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为探讨大尺度区域土壤属性空间化的方法,以吉林省为例,研究土壤养分(pH值、有机质、速效磷、速效钾和碱解氮)和地形地貌、微量元素等变量之间的关系;并在考虑土壤类型基础上,将相关性较高的变量作为协因子进行土壤养分的Cokriging插值研究。结果表明,pH值与经度、有效铁、锰和速效氮的相关系数分别高达-0.66、-0.71、-0.70和-0.67;有机质与经度、pH值、有效钙、锰的相关系数分别为0.55、-0.58、0.56和0.52;碱解氮与经度、纬度、pH值、有效铁、锌的相关系数分别为0.57、-0.57、-0.67、0.56、0.54;速效磷与速效钾、有效锌的相关系数分别为0.67和0.64。分析发现以相关性较高的微量元素作为协因子进行Cokriging插值精度均优于采用地形变量作为协因子的Cokriging插值。交叉检验和检验站验证结果表明,与普通Kriging相比,基于土壤类型和微量元素的Cokriging插值在增加估值精度方面有所提高。
In order to explore the method of spatialization of soil properties in large-scale area, taking Jilin Province as an example, the relationships among soil nutrients (pH, organic matter, available phosphorus, available potassium and available nitrogen) and topography, trace elements and other variables were studied. Based on the soil types, Cokriging interpolation of soil nutrients was used as a co-factor with higher correlations. The results showed that the correlation coefficient between pH value and longitude, available iron, manganese and available nitrogen were as high as -0.66, -0.71, -0.70 and -0.67, respectively. The correlation coefficient between organic matter and longitude, pH, available calcium and manganese were 0.55, -0.58, 0.56 and 0.52, respectively. The correlation coefficient between available nitrogen and longitude, latitude, pH value, available iron and zinc were 0.57, -0.57, -0.67, 0.56 and 0.54 respectively; the correlation coefficient between available phosphorus and available potassium and available zinc Respectively 0.67 and 0.64. The analysis shows that the Cokriging interpolation accuracy is better than the Cokriging interpolation using the topographic variables as the co-factor. Cross-validation and validation of validation stations show that Cokriging interpolation based on soil type and trace elements improves the accuracy of the estimates compared to normal Kriging.