Liang Zongzheng, Chen Songchao, Yang Yuanyuan, Zhou Yue, Shi Zhou
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
INRA Unité InfoSol, Orléans 45075, France; UMR SAS, INRA, Agrocampus Ouest, Rennes 35000, France.
Sci Total Environ. 2019 Oct 1;685:480-489. doi: 10.1016/j.scitotenv.2019.05.332. Epub 2019 May 28.
Soil organic carbon (SOC) is a key factor in soil fertility and structure and plays an important role in the global carbon cycle. However, SOC causes a large uncertainty in Earth System Models for predicting future climate change. The GlobalSoilMap (GSM) project aims to provide global digital soil maps of primary functional soil properties at six standard depth intervals (0-5, 5-15, 15-30, 30-60, 60-100, and 100-200 cm) with a grid resolution of 90 × 90 m. Currently, few SOC national products that meet the GSM specifications are available. This study describes the three-dimensional spatial modeling of SOC maps according to GSM specifications. We used 5982 soil profiles collected during the Second National Soil Survey of China, along with 16 environmental covariates related to soil formation. The results were obtained by parallel computing over tiles of 100 × 100 km, and the predictions for the tiles were subsequently merged into a single SOC map for the whole of China per standard GSM depth interval. For each standard GSM depth interval, SOC contents and their uncertainties were predicted and mapped at a spatial resolution of approximately 90 m using bootstrapping. Southwestern and northeastern China had higher SOC contents than the rest of China did, whereas northwestern China had a lower SOC content. The range of the coefficient of determination for the six depth intervals ranged from 0.35 to 0.02, and the mean SOC content was 17.86-8.67 g kg. Both these values decreased strongly with increasing soil depth. Cropland SOC content was lower than that of forest and grassland. The results of variable importance show that SoilGrids data were the best predictors for defining the soil-landscape relationship during regression modeling for SOC. These SOC maps can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor SOC dynamics, and a guide for the design of future soil surveys.
土壤有机碳（SOC）是土壤肥力和结构的关键因素，在全球碳循环中发挥着重要作用。然而，土壤有机碳在预测未来气候变化的地球系统模型中造成了很大的不确定性。全球土壤地图（GSM）项目旨在提供六个标准深度间隔（0 - 5、5 - 15、15 - 30、30 - 60、60 - 100和100 - 200厘米）的主要功能性土壤属性的全球数字土壤地图，网格分辨率为90×90米。目前，符合GSM规范的土壤有机碳国家产品很少。本研究描述了根据GSM规范进行的土壤有机碳地图三维空间建模。我们使用了在中国第二次全国土壤普查期间收集的5982个土壤剖面，以及16个与土壤形成相关的环境协变量。结果是通过对100×100公里的图块进行并行计算获得的，随后将这些图块的预测结果合并为每个标准GSM深度间隔的全中国单一土壤有机碳地图。对于每个标准GSM深度间隔，使用自抽样法以约90米的空间分辨率预测并绘制了土壤有机碳含量及其不确定性。中国西南部和东北部的土壤有机碳含量高于中国其他地区，而中国西北部的土壤有机碳含量较低。六个深度间隔的决定系数范围为0.35至0.02，平均土壤有机碳含量为17.86 - 8.67克/千克。这两个值都随着土壤深度的增加而显著降低。农田土壤有机碳含量低于森林和草地。变量重要性结果表明，SoilGrids数据是土壤有机碳回归建模期间定义土壤 - 景观关系的最佳预测因子。这些土壤有机碳地图可为环境建模提供数据源，为评估和监测土壤有机碳动态提供基准，并为未来土壤调查的设计提供指导。
