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为了探讨在条件模拟计算环境下,是否可以利用高程数据辅助提高土壤有机质空间变化的预测精度及相应的预测不确定性模拟的准确性,该文在北京市平谷区内选取研究样区,以土壤有机质作为目标变量,一方面利用序贯高斯模拟法对土壤有机质的空间分布进行模拟,另一方面以高程作为辅助信息,利用序贯高斯协模拟法对土壤有机质的空间分布进行模拟,然后对两种方法的模拟结果进行对比分析。结果表明,在土壤有机质的空间预测精度、模拟预测结果的局部不确定性和模拟预测结果的空间不确定性三方面,通过将高程数据考虑进有机质条件模拟过程中,准确性都得到了提高。这对于农业可持续发展以及全球碳平衡研究都具有十分重要的意义。
In order to explore whether the elevation data can be used to improve the prediction accuracy of the spatial variation of soil organic matter and the accuracy of the corresponding prediction uncertainty simulation in the conditions simulation and calculation environment, this paper selected the research sample area in Pinggu District, Organic matter as the target variable. On the one hand, the spatial distribution of soil organic matter was simulated by using sequential Gaussian simulation. On the other hand, the spatial distribution of soil organic matter was simulated by using sequential Gaussian co-simulation method with elevation as auxiliary information. The simulation results of different methods are compared and analyzed. The results show that the accuracy of soil organic matter has been improved by considering the accuracy of spatial prediction of soil organic matter, the local uncertainty of the simulated prediction result and the spatial uncertainty of the simulated prediction result. This is of great significance to the sustainable development of agriculture and the study of global carbon balance.