| 县域尺度土壤有机碳储量估算的样点密度优化 |
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| 引用本文： | 上官魁星,吴金水,周脚根,朱捍华.县域尺度土壤有机碳储量估算的样点密度优化[J].土壤学报,2014,51(1):41-48. |
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| 作者姓名： | 上官魁星 吴金水 周脚根 朱捍华 |
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| 作者单位： | 中国科学院亚热带农业生态研究所；中国科学院大学,中国科学院亚热带农业生态研究所,中国科学院亚热带农业生态研究所,中国科学院亚热带农业生态研究所 |
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| 基金项目： | 中国科学院战略性先导科技专项-应对气候变化的碳收支认证及相关问题（XDA05050505）、国家自然科学基金项目（41090283，41201299和41001141）资助 |
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| 摘 要： | 县域是我国国家尺度土壤碳库估算的基本地域单元,合理的土壤样品采集密度是保证估算精度要求的基础。以桃源县为例,设置4.70、0.90、0.60、0.40、0.25、0.15、0.10和0.05个km-2共8个样点密度梯度,利用经典统计学和地统计学方法,研究样点密度对县域尺度土壤有机碳库估算精度的影响。经典统计表明,随着样点密度的降低,重复抽样下土壤有机碳均值及其变异系数的波动逐渐增大,标准误差呈幂函数增加(Y=0.025X-0.47,R2=0.97,p0.01)。地统计学分析表明,随着样点密度的降低,块金值和基底效应逐渐增加,偏基台、变程和决定系数的波动幅度逐渐增大,拟合残差呈幂函数增加(Y=0.001 4X-1.66,R2=0.56,p0.05);土壤有机碳空间分布的局部差异逐渐被弱化,重复抽样下县域土壤有机碳库储量及其平均误差的波动逐渐增强,均方根误差呈幂函数增加(Y=0.77X-0.05,R2=0.59,p0.05)。从整体上看,样点密度小于0.15个km-2时,以上变化均急剧增强,土壤碳储量估算的精度快速降低。因此,综合科学、高效和经济方面的考虑,估算县域农田表层土壤有机碳储量的最佳样点密度为0.15个km-2。本研究结果可为开展区域尺度土壤有机碳野外调查提供辅助支持。
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| 关 键 词： | 县域尺度 土壤有机碳 样点密度 优化 半方差函数 |
| 收稿时间： | 2013/1/28 0:00:00 |
| 修稿时间： | 2013/4/24 0:00:00 |
Optimum density of sampling for estimation of soil organic carbon stock at a county scale |
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| Shangguan Kuixing,Wu Jinshui,Zhou Jiaogen and Zhu Hanhua.Optimum density of sampling for estimation of soil organic carbon stock at a county scale[J].Acta Pedologica Sinica,2014,51(1):41-48. |
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| Authors: | Shangguan Kuixing Wu Jinshui Zhou Jiaogen and Zhu Hanhua |
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| Abstract: | County is the basic unit for estimating soil organic carbon stock in China at the country scale. Rational soil sampling density is fundamental to assurance of a desired accuracy of the estimation. A case study of Taoyuan County was performed and designed to have 8 levels of sampling density, i.e. 4.70, 0.90, 0.60, 0.40, 0.25, 0.15, 0.10, and 0.05 samples per km-2, to explore effect of sampling density on accuracy of the estimation of soil organic carbon stock at a county scale. Data of the sampling were analyzed with the classical statistical and geo-statistical methods. Classical statistical analysis shows that lower sampling density increased fluctuation of the mean of soil organic carbon and its variation coefficient, and the standard errors (Y) as a power function of sampling density (X) (Y=0.025X-0.47, R2=0.97, p<0.01). Geo-statistical analysis discovers that lower sampling density increased nugget value and nugget-to-sill ratio, and fluctuation of partial sill, range, and R2 as well, and residual error (Y), too, as a power function of sampling density (X) (Y=0.0014X-1.66, R2=0.56, p<0.05). Meanwhile, the variation of spatial distribution of soil organic carbon in a small locality gradually weakened, and fluctuation of the estimation of soil organic carbon stock and the associated mean error gradually intensified, while the root mean square error (Y) increased as a power function of sampling density (X) (Y=0.77X-0.05, R2=0.59, p<0.05). As a whole, when the sampling density dropped below 0.15 samples per km2, all the above-mentioned indices intensified and as a result, accuracy of the estimation of soil organic carbon stock declined drastically. In overall consideration of the balance between economic inputs and the accuracy demand, the authors propose that the optimal sampling density for estimation of soil organic carbon stock at a county scale is 0.15 samples per km2. |
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| Keywords: | Country scale Soil organic carbon Sampling density Optimization Semivariogram |
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