Underestimation of soil organic carbon by global spatially explicit soil datasets in Chinese urban greenspaces

Soil organic carbon (SOC) in urban greenspaces can provide important ecosystem services for a large number of urban population around the world. However, the distribution, stocks, and drivers of SOC change in urban greenspaces are relatively unknown compared with those of natural ecosystems. To address this knowledge gap, we utilized 744 SOC measurements (0-30 cm depth) collected from urban greenspaces across 148 cities in China to investigate the key the distribution and drivers of surface SOC stocks. Our analysis revealed weak correlations between in-field SOC measurements and estimates derived from widely used global SOC datasets (<i>e.g.,</i> SoilGrids250m v2.0 and HWSD v2.0), underscoring the necessity for improved characterization of SOC distribution and its determinants in urban greenspaces. By employing Random Forest (RF) and SHapley Additive exPlanations (SHAP), we found that the enhanced vegetation index (EVI), mean annual precipitation (MAP), and social-economic status indicated by night-time light density (NLD) and population density (POP) were the most important predictors of surface SOC in Chinese urban greenspaces. RF model-predicted surface SOC in urban greenspaces was higher in moderately developed cities than in underdeveloped or highly developed cities. SOC increased with EVI, but displayed a hump-shaped trend with MAP, NLD, and POP. Spatial prediction of SOC stocks from the RF model showed that there were 0.21 (0.20-0.22, 95% confidence intervals) Pg C in the top 30 cm of soil in urban greenspaces of China, which was 17% and 50% greater than estimated by SoilGrids250m v2.0 (0.18 Pg C) and HWSD v2.0 (0.14 Pg C), respectively. Our study provides valuable data support and scientific guidance for carbon neutrality and urban greenspaces ecosystem management in China.

城市绿地中的土壤有机碳（Soil Organic Carbon, SOC）可为全球范围内大量城市人口提供重要的生态系统服务。然而，相较于自然生态系统，当前学界对城市绿地中SOC的分布、储量及其变化驱动因子的认知仍相对匮乏。为填补这一研究空白，本研究依托中国148座城市绿地中采集的744组0-30cm深度SOC实测数据，探究了表层SOC储量的关键分布特征与驱动因子。分析结果显示，野外实测SOC数据与主流全球SOC数据集（如SoilGrids250m v2.0与HWSD v2.0）的估算结果相关性较弱，这凸显了对城市绿地SOC分布及其主控因子开展精细化表征的必要性。本研究借助随机森林（Random Forest, RF）模型与SHAP加性解释（SHapley Additive exPlanations, SHAP）分析方法，发现增强型植被指数（Enhanced Vegetation Index, EVI）、年平均降水量（Mean Annual Precipitation, MAP）以及由夜间灯光密度（Night-time Light Density, NLD）与人口密度（Population Density, POP）表征的社会经济状况，是影响中国城市绿地表层SOC的核心预测因子。随机森林模型预测结果显示，中等发展水平城市的城市绿地表层SOC储量高于欠发达或高度发达城市。SOC储量随EVI升高而增加，但与MAP、NLD及POP呈驼峰型变化趋势。基于随机森林模型的SOC储量空间预测结果表明，中国城市绿地0-30cm土层的SOC总储量为0.21（95%置信区间：0.20-0.22）拍克碳（Pg C），分别比SoilGrids250m v2.0（0.18 Pg C）与HWSD v2.0（0.14 Pg C）的估算值高出17%与50%。本研究可为中国的碳中和目标实现与城市绿地生态系统管理提供宝贵的数据支撑与科学指导。