Proportional allocation with soil depth improved mapping soil organic carbon stocks

Abstract

Soil organic carbon (SOC) is vital to the assessment of land quality, management of farmland and ecological environment, and carbon cycle. A more accurate spatial prediction of multilayer soil organic carbon density (SOCD) can contribute to a better interpretation of the changes in multilayer SOC stocks and carbon dynamics. However, previous mapping techniques still have limitations, such as ignoring the relationship of profile depths, not further taking advantage of vertical distribution and surface categorical information. In addition, it is unclear whether it is better to model each depth interval of SOC separately or to model the total layer and then allocate it. Here, we propose two new methods based on the proportional allocation of soil depth for multilayer mapping: vertical log-ratio method (VLR) of SOCD by applying the percentage of SOCD data and isometric log-ratio (ILR) transformation, and vertical distribution method (VD) of SOCD by considering different land-use types. We compared five methods, including the two new methods, the exponential and equal-area spline functions, and independent modeling without depth information. We combined these five methods with the generalized linear model (GLM) and random forest (RF) to produce predictions of the Sanjiang Plain, northeastern China. The results demonstrated that SOCD did not always decrease with increasing soil depth, and classification of SOCD vertical distribution features needs to be considered by combining with soil depths. For accuracy assessment, the exponential mode with both GLM and RF over-calculated the predicted values and performed poorly, indicating that the blind use of depth information increased the prediction error. The spline function prediction was scarcely better than that of independent modeling. The proportional allocation methods performed better than other separate modeling methods for accuracy and interpretability with GLM or RF, especially for the middle and surface layers. The GLM generated more aggregated predictions than the RF, losing the distribution pattern of the original data. Therefore, we recommend RF combined with proportional allocation methods for spatial SOCD prediction in large-scale study areas. We calculated the SOC stocks in the Sanjiang Plain using our new methods, which were more reasonable compared with those of previous studies and had the advantages of in-depth information, environmental variable selection, and model optimization. Our findings provide not only other perspectives for SOCD mapping, with more fully integrated depth information and more accurate assessment of multilayer SOC stocks, but also provide guidance for the evaluation of land quality, farmland, and ecological environmental management.