综合土地利用及空间异质性的土壤有机碳空间插值模型

Spatial interpolation model of soil organic carbon density considering land-use and spatial heterogeneity

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碳循环有着重要的影响.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SOCD空间分布对发展精确农业有重要意义.本文使用江汉平原地区242个农田土壤样本数据,探究平原地区土地利用类型对SOCD空间分布的影响,以及当SOCD空间分布规律呈现空间异质性且存在空间异常值的情况下,虚拟变量回归克里格法(DV_RK)、均值中心化克里格法(MC_OK1)和中位数中心化克里格法(MC_OK2)这3种结合土地利用的克里格法在SOCD空间预测中的应用.结果表明:土地利用方式的差异是研究区水田和水浇地SOCD存在空间异质性的原因之一,导致SOCD存在空间非平稳特征,降低了普通克里格法(OK)的预测精度;而DV_RK、MC_OK1和MC_OK2在消除了由土地利用引起的SOCD空间异质性对建模的影响后,模型的稳定性提升,其预测精度均高于OK,其中,MC_OK2的模型可靠程度、预测精度和对SOCD总方差的解释能力最优.因此,土地利用类型作为易获取的辅助变量,可以有效减弱空间异质性和空间异常值对SOCD空间插值的影响,提升模型精度,降低不确定性,并与MC_OK2结合生成更高精度的SOCD空间分布图,帮助揭示SOCD空间分异规律,指导农业生产.

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.