基于条件拉丁超立方抽样的县域耕地土壤有机质空间插值合理样本密度的确定

李维友; 段良霞; 谢红霞; 胡碧峰; 周清; 罗喆

doi:10.19336/j.cnki.trtb.2021081801

基于条件拉丁超立方抽样的县域耕地土壤有机质空间插值合理样本密度的确定

doi: 10.19336/j.cnki.trtb.2021081801

李维友^1,,
段良霞¹,
谢红霞^1, ,,
胡碧峰²,
周清¹,
罗喆^{1, 3}

1.
湖南农业大学资源环境学院，湖南长沙 410000
2.
江西财经大学旅游与城市管理学院，江西南昌 330013
3.
湖南省土壤肥料工作站，湖南长沙 410000

基金项目: 国家科技基础性工作专项项目课题（2014FY110200）和湖南省教育厅重点科学研究项目（19A242）资助

详细信息

作者简介:
李维友（1995−），男，湖南省邵阳市人，硕士研究生，主要研究方向为土地资源与信息技术。E-mail: Leviyer@163.com

通讯作者:
E-mail: xiehongxia136@sina.com

中图分类号: S159
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出版历程
- 收稿日期: 2021-08-18
- 录用日期: 2022-01-27
- 修回日期: 2021-12-29
- 刊出日期: 2022-06-01

Determination of Reasonable Sample Density for Spatial Interpolation of Soil Organic Matter in Cultivated Land of County Region Based on Conditional Latin Hypercube Sampling

1.
College of Resources and Environment, Hunan Agricultural University, Changsha 410000, China
2.
School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
3.
Hunan Soil and Fertilizer Station, Changsha 410000, China

摘要

摘要: 目的采样密度与耕地土壤有机质（SOM）的空间预测精度密切相关，为提高耕地SOM空间预测精度，需要确定合理的采样密度。方法以湖南省岳阳县为例，用R语言设计了条件拉丁超立方体抽样（cLHS）方案，从7399个（采样密度为14.82个 km⁻²）耕地土壤样本中独立抽取不同采样密度的8个训练集（采样密度分别为10.01、7.41、3.70、1.85、0.93、0.46、0.23、0.12个 km⁻²），为了兼顾样本特征空间与地理空间，地形部位、坡度、成土母质、土壤类型、乡镇和经纬度等信息被添加到了cLHS中。结合普通克里格方法，分析和探讨了不同采样密度的耕地SOM空间预测效果。结果不同采样密度训练集SOM均值高于湖南省平均水平，具有中等程度变异，描述性统计结果差异不大，各训练集对总体均具有较强的代表性；半方差函数模型均为指数模型，具有较好的半方差结构（结构性比例：87.5% ~ 94.5%），空间相关性较强，变程与拟合优度呈现出正相关关系(相关系数r = 0.96)，与结构性比例则表现为负相关关系(相关系数r = −0.79)；在采样密度为3.70个 km⁻²时，探测到的SOM变异结构中结构性组分最完整，精度最佳。当采样密度达到1.85个 km⁻²以上时可较稳健地揭示其空间结构特征，继续增加采样密度并不能大幅提升预测精度。结论考虑预测精度要求和工作成本，与研究区自然地理条件相似的地区将耕地土壤采样密度控制在1.85个 km⁻²以上可获得预期的效果。
- 土壤有机质 /
- 条件拉丁超立方抽样 /
- 普通克里格 /
- 空间预测 /
- 合理样本密度
Abstract: Objective Sampling density is closely related to the spatial prediction accuracy of soil organic matter (SOM) in cultivated land. Determining the reasonable sampling density of SOM spatial prediction in cultivated land is beneficial to scientifically guide soil sampling and save work cost. Method In this study, taking Yueyang County of Hunan Province as an example, the conditional Latin Hypercube Sampling (cLHS) scheme was designed with R language. Eight training sets with different sampling densities (10.01, 7.41, 3.70, 1.85, 0.93, 0.46, 0.23 and 0.12 samples per km²) were independently extracted from 7399 cultivated land soil samples (the sampling density was 14.82 samples per km²). In order to take into account the sample feature space and geographical space, the topographic position, slope, soil parent material, soil type, township, longitude and latitude and other information were added to the cLHS. Combined with ordinary Kriging method, the spatial prediction effect of SOM in cultivated land with different sampling densities was analyzed and discussed. Result The results showed that the average of SOM in the training set with different sampling densities was higher than the average level of Hunan Province. Each training set had strong representativeness on the whole. Semi-variance function models were exponential models, with good semi-variance structure (structural ratio: 87.5%-94.5%) and strong spatial correlation. The range was positively correlated with the goodness of fit (correlation coefficient r = 0.96), and negatively correlated with the structural ratio (correlation coefficient r = −0.79). When the sampling density was 3.70 samples per km², the structural components in the detected variation structure of SOM was the most complete, and the accuracy was the best. Further increasing the sampling density was not necessarily conducive to the identification of structural continuous components. When the sampling density was lower than 0.46 samples per km², the semi-variance function model could not be effectively inferred, and the accuracy was poor. When the sampling density reached more than 1.85 samples per km², the spatial structure characteristics could be revealed more robustly. Continuously increasing sampling density would not significantly improve prediction accuracy. Conclusion Considering the requirements of prediction accuracy and working cost, in areas similar to the natural and geographical conditions of the study area, the expected effect can be obtained by controlling the soil sampling density of cultivated land above 1.85 samples per km².
- Soil organic matter /
- Conditional Latin Hypercube Sampling /
- Ordinary Kriging /
- Spatial prediction /
- Reasonable sampling density

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图 1 研究区基础数据

Figure 1. Basic data of the study area

下载: 全尺寸图片幻灯片

图 2 全部样点的有机质含量频率分布直方图及统计特征

Figure 2. Frequency distribution histogram and statistical characteristics of soil organic matter (SOM) content of all the sampling sites

下载: 全尺寸图片幻灯片

图 3 不同采样密度下OK插值精度的比较

Figure 3. Accuracy comparison of Ordinary Kriging interpolation as affected by sampling density

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图 4 不同采样密度下土壤有机质空间分布图

Figure 4. Spatial distribution of soil organic matter as affected by sampling density

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表 1 不同采样密度下有机质含量统计特征

Table 1. Descriptive statistics of soil organic matter contents as affected by sampling density

采样密度(个 km⁻²) Sampling density	样点数(个) Sampling number	均值(g kg⁻¹) Mean	中值(g kg⁻¹ ) Median	标准偏差（g kg⁻¹） Standard deviation	偏度 Skewness	峰度 Kurtosis	变异系数(%) CV
10.01	5000	33.16	33.40	8.49	−0.01	0.34	25.61
7.41	3700	33.06	33.30	8.51	−0.04	0.38	25.75
3.70	1850	33.13	33.60	8.65	−0.09	0.33	26.13
1.85	925	33.31	33.80	8.60	−0.08	0.64	25.82
0.93	463	33.68	33.20	8.37	0.24	1.07	24.85
0.46	232	32.90	33.20	8.36	−0.21	0.35	25.42
0.23	116	32.86	34.85	9.23	−0.40	−0.16	28.08
0.12	58	31.72	33.15	8.16	−0.50	0.47	25.73

下载: 导出CSV

表 2 不同采样密度下有机质含量的半方差模型及其参数

Table 2. Semivariance models and parameters of soil organic matter contents as affected by sampling density

采样密度(个 km⁻²) Sampling density	样点数(个) Sampling number	最优模型 Optimal fitting model	变程(km) Range	块金值 C₀	基台值 C + C₀	结构性比例(%) 1−C₀/(C + C₀)	拟合优度 R²	残差平方和 RSS
10.01	5000	指数模型	8.52	5.30	70.57	92.50	0.85	4.38
7.41	3700	指数模型	9.00	5.70	71.25	92.00	0.90	3.65
3.70	1850	指数模型	8.70	5.10	72.84	93.00	0.83	5.95
1.85	925	指数模型	10.74	6.70	73.49	90.90	0.92	5.53
0.93	463	指数模型	7.38	5.10	68.47	92.60	0.69	5.11
0.46	232	指数模型	10.80	9.10	72.60	87.50	0.99	0.35
0.23	116	指数模型	4.08	4.50	81.49	94.50	0.03	6.54
0.12	58	指数模型	1.10	4.30	61.15	93.00	0.01	76.60

下载: 导出CSV

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