hazard data, and four random forest (RF) models were constructed to predict SOC content based on natural variables alone (RF-N), natural variables + crop rotation (RF-C), natural variables + farmland damage (RF-F), and natural variables + crop rotation + farmland damage (RF-A). We found that: (1) the RF-A model most accurately captured SOC spatial differentiation information (followed by RF-C, RF-F, and RF-N). (2) There were significant differences in the SOC of different crop rotation areas and between low and high damage farmlands; specifically, the SOC content of wheat-corn rotations (one-year two cropping system) was higher than wheat-soybean-corn rotations (two-year three cropping system) and wheat (one-year one cropping system), and the SOC of high damage farmlands was lower than that of low damage farmlands. (3) Variable importance analysis showed that crop rotation and farmland damage were the two most important variables for predicting SOC in mining areas—demonstrating the effectiveness of common farming practices in predicting SOC in mining areas. Our results provide a scientific basis for land reclamation, soil carbon input, and land remediation planning in mining areas. 

Prediction of soil organic carbon in mining areas

Land use and sediment alluviation/deposition are the main factors influencing the vertical distributions of particles with different sizes and soil organic carbon (SOC) forms. Based on field investigation, experimental analysis, data analysis, soil particle characteristics, and the relationships with SOC compositions are studied in the typical alluviation/deposition area of Kaifeng–Zhoukou. In the soil profile, the particulate matter is mainly 10–50 μm and 50–250 μm in size with an average content of approximately 65%, the content difference within the same size particle range is small. There is a large content range of <1,000 μm particles, which is the main factor affecting the change in soil properties. The fractal dimension (D) of soil particles ranges from 2.21 to 2.78. The value of D of each layer in farmland is higher than that of each layer in woodland, and it has been observed that D NF(farmland in the nonflooded area) > D F(farmland in the flooded area) and D NW(woodland in the nonflooded area) > D W(farmland in the nonflooded area). The contents of particles smaller than 50 μm have a positive effect on D, and the particles exceeding 50 μm have a negative effect. The contents of 10–50 μm, <2 μm, 2–5 μm, and 5–10 μm particles and their dynamic variances are the root causes of the D differences in the farmland/woodland soil in the FA (the flooded area) and NFA (the nonflooded area). SOC components combine to a greater extent with silt and clay that are <10 μm in size in the NF, and the stability is relatively high. The contents of the 10–50 μm and <10 μm particles are the main reasons for the differences in the soil active and nonactive organic carbon (AOC and NOC, respectively) contents in the FA and the NFA. The difference in D can reflect the change in SOC and its components and can be used as an index to characterize the variance in soil properties and quality. This study revealed the influences of the different particle sizes in the SOC components, which will expand and enrich the current area of study and further provide a basis to increase SOC and improve soil quality.

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Distribution Characteristics of Soil Particles and Their Relationships With Soil Organic Carbon Components in the Alluvial/Sedimentary Zone in the Lower Reaches of the Yellow River

Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0–20 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties.

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Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

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Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Soil organic matter (SOM) and environmental factors have been shown to have a scale-location dependence relationship. However, few studies have considered the anisotropy, and the scale-location dependence relationship may not be fully characterized. In this study, transects with dominant directions of SOM variability in the dryland farming regions of Songliao Plain, China were extracted by anisotropy analysis. The scale-location specific multivariate relationships between SOM and environmental factors along the two transects were examined using multiple wavelet coherence. Results indicated that the scale and location-specific variations in SOM and environmental factors were direction-specific. The major direction with the most significant SOM variations was 56° east by north, while the minor direction was perpendicular to the major direction. The strongest single factor for explaining SOM variations differed between two dominant directions, sand along the major direction (average wavelet coherence (AWC) = 0.57, percentage area of significant coherence (PASC) = 40.32% at all scales) and bulk density (BD) along the minor direction (AWC = 0.66, PASC = 50.16% at all scales). The combination of mean annual temperature (MAT) and BD was the best to explain SOM variations along the major direction (AWC = 0.78, PASC = 46.23% at all scales). A two-factor combination is adequate to explain SOM variability along the major direction, whereas a single factor is sufficient for the explanation along the minor direction. More factors did not evidently increase or even decrease the percentage of scale-location domains where SOM variations were significantly explained. This work has important implications for developing future sampling strategies and preparing detailed digital soil maps.

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Jingting Qin

Papers

Revealing the scale- and location-specific relationship between soil organic carbon and environmental factors in China's north-south transition zone