短距离样点对土壤呼吸空间变异预测精度的影响

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谢梦姣^1,,
陈奇乐²,
张俊梅²,
康营²,
吴超玉²,
刘琦¹,
王洋^1,,
1.河北农业大学国土资源学院保定 071000
2.河北农业大学资源与环境科学学院保定 071000
基金项目: "十三五"国家重点研发计划"粮食丰产增效科技创新"项目2018YFD0300504

详细信息

作者简介:谢梦姣, 主要从事土地资源利用与环境效应研究。E-mail:xiemengjiao94@163.com

通讯作者:王洋, 主要从事土地利用变化与资源环境效应研究。E-mail:xiaoyiranwy85@163.com

中图分类号:S159.3

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出版历程

收稿日期:2019-09-27
录用日期:2019-11-07
刊出日期:2020-03-01

Effects of short distance sampling on the prediction accuracy of the spatial variability of soil respiration

XIE Mengjiao^1,,
CHEN Qile²,
ZHANG Junmei²,
KANG Ying²,
WU Chaoyu²,
LIU Qi¹,
WANG Yang^1,,
1. College of Land and Resources, Hebei Agricultural University, Baoding 071000, China
2. College of Resources and Environment, Hebei Agricultural University, Baoding 071000, China
Funds: the National Key Research and Development Project of China2018YFD0300504

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Corresponding author:WANG Yang, E-mail: xiaoyiranwy85@163.com

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摘要
摘要:不同采样设计会对土壤呼吸空间变异特征的预测精度产生重要影响。本研究选取黄淮海平原北部潮土区1 km×1 km夏玉米样地，在7×7单元规则格网（样点间距167 m）、完全随机（样点平均间距433 m）以及3×3单元规则格网+完全随机（样点平均间距405 m）3种布点方式的基础上，保持样本总量（49）不变，以占总样点2%~14%的短距离样点（样点间距4 m）随机替换原方案相应样点个数的方法优化布点方式，应用普通克里金法插值，以均方根误差（RMSE）和确定系数（R²）作为验证指标，检验基于3种布点方式设置的短距离样点对土壤呼吸空间变异预测精度的影响。结果表明：研究区土壤呼吸平均速率为2.65 μmol·m^-2·s^-1，空间分布均呈西高东低，表现出中等程度变异。采样设计对土壤呼吸空间分布的预测精度影响显著，基于3种布点方式设置短距离样点可提高预测精度7%~13%。无短距离样点替换时，规则格网+完全随机的布点方式最优，比完全随机布点和规则格网布点的空间插值预测精度分别提高10%和22%；设置短距离样点替换后，在最优布点方式（规则格网+完全随机）中，对土壤呼吸空间变异的预测精度可再提高4%~7%，其中短距离样点个数占样本总量10%对土壤呼吸空间变异预测精度的提高最为明显。研究发现，基于相同的样本数量设置短距离样点可增加区域范围内样点密度，提高土壤呼吸空间变异预测精度及试验结果的可靠性。因此，在黄淮海平原北部潮土区100 hm²尺度的夏玉米样地中，规则格网+完全随机+10%短距离样点的布点方式是预测土壤呼吸空间变异最适宜的采样布点方式。
关键词:土壤呼吸/
空间变异/
采样设计/
预测精度/
短距离样点/
普通克里金
Abstract:Sampling design is important for the prediction accuracy of the spatial variability of soil respiration. In this study, a plot of 1 km×1 km was selected in a summer maize field from the northern part of the Huang-Huai-Hai Plain. Each of the forty-nine sampling sites were set on the basis of three different sampling designs, including a regular grid of 7×7 unit rule (with a spacing of 167 m), completely random (with an average spacing of 433 m), and a regular grid of 3×3 unit rule combined with completely random (with an average spacing of 405 m). To optimize the layout, based on the 3 designs, we maintained the total number of samples (49) and replaced the original sampling with short-distance sampling points for 2% to 14% of the total number of samples (with a spacing of 4 m). The spatial interpolation was finished with the ordinary Kriging interpolation method. The root mean square error (RMSE) and determination coefficient (R²) were chosen as indicators to investigate the effects of short distance sampling on the prediction accuracy of the spatial variability of soil respiration. The results showed that the spatial distribution of soil respiration under the three sampling designs was high in the west and low in the east, with moderate variation. Different sampling designs had significant impacts on the prediction accuracy of the spatial variability of soil respiration. The short distance sampling under the three sampling designs increased the prediction accuracy of the spatial variability of soil respiration by 7%-13%. Without short distance samples, the sampling design of the regular grid combined with completely random had the highest prediction accuracy, which was 10% and 22% higher than the regular grid and completely random sampling designs, respectively. Upon the replacement with short distance sampling, the prediction accuracy of the optimal sampling design (regular grid combined with completely random) was increased by 4%-7%. The prediction accuracy of the spatial variability of soil respiration was most obviously improved when the proportion of short distance samples was 10% of the whole size. This study found that setting short distance samples based on the same sample size could increase the sample density within a region and improve the prediction accuracy of soil respiration spatial variation and the reliability of experimental results. Therefore, a completely random sampling design combined with a regular grid and 10% short distance samples is a better choice for the soil respiration spatial variation estimation of a 1 km×1 km plot in a summer maize field from the northern part of the Huang-Huai-Hai Plain. The results of this study provide guidance for relevant research and field sampling designs.
Key words:Soil respiration/
Spatial variation/
Sampling design/
Prediction accuracy/
Short distance sample/
Ordinary Kriging

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图1研究区域不同样点布设方案的样点分布图
Figure1.Samples distribution of different sampling methods in the study area

下载: 全尺寸图片幻灯片

图2不同样点布设方案下基于普通克里金插值的土壤呼吸空间分布特征(上:无短距离样点; 下: 10%短距离样点)
Figure2.Spatial distribution of soil respiration rate based on Ordinary Kriging interpolation under different sampling methods (top: no short distance samples; bottom: with 10% short distance samples)

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图3不同样点布设方案下土壤呼吸速率预测相关系数和均方根预测误差(RMSE)随短距离样点占比增加的变化
Figure3.Variation of estimation correlation coefficient and root mean square prediction error (RMSE) of soil respiration rate with the proportion of short distance samples under different sampling methods

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表1不同样点设计方案的样点布设方法
Table1.Sample layout of different sampling methods

布点方案 Sampling method	基础布点方法 Basic sampling method	短距离样点数 Short distance samples number¹⁾
a	规则格网点(49个样点) Regular grid (7×7 unit with plots spacing of 167 m)	样点总数的0~14%, 0~7个逐个增加 0-7 (0-14% of total samples) short distance samples
b	完全随机点(49个样点) Completely random (49 samples, average samples spacing 433 m)	样点总数的0~14%, 0~7个逐个增加 0-7 (0-14% of total samples) short distance samples
c	规则格网点(9个样点)+完全随机点(40个) Regular grid (9 samples) and completely random (40 samples)	样点总数的0~14%, 0~7个逐个增加 0-7 (0-14% of total samples) short distance samples
1)在增加短距离样点数的同时, 相应随机减少常规布设的样点数。1) The total number of every sampling method is kept the same at 49 samples, including basic samples and short distance samples, i.e. with the increase of short distance samples number, the basic samples number decreased.

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表2不同样点布设方案下土壤呼吸速率的描述性统计结果
Table2.Descriptive statistical results of soil respiration rate of different sampling methods

布点设计Sampling method		最小值 Min (μmol?m^-2?s^-1)	最大值 Max (μmol?m^-2?s^-1)	平均值 Average (μmol?m^-2?s^-1)	标准差 Standard deviation (μmol?m^-2?s^-1)	偏度 Skewness	峰度 Kurtosis	变异系数 Coefficient of variation (%)	K-S检验(P值) K-S test (P value)
基础布点方法 Basic method	短距离样点比 Ratio of short distance samples (%)	最小值 Min (μmol?m^-2?s^-1)	最大值 Max (μmol?m^-2?s^-1)	平均值 Average (μmol?m^-2?s^-1)	标准差 Standard deviation (μmol?m^-2?s^-1)	偏度 Skewness	峰度 Kurtosis	变异系数 Coefficient of variation (%)	K-S检验(P值) K-S test (P value)
全部采样点All sampling		1.16	4.88	2.65	0.72	0.58	0.41	27	0.84
规则格网 Regular grid	0	1.58	4.88	2.89	0.76	0.72	–0.21	26	0.92
规则格网 Regular grid	10	1.58	4.88	2.86	0.74	0.71	–0.18	26	0.91
完全随机 Completely random	0	1.16	4.88	2.49	0.65	0.38	0.50	26	0.91
完全随机 Completely random	10	1.16	4.88	2.51	0.64	0.35	0.55	26	0.91
规则格网+完全随机 Regular grid + completely random	0	1.16	4.28	2.55	0.67	0.26	0.18	26	0.86
规则格网+完全随机 Regular grid + completely random	10	1.16	4.28	2.61	0.67	0.28	0.15	26	0.86

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表3不同样点布设方案下土壤呼吸空间半变差函数模型及参数
Table3.Semi-variation function models and parameters of soil respiration under different sampling methods

基础布点方法 Basic sampling method	模型 Model	模型参数 Model parameter	短距离样本点占比Ratio of short distance samples (%)
基础布点方法 Basic sampling method	模型 Model	模型参数 Model parameter	0	2	4	6	8	10	12	14
规则格网 Regular grid	球状 Spherical	C₀/(C₀+C) (%)	48	50	54	60	63	70	69	71
		R²	0.58	0.6	0.6	0.6	0.63	0.68	0.67	0.68
		C₀	0.11	0.08	0.07	0.07	0.08	0.06	0.06	0.06
		变程Range (m)	296	325	312	350	314	307	307	301
完全随机 Completely random	球状 Spherical	C₀/(C₀+C) (%)	42	49	55	57	62	68	70	70
		R²	0.47	0.56	0.57	0.59	0.61	0.62	0.65	0.65
		C₀	0.16	0.12	0.1	0.1	0.1	0.09	0.08	0.08
		变程Range (m)	325	306	249	247	296	315	317	306
规则格网+完全随机 Regular grid + completely random	球状 Spherical	C₀/(C₀+C) (%)	55	53	52	56	65	70	71	72
		R²	0.68	0.65	0.64	0.69	0.74	0.78	0.8	0.8
		C₀	0.05	0.03	0.03	0.05	0.05	0.05	0.04	0.03
		变程Range (m)	305	289	270	287	295	295	312	308

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表4研究区土壤呼吸与土壤温度、土壤水分的 Pearson相关性分析
Table4.Correlation among soil respiration, soil temperature and soil moisture

	土壤呼吸 Soil respiration	土壤水分 Soil moisture	土壤温度Soil temperature
	土壤呼吸 Soil respiration	土壤水分 Soil moisture	5 cm	10 cm
土壤呼吸 Soil respiration	1.000	0.179	0.020	0.023
土壤水分 Soil moisture		1.000	0.044	–0.005

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