基于条件拉丁超立方抽样的县域耕地土壤有机质空间插值合理样本密度的确定  

Determination of Reasonable Sample Density for Spatial Interpolation of Soil Organic Matter in Cultivated Land of County Region Based on Conditional Latin Hypercube Sampling

作　　者：李维友 段良霞 谢红霞[1] 胡碧峰 周清[1] 罗喆 LI Wei-you;DUAN Liang-xia;XIE Hong-xia;HU Bi-feng;ZHOU Qing;LUO Zhe(College of Resources and Environment,Hunan Agricultural University,Changsha 410000,China;School of Tourism and Urban Management,Jiangxi University of Finance and Economics,Nanchang 330013,China;Hunan Soil and Fertilizer Station,Changsha 410000,China)

机构地区：[1]湖南农业大学资源环境学院,湖南长沙410000 [2]江西财经大学旅游与城市管理学院,江西南昌330013 [3]湖南省土壤肥料工作站,湖南长沙410000

出　　处：《土壤通报》2022年第3期505-513,共9页Chinese Journal of Soil Science

基　　金：国家科技基础性工作专项项目课题(2014FY110200);湖南省教育厅重点科学研究项目(19A242)资助。

摘　　要：【目的】采样密度与耕地土壤有机质(SOM)的空间预测精度密切相关,为提高耕地SOM空间预测精度,需要确定合理的采样密度。【方法】以湖南省岳阳县为例,用R语言设计了条件拉丁超立方体抽样(cLHS)方案,从7399个(采样密度为14.82个km^(−2))耕地土壤样本中独立抽取不同采样密度的8个训练集(采样密度分别为10.01、7.41、3.70、1.85、0.93、0.46、0.23、0.12个km^(−2)),为了兼顾样本特征空间与地理空间,地形部位、坡度、成土母质、土壤类型、乡镇和经纬度等信息被添加到了cLHS中。结合普通克里格方法,分析和探讨了不同采样密度的耕地SOM空间预测效果。【结果】不同采样密度训练集SOM均值高于湖南省平均水平,具有中等程度变异,描述性统计结果差异不大,各训练集对总体均具有较强的代表性;半方差函数模型均为指数模型,具有较好的半方差结构(结构性比例:87.5%~94.5%),空间相关性较强,变程与拟合优度呈现出正相关关系(相关系数r=0.96),与结构性比例则表现为负相关关系(相关系数r=−0.79);在采样密度为3.70个km^(−2)时,探测到的SOM变异结构中结构性组分最完整,精度最佳。当采样密度达到1.85个km^(−2)以上时可较稳健地揭示其空间结构特征,继续增加采样密度并不能大幅提升预测精度。【结论】考虑预测精度要求和工作成本,与研究区自然地理条件相似的地区将耕地土壤采样密度控制在1.85个km^(−2)以上可获得预期的效果。[Objective]Sampling density is closely related to the spatial prediction accuracy of soil organic matter(SOM)in cultivated land.Determining the reasonable sampling density of SOM spatial prediction in cultivated land is beneficial to scientifically guide soil sampling and save work cost.[Method]In this study,taking Yueyang County of Hunan Province as an example,the conditional Latin Hypercube Sampling(cLHS)scheme was designed with R language.Eight training sets with different sampling densities(10.01,7.41,3.70,1.85,0.93,0.46,0.23 and 0.12 samples per km^(2))were independently extracted from 7399 cultivated land soil samples(the sampling density was 14.82 samples per km^(2)).In order to take into account the sample feature space and geographical space,the topographic position,slope,soil parent material,soil type,township,longitude and latitude and other information were added to the cLHS.Combined with ordinary Kriging method,the spatial prediction effect of SOM in cultivated land with different sampling densities was analyzed and discussed.[Result]The results showed that the average of SOM in the training set with different sampling densities was higher than the average level of Hunan Province.Each training set had strong representativeness on the whole.Semi-variance function models were exponential models,with good semi-variance structure(structural ratio:87.5%-94.5%)and strong spatial correlation.The range was positively correlated with the goodness of fit(correlation coefficient r=0.96),and negatively correlated with the structural ratio(correlation coefficient r=−0.79).When the sampling density was 3.70 samples per km^(2),the structural components in the detected variation structure of SOM was the most complete,and the accuracy was the best.Further increasing the sampling density was not necessarily conducive to the identification of structural continuous components.When the sampling density was lower than 0.46 samples per km^(2),the semi-variance function model could not be effectively inferred,and the accu

关 键 词：土壤有机质 条件拉丁超立方抽样 普通克里格 空间预测 合理样本密度 

分 类 号：S159[农业科学—土壤学]