塔里木盆地北缘盐土有机碳含量的贝叶斯地统计预测

发布时间：2018-02-20 20:56

本文关键词： 土壤有机碳空间预测贝叶斯地统计盐土　出处：《应用生态学报》2017年02期 　论文类型：期刊论文

【摘要】：准确预测土壤有机碳的空间分布,对于土壤资源开发和保护、应对气候变化和生态系统健康都具有重要意义.本文以塔里木盆地北缘盐土1300 m×1700 m样地为试验区,采集5~10 cm深度土壤样品144个,构建土壤有机碳含量的贝叶斯地统计空间预测模型,并以普通克里格、序惯高斯模拟和逆距离加权方法为对照,评价贝叶斯地统计对土壤有机碳含量的预测性能.结果表明:研究区土壤有机碳含量处于1.59~9.30 g·kg~(-1),平均值为4.36 g·kg~(-1),标准偏差为1.62 g·kg~(-1);半方差函数符合指数模型,空间结构比参数值为0.57;利用贝叶斯地统计方法,获得了土壤有机碳含量的空间分布图以及评价预测不确定性的预测方差、上95%分位数、下95%分位数分布图;与普通克里格、序惯高斯模拟和逆距离加权方法相比,贝叶斯地统计方法具有更高的土壤有机碳含量空间预测精度,显示出该方法对土壤有机碳含量预测的优越性.
[Abstract]:The accurate prediction of soil organic carbon spatial distribution is of great significance for the development and protection of soil resources, the response to climate change and ecosystem health. In this paper, 1 300 m 脳 1 700 m saline soil samples in the northern margin of the Tarim Basin are taken as the experimental area. A Bayesian statistical spatial prediction model of soil organic carbon content was constructed by collecting 144 soil samples at a depth of 5 ~ 10 cm, and compared with ordinary Kriging, sequential habitual Gao Si simulation and inverse distance weighting method. The prediction performance of Bayesian geostatistics on soil organic carbon content was evaluated. The results showed that the soil organic carbon content in the study area was at 1.59 ~ 9.30 g 路kg ~ (-1) g 路kg ~ (-1), the average value was 4.36 g 路kg ~ (-1) ~ (-1), the standard deviation was 1.62 g 路kg ~ (-1) ~ (-1), and the semi-variance function was in accordance with the exponential model. The spatial structure ratio parameter is 0.57. By using Bayesian statistical method, the spatial distribution map of soil organic carbon content and the prediction variance of evaluating prediction uncertainty are obtained, the upper 95 quartile, the lower 95 quartile, the lower 95 quartile distribution map, and the ordinary Kriging. Compared with the inverse distance weighting method, the Bayesian geostatistics method has higher precision of spatial prediction of soil organic carbon content, which shows the superiority of this method in predicting soil organic carbon content.
【作者单位】：浙江大学环境与资源学院;塔里木大学植物科学学院;浙江大学海岛海岸带研究所;
【基金】：国家自然科学基金项目(40961028)资助~~
【分类号】：S153.6

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