采样点空间集聚度对揭示区域土壤全氮变异性的影响  

Effects of Sampling Point Aggregation on Detecting Spatial Variability of Regional Soil Total Nitrogen

作　　者：孙益权 张忠启 于东升[2] 茆彭[1] SUN Yiquan;ZHANG Zhongql;YU Dongsheng;MAO Peng(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou,Jiangsu 221116,China;State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China)

机构地区：[1]江苏师范大学地理测绘与城乡规划学院,江苏徐州221116 [2]土壤与农业可持续发展国家重点实验室(中国科学院南京土壤研究所),南京210008

出　　处：《土壤》2018年第4期787-794,共8页Soils

基　　金：国家自然科学基金项目(41201213;41571206);土壤与农业可持续发展国家重点实验室开放基金项目(Y20160008)资助

摘　　要：基于余江县中部地区土壤密集采样点,通过重采样获得4个集聚度样点分布等级,每个等级取5个重复,其分布VMR均值(样方分析中的样点数变差–均值比)分别为0.13、0.83、1.49和2.16,利用普通克里金(OK)和结合土地利用信息的克里金(KLU)方法对土壤全氮(STN)含量进行空间预测,并通过验证样点比较了4种集聚度采样点的STN空间预测精度,以揭示土壤采样点集聚度对STN空间预测精度的影响。研究结果表明:通过两预测方法得到的验证点实测值与预测值散点图的相关系数(r)均随采样点集聚度的增加而降低,其中OK方法的r值由0.400降低至0.142,KLU方法的r值由0.718降低至0.542;两方法的预测平均绝对误差(MAE)和均方根误差(RMSE)随着集聚度的增加均呈现增加趋势,OK和KLU方法的MAE分别又由0.49和0.33增加至0.61和0.44,RMSE则分别由0.56和0.39增加到0.65和0.47。表明在土壤采样点数量相同的前提下,采样点的空间集聚度越低,即样点分布越均匀,克里金方法的预测精度越高,这说明对STN进行空间预测时,基于规则网格的采样点更有利于克里金方法的空间估算;同时,采样点空间集聚度对不同克里金方法预测精度的影响也存在差异,对KLU方法的影响大于OK方法。Based on dense soil samples collected from the middle-area of Yujiang County to get 4 aggregation grades of samples distribution by resample which included 5 repetitions in each one, the VMRs which represent the mean variance of samples in quadrat analysis were 0.13, 0.83, 1.49 and 2.16, respectively. Ordinary kriging （OK） and kriging combined with land use pattern information （KLU） were used to predict soil total nitrogen （STN） spatial distribution pattern, and 40 samples were validated to compare the prediction accuracy of these four aggregation grades, and to reveal the aggregation grades of samples distribution impact on prediction accuracy of STN. The results showed that the correlation coefficients r between measured and predicted STN contents from OK and KLU were decreased with increasing aggregation, and the r values reduced from 0.400 to 0.142 for OK and from 0.718 to 0.542 for KLU, respectively. The mean absolute errors （MAEs） and the root mean square errors （RMSEs） of STN from OK and KLU increased with increasing aggregation, and MAEs increased from 0.49 to 0.61 for OK and from 0.33 to 0.44 for KLU, and RMSEs increased from 0.56 to 0.65 for OK and from 0.39 to 0.47 for KLU, respectively, indicating that on the premise of the same number of samples, the lower aggregation that the samples distribution more uniform, the higher prediction accuracy by kriging. It shows that using the regular grid sampling is more conducive to spatial estimation when spatial prediction for STN, and the spatial aggregation of samples also have different impacts on the prediction accuracy with different kriging, and the impacts on KLU is greater than OK.

关 键 词：土壤全氮 样点集聚度 空间预测 红壤区 

分 类 号：S159[农业科学—土壤学]