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Priority selection rating of sampling density and interpolation method for detecting the spatial variability of soil organic carbon in China

机译:中国土壤有机碳空间变异性的抽样密度优先选择等级及内插法

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摘要

Soil sampling density and spatial interpolation method both have effects on interpreting the spatial variability of regional soil organic carbon (SOC). However, there are few comparisons of the effects between the two factors. Based on three soil sampling designs in Yujiang County, Jiangxi Province of China, the SOC spatial distributions in a specific area, imposed on three grid sampling densities of 2 × 2 km (G_(2×2)), 1 × 1 km (G_(1×1)), and 0.5 × 0.5 km (G_(0.5×0.5), were predicted via two interpolation methods: Ordinary Kriging (OK) and Kriging combined with land use information (LUK). Prediction accuracies from OK and LUK at three sampling densities were compared on the basis of 65 validation samples in the area. The results demonstrated that the correlation coefficients (r) between the measured and predicted values of validation locations obtained from OK (r = 0.212, 0.491 and 0.512) and LUK (r = 0.602, 0.776 and 0.875) increased with decreased grid size, and the root mean square errors (RMSE) from OK (RMSE = 6.79, 5.33, and 5.19 g kg~(-1)) and LUK (RMSE = 4.74, 3.60, and 3.14 g kg~(-1)) all decreased as expected with the sampling density increasing from G_(2×2) to G_(0.5×0.5). The rs from LUK were all higher and RMSEs were all lower than those from OK at three densities, respectively. More interesting, the prediction accuracy of LUK from G_(2×2) was not only lower than that of OK at same density, but also lower than those of OK at G_(1×1) and G_(0.5×0.5). This indicates that LUK can use several times fewer soil samples than OK to predict SOC spatial variability with same accuracy. The conclusion is that the efficient interpolation method not only makes sense to obtain high-precision SOC distribution information, but also can save lots of sampling points and research costs. Therefore, research on efficient interpolation method is a key step and should be paid more attention than increasing sampling points for revealing SOC spatial variability in the hilly red soil region of China, even in the regions with similar complex terrain.
机译:土壤采样密度和空间插值方法都对解释区域土壤有机碳(SOC)的空间变异性有影响。但是,这两个因素之间的影响很少有比较。基于江西省余江县的三种土壤采样设计,以3×2 km(G_(2×2)),1×1 km(G_ (1×1))和0.5×0.5 km(G_(0.5×0.5),通过两种插值方法进行了预测:普通克里格(OK)和结合土地利用信息(LUK)的克里格(Kriging),根据OK和LUK的预测精度在该区域的65个验证样本的基础上,对三个采样密度进行了比较,结果表明,从OK(r = 0.212、0.491和0.512)和LUK( r = 0.602、0.776和0.875)随着网格尺寸的减小而增加,并且OK(RMSE = 6.79、5.33和5.19 g kg〜(-1))和LUK(RMSE = 4.74、3.60)的均方根误差(RMSE)和3.14 g kg〜(-1))均按预期下降,采样密度从G_(2×2)增加到G_(0.5×0.5),来自LUK的rs均为在三个密度下,较高和RMSE均低于OK。更有趣的是,G_(2×2)的LUK预测精度不仅低于相同密度的OK的预测精度,而且也低于G_(1×1)和G_(0.5×0.5)的OK的预测精度。这表明,LUK可以用比OK少几倍的土壤样本来以相同的精度预测SOC空间变异性。结论是,有效的插值方法不仅对于获得高精度的SOC分布信息有意义,而且可以节省大量的采样点和研究成本。因此,研究有效的插值方法是关键步骤,即使是在地形复杂的地区,也要增加采样点,以揭示中国红壤丘陵区SOC的空间变异性。

著录项

  • 来源
    《Environmental earth sciences》 |2015年第5期|2287-2297|共11页
  • 作者单位

    State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China,Jiangsu Normal University, Xuzhou 221116, China;

    State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China,Graduate University of the Chinese Academy of Sciences, Beijing 100039, China;

    State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China,Graduate University of the Chinese Academy of Sciences, Beijing 100039, China;

    State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;

    State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China,Graduate University of the Chinese Academy of Sciences, Beijing 100039, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Soil organic carbon (SOC); Spatial interpolation method; Sampling density; SOC spatial variability; Red soil hill region of China;

    机译:土壤有机碳(SOC);空间插值法;采样密度SOC空间变异性;中国红土山丘地区;

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