| 基于土壤类型和微量元素辅助信息的土壤属性空间模拟 |
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| 引用本文： | 石淑芹,陈佑启,李正国,杨 鹏,吴文斌,姚艳敏.基于土壤类型和微量元素辅助信息的土壤属性空间模拟[J].农业工程学报,2010,26(12):199-205. |
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| 作者姓名： | 石淑芹 陈佑启 李正国 杨 鹏 吴文斌 姚艳敏 |
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| 作者单位： | 1. 农业部资源遥感与数字农业重点开放实验室,北京,100081;天津工业大学管理学院,天津,300387
2. 农业部资源遥感与数字农业重点开放实验室,北京,100081;中国农科院农业资源与农业区划研究所,北京,100081 |
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| 基金项目： | 国家重点基础研究发展计划“973”计划项目（2010CB951500）；国家自然科学基金项目（40930101和40801221）；农业部资源遥感与数字农业重点开放实验室基金（RDA1010）；天津市高等学校人文社会科学研究项目（20092115）；教育部人文社会科学研究项目（10YJCZH129） |
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| 摘 要： | 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。
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| 关 键 词： | 土壤，计算机仿真，插值，微量元素 |
| 收稿时间： | 2010/3/25 0:00:00 |
| 修稿时间： | 9/8/2010 12:00:00 AM |
Spatial interpolation of soil properties based on soil types and trace micro-elements |
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| Shi Shuqin,Chen Youqi,Li Zhengguo,Yang Peng,Wu Wenbin and Yao Yanmin.Spatial interpolation of soil properties based on soil types and trace micro-elements[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(12):199-205. |
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| Authors: | Shi Shuqin Chen Youqi Li Zhengguo Yang Peng Wu Wenbin and Yao Yanmin |
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| Abstract: | To develop the methodology for spatialization of large scale soil property, we firstly investigated the relationship between soil properties and environmental factors in Jilin province located in Northeast China. Secondly, with a consideration of soil types, the relevant factors were utilized as co-factors for interpolating soil properties by using the means of Cokriging technique. The results showed that soil pH value, soil organic matter and alkali-hydrolyzable N were generally correlated with latitude and longitude. In details, soil pH value had an obviously negative correlation with longitude (-0.66), available iron (-0.71), manganese (-0.70) and active nitrogen (-0.67); Negative correlation could be found between soil organic matter and soil pH value (-0.58), while positive between soil organic matter and longitude (0.55), available calcium (0.560) and manganese (0.52); in addition, alkali-hydrolyzable N was negatively correlated with soil pH (-0.67) and latitude (-0.57), and positively correlated with longitude (0.57), available iron (0.56) and zinc (0.54); and active phosphorus had an obviously positive correlation with active potassium (0.67) and zinc (0.64). By contrast, the accuracy of Cokriging interpolations used with more relevant co-factors (i.e. trace elements) was obviously higher than the interpolations with other factors (i.e. topographic factors). In addition, the verification results of cross-validation and testing station also prove that accuracy of Cokrigng interpolations is higher than ordinary Kriging. |
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| Keywords: | soils computer simulation interpolation trace elements |
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