Wu Zi Hao, Liu Yan Fang, Chen Yi Yun, Guo Long, Jiang Qing Hu, Wang Shao Chen
School of Resource and Environment Science, Wuhan University, Wuhan 430079, China.
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China.
Ying Yong Sheng Tai Xue Bao. 2018 Jan;29(1):238-246. doi: 10.13287/j.1001-9332.201801.013.
Soil organic carbon pool is an important component of terrestrial carbon pool. Soil organic carbon pool and its dynamic change have important influence on carbon cycle in terrestrial ecosystem. Soil organic carbon density (SOCD) is an important parameter of soil carbon storage, and it is also an important index to evaluate farmland soil quality. Accurate prediction of regional organic carbon density spatial distribution is of great significance to the development of precision agriculture. A total of 242 farmland soil samples collected from the Jianghan Plain were used to explore the effects of land use types on the spatial distribution of SOCD in plain areas. Moreover, in the presence of spatial heterogeneity and spatial outliers of SOCD, three Kriging approaches combining land use types were used for the spatial prediction of SOCD. They were dummy variable regression Kriging (DV_RK), mean centering ordinary Kriging (MC_OK1) and median centering ordinary Kriging (MC_OK2). Results showed that the difference of land use types between paddy field and irrigable land was one of the reasons for the spatial heterogeneity of SOCD in the study area, resulting in spatial non-stationary characteristics of SOCD and lowering the performance of OK. DV_RK, MC_OK1 and MC_OK2, however, eliminating the impacts of SOCD spatialheterogeneity caused by land use types while modeling, enhancing the model stability. Therefore, the prediction accuracy of these three models was higher than that of ordinary Kriging (OK). Moreover, MC_OK2 outperformed the others in terms of model reliability, prediction accuracy and the ability to explain the total variance of SOCD. In summary, as an easily accessed auxiliary variable, land use type could effectively decrease the effects of spatial heterogeneity and spatial outliers on SOCD spatial interpolation model, improving the prediction performance and reducing the model uncertainty. SOCD map with higher quality could also be achieved to help reveal the spatial characteristics of SOCD for guiding the agricultural production.
土壤有机碳库是陆地碳库的重要组成部分。土壤有机碳库及其动态变化对陆地生态系统的碳循环具有重要影响。土壤有机碳密度（SOCD）是土壤碳储量的重要参数，也是评价农田土壤质量的重要指标。准确预测区域有机碳密度的空间分布对精准农业的发展具有重要意义。本研究以江汉平原采集的242个农田土壤样本为对象，探讨土地利用类型对平原地区SOCD空间分布的影响。此外，针对SOCD存在的空间异质性和空间离群值，采用三种结合土地利用类型的克里金方法对SOCD进行空间预测，分别为虚拟变量回归克里金（DV_RK）、均值中心化普通克里金（MC_OK1）和中位数中心化普通克里金（MC_OK2）。结果表明，水田与水浇地土地利用类型的差异是研究区SOCD空间异质性的原因之一，导致SOCD的空间非平稳特征，降低了普通克里金（OK）的性能。然而，DV_RK、MC_OK1和MC_OK2在建模时消除了土地利用类型引起的SOCD空间异质性影响，增强了模型稳定性。因此，这三种模型的预测精度均高于普通克里金（OK）。此外，MC_OK2在模型可靠性、预测精度和解释SOCD总方差的能力方面均优于其他模型。综上所述，土地利用类型作为一种易于获取的辅助变量，能够有效降低空间异质性和空间离群值对SOCD空间插值模型的影响，提高预测性能，降低模型不确定性，还能得到更高质量的SOCD图，有助于揭示SOCD的空间特征，指导农业生产。
