基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例

doi:10.3864/j.issn.0578-1752.2020.18.008

中国农业科学 ›› 2020, Vol. 53 ›› Issue (18): 3716-3728.doi: 10.3864/j.issn.0578-1752.2020.18.008

• 土壤肥料·节水灌溉·农业生态环境 • 上一篇下一篇

基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例

申哲¹(),张认连¹,龙怀玉¹,王转¹,朱国龙¹,石乾雄²,喻科凡¹,徐爱国¹()

¹中国农业科学院农业资源与农业区划研究所,北京 100081
²北京大学地球与空间科学学院,北京100871

收稿日期:2019-12-10 接受日期:2020-03-17 出版日期:2020-09-16 发布日期:2020-09-25
通讯作者: 徐爱国
作者简介:申哲,Tel：16619922154;E-mail： 18211097094@163.com。
基金资助:
国家重点研发计划(2017YFD0200607);科技基础性工作专项(2012FY112100);中国农业科学院基本科研业务费专项(Y2020PT37);中国农业科学院基本科研业务费专项(1610132019043)

Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example

SHEN Zhe¹(),ZHANG RenLian¹,LONG HuaiYu¹,WANG Zhuan¹,ZHU GuoLong¹,SHI QianXiong²,YU KeFan¹,XU AiGuo¹()

¹Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081
²School of Earth and Space Sciences, Peking University, Beijing 100871

Received:2019-12-10 Accepted:2020-03-17 Online:2020-09-16 Published:2020-09-25
Contact: AiGuo XU

摘要/Abstract

摘要：

【目的】探索适合地形复杂的黄土母质地区土壤颗粒组成的空间预测方法。【方法】以宁夏自治区海原县为研究区域,结合地形因子、土壤类型、归一化植被指数变量,采用基于对称对数比转换的经验贝叶斯克里格法（SLR-EBK）、回归克里格法（SLR-RK）、随机森林（SLR-RF）3种方法对训练集100个样点表层土壤颗粒组成的空间分布进行预测,并通过验证集24个样点比较了3种方法的预测精度。【结果】（1）最终进入土壤颗粒组成线性回归预测模型的辅助变量包括高程（Ele）和土壤类型;进入RF模型的辅助变量包括高程（Ele）、土壤类型、坡度（Slo）和风力作用指数（WEI）,其中,高程（Ele）是最重要的辅助变量,其次是土壤类型,坡度（Slo）和风力作用指数（WEI）重要性相对较低。（2）3种方法预测的海原县土壤各粒级含量空间分布的趋势基本一致,表现为砂粒含量西南部低,东北部高,粉粒、黏粒则相反。与SLR-EBK相比,SLR-RK和SLR-RF能够更好地反映局部变异并减小平滑效应。（3）SLR-RF法对验证集3个粒级含量预测的平均绝对误差（MAE）和均方根误差（RMSE）均低于其他两种方法,且从平均Aitchison距离（MAD）来看,SLR-RF（0.208）

关键词: 土壤颗粒组成, 空间预测, 经验贝叶斯克里格法, 回归克里格法, 随机森林, 对称对数比转换

Abstract:

【Objective】 This study was oriented to explore for a model that was capable of predicting spatial distribution of soil particle size distribution in the region with complicated terrain and single parent material, like Haiyuan County, Ningxia. 【Method】 Empirical Bayesian Kriging with the symmetry logratio transform (SLR-EBK), regression Kriging with the symmetry logratio transform (SLR-RK) and random forest with the symmetry logratio transform (SLR-RF) were used to predict spatial distribution of soil particle size distribution in Haiyuan County based on the training set of 100 samples, combined with topographic factors, normalized difference vegetation index and soil types, and then the predictions were validated with a validating set of 24 validation points in the study area for comparison of prediction accuracy. 【Result】 (1) The auxiliary variables that came into the linear regression equator for prediction included elevation (Ele) and soil types. The auxiliary variables that came into the RF model included Ele, soil type, slope (Slo) and wind exposition index (WEI), of which Ele was the most important auxiliary variable, followed by soil types. Slo and wind exposition index (WEI) were less important. (2) Results predicted by three methods showed a spatial distribution trend that the sand content was lower in the southwest and higher in the northeast of the county, while the silt content and clay content were higher in the southwest and lower in the northeast. SLR-RK and SLR-RF could better describe local variation of different soil particle size contents. (3) The mean absolute error (MAE) and root-mean-square error (RMSE) of SLR-RF were lower than those of the other two methods. As for mean Aitchison distance (MAD), the sequences of MAD were obtained as following: SLR-RF (0.208)

Key words: soil particle size distribution, spatial prediction, empirical Bayesian Kriging, regression Kriging, random forest, symmetric log-ratio transform

申哲,张认连,龙怀玉,王转,朱国龙,石乾雄,喻科凡,徐爱国. 基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例[J]. 中国农业科学, 2020, 53(18): 3716-3728.

SHEN Zhe,ZHANG RenLian,LONG HuaiYu,WANG Zhuan,ZHU GuoLong,SHI QianXiong,YU KeFan,XU AiGuo. Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example[J]. Scientia Agricultura Sinica, 2020, 53(18): 3716-3728.

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土壤类型 Soil type	采样个数 Sampling point	本县面积占比 Proportion of area (%)
黄绵土 Cultivated loessial soil	65	53.33
黑垆土 Dark loessial soil	21	15.31
灰钙土 Sierozem	19	14.87
灰褐土 Gray cinnamonic soil	9	6.87
新积土 Alluvial soil	5	4.71
粗骨土 Skeletal soil	3	3.75
红黏土 Red clay	2	1.16

辅助变量 Auxiliary variable	粒级 Soil particle
辅助变量 Auxiliary variable	砂粒 Slr-sand	粉粒 Slr-silt	黏粒 Slr-clay
高程Ele	-0.387^**	0.341^**	0.239^**
坡度Slo	0.044	0.061	-0.111
平面曲率 HC	-0.129	0.043	0.107
剖面曲率 PC	-0.018	0.019	-0.011
地形湿度指数 TWI	0.130	-0.132	0.065
风力作用指数 WEI	0.129	-0.045	-0.111
归一化植被指数 NDVI	-0.296^**	0.235^**	0.204^**
土壤类型 ST1	0.576^**	--	--
土壤类型 ST2	--	0.435^**	--
土壤类型 ST3	--	--	0.466^**

样本组 Sample group	粒级 Soil particle	样本数 Sample size	最大值 Max (%)	最小值 Min (%)	均值 Mean (%)	标准差 Std. deviation (%)	变异系数 CV (%)	偏度 Skewness
训练集 Calibration sample	砂粒Sand	100	79.18	43.15	61.82	8.67	14.02	-0.04
	粉粒 Silt	100	40.40	13.21	23.62	5.66	23.96	0.16
	黏粒Clay	100	23.07	7.59	14.56	4.00	27.47	0.24
验证集 Validation sample	砂粒Sand	24	77.49	48.68	63.96	7.23	11.30	-0.34
	粉粒 Silt	24	34.06	13.76	22.68	4.97	21.91	0.48
	黏粒Clay	24	22.61	8.10	13.36	3.40	25.45	0.72

粒级Soil particle	方程Equator	调整决定系数Adjusted R²	概率Probability
砂粒 Slr-sand	slr-sand=0.160-0.140Ele+0.875ST1	0.339	<0.001
粉粒 Slr-silt	slr-silt=-0.070+0.097Ele+0.811ST2	0.208	<0.001
黏粒 Slr-clay	slr-clay=-0.054+0.042Ele+0.945ST3	0.205	<0.001

粒级Soil particle	辅助变量Auxiliary variable	Ntree	Mtry
砂粒 Slr-sand	Ele、ST1、Slo、WEI	300	2
粉粒 Slr-silt	Ele、ST2、Slo、WEI	300	2
黏粒 Slr-clay	Ele、ST3、Slo、WEI	300	2

基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例

Research on Spatial Distribution of Soil Particle Size Distribution in Loess Region Based on Three Spatial Prediction Methods—Taking Haiyuan County in Ningxia as an Example

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方法 Method	砂粒 Sand		粉粒 Silt		黏粒 Clay		MAD
方法 Method	MAE (%)	RMSE (%)	MAE (%)	RMSE (%)	MAE (%)	RMSE (%)	MAD
SLR-EBK	5.706	6.741	3.515	4.308	3.113	4.069	0.274
SLR-RK	4.404	5.447	2.756	3.408	2.839	3.677	0.235
SLR-RF	3.809	4.868	2.623	3.317	2.189	2.914	0.208

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