Abstract
Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg−1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.
Highlights
Soil organic carbon (SOC) and pH are important soil properties [1]
Since SOC data did not conform to an orthogonal distribution, log transformation was performed to make the data conform to a normal distribution during two periods
Slope gradient, and MAP were positively correlated with SOC, while TWI, MAT, B3, B4, NDVI, and land use were negatively correlated with SOC in both periods
Summary
Soil organic carbon (SOC) and pH are important soil properties [1]. They are important indicators to measure soil fertility and soil environmental quality [2,3]. The effects of pH on SOC in acidic soils mainly include reducing the solubility of organic carbon, changing the organic–mineral interaction in tropical soils with variable negative charges, increasing the number of biotoxic cations (i.e., Al3+ and Mn2+), changing the composition and quantity of microbial population, and changing soil microbial activity and enzyme activity [7]. These effects of soil pH will inevitably affect the turnover process of SOC. It is important to find a robust, reliable, and economical method for estimating SOC and pH values and their spatial-temporal variations [8,9]
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