不同采样点数量下土壤有机质含量空间预测方法对比

运用普通克里格、泛克里格、协同克里格和回归克里格4种方法，结合由DEM获取的高程因子以及土壤全氮和阳离子交换量（CEC），预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明，样点数量多并不一定能够识别土壤有机质含量的结构性连续组分，最优化的布置采样点位置可能比单纯增加...

A comparison of predictive methods for mapping the spatial distribution of soil organic matter content with different sampling densities

Soil organic matter(SOM) is extremely important for maintaining soil fertility and linking carbon cycle within terrestrial ecosystem.In order to quantify the spatial patterns of SOM content in Hailun County of China,ordinary kriging(OK),universal kriging(UK),co-kriging(CK),regression kriging(RK),and secondary data derived from soil total nitrogen(TN),cation exchangeable capacity(CEC) and digital elevation model(DEM) were employed to map the spatial distribution patterns of SOM with different sampling densities.The results of SOM spatial structures identified by different sampling densities indicated that the increase of soil sampling densities may not benefit the identification of SOM continuous component and optimal design of sampling locations was even more important than improving sampling densities.With respect to spatial predictive performance,CK with TN as co-variable always had the highest accuracy,and more than 50% of the SOM variation can be explained by CK method.The local detail of SOM variability derived from CK method also outperformed OK,UK,and RK,and the predictive accuracy of RK method may not be improved when compared with univariate OK method.The soil sampling density is not the exclusive factor affecting the spatial predictive accuracy of SOM,and secondary data(ancillary information) significantly correlated to SOM as well as denser coverage of the study area would be more helpful for improving the spatial predictive performance of SOM with limited number of soil samples.