杨其坡^{1, 3,},
武伟^{2, 3},
刘洪斌^{1, 3,,}
1.西南大学资源环境学院 重庆 400716
2.西南大学计算机与信息科学学院 重庆 400715
3.重庆市数字农业重点实验室 重庆 400716
基金项目: 中央高校基本科研业务费专项XDJK2016D041

详细信息

作者简介:杨其坡, 主要从事土壤资源环境遥感与信息技术的研究。E-mail:Qipoyang@163.com

通讯作者:刘洪斌, 主要从事土壤调查与信息管理方面的研究。E-mail:lhbin@swu.edu.cn

中图分类号:S158.9

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出版历程

收稿日期:2017-05-17
录用日期:2017-12-05
刊出日期:2018-03-01

Prediction of spatial distribution of soil available iron in a typical hilly farm-land using terrain attributes and random forest model

YANG Qipo^{1, 3,},
WU Wei^{2, 3},
LIU Hongbin^{1, 3,,}
1. College of Resources and Environment, Southwest University, Chongqing 400716, China
2. College of Computer and Information Science, Southwest University, Chongqing 400715, China
3. Chongqing Key Laboratory of Digital Agriculture, Chongqing 400716, China
Funds: the Fundamental Research Funds for the Central Universities of ChinaXDJK2016D041

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Corresponding author:LIU Hongbin, E-mail:lhbin@swu.edu.cn

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摘要
摘要:为了掌握丘陵地区农田土壤有效铁含量及其空间分布，本文以重庆市江津区永兴镇内同源成土母质的典型丘陵（2 km²）为研究区，采集309个土壤样点，利用普通克里格（Ordinary Kriging，OK）、多元线性回归（Multiple Linear Regression，MLR）、随机森林（Random Forest，RF）模型，结合高程、坡度、坡向、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数等地形因子对土壤有效铁进行空间分布预测，并通过85个验证点评价、筛选预测模型。结果表明：1）土壤有效铁与谷深、地形湿度指数存在极显著水平正相关关系，与坡度、平面曲率、剖面曲率、汇聚指数、相对坡位指数存在极显著水平负相关关系。2）随机森林模型的预测精度明显高于多元线性回归和普通克里格插值，其平均绝对误差为22.33 mg·kg^-1、均方根误差为27.98 mg·kg^-1、决定系数为0.76，是研究区土壤有效铁含量空间分布的最适预测模型。3）地形湿度指数和坡度是影响该区域土壤有效铁含量空间分布的主要地形因子。土壤有效铁与坡度、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数均达到极显著水平相关关系。4）研究区土壤有效铁含量范围为3.00~276.97 mg·kg^-1，水田有效铁含量大于旱地；土壤有效铁具有较强的空间相关性，土壤有效铁含量空间变异主要受到结构性因素的影响。可见，基于地形因子的随机森林预测模型可以较好地解释丘陵区农田土壤有效铁含量的空间变异，研究结果为丘陵区土壤中、微量元素含量及空间分布预测提供方法借鉴和理论依据。
关键词:地形因子/
随机森林模型/
土壤有效铁/
空间分布/
丘陵区农田
Abstract:Soil available iron is essential for plant growth. Detailed information on the spatial distribution of soil available iron is critical for effective management of soil fertility. To date, published works on soil available iron have mainly focused on the spatial variability and little has been done on predicting the spatial distribution of soil available iron. To understand the spatial distribution of soil available iron in hilly areas of Southwest China, we conducted a study in 2014 at a 2-km² typical hilly region with uniform soil parent materials in Yongxing Town, Jiangjin County, Chongqing City. A total of 309 soil samples were collected from cultivated lands at the depth of 0-20 cm. The samples were randomly divided into calibration (224) and validation (85) samples. Nine terrain attributes (including elevation, slope, aspect, valley depth, horizontal curvature, profile curvature, convergence index, relation position index and topographic wetness index) were extracted from a digital elevation model of spatial resolution of 2.0 m. Ordinary Kriging (OK), Multiple Linear Regression (MLR) and Random Forest (RF) analyses were used to predict the content of soil available iron based on the terrain attributes. Accuracy indicators, including mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²), were used to evaluate model performance based on validation data. Correlation analysis showed that topographic wetness index and valley depth were significantly positively correlated with soil available iron content. Slope, horizontal curvature, profile curvature, convergence index and relative position index were on the other hand significantly negatively correlated with soil available iron content. Compared with OK and MLR, RF model performed best, with MAE=22.33 mg·kg^-1, RMSE=27.98 mg·kg^-1 and R²=0.76. Additionally, RF analysis indicated that topographic wetness and slope were the main factors controlling the spatial distribution of soil available iron. Soil available iron content in the study area was 3.00-276.97 mg·kg^-1, which was higher for paddy field than for dryland. Semivariance model showed strong spatial autocorrelation of soil available iron, indicating that structural factors were the main driving force of spatial variation of soil available iron. Therefore it was concluded that the RF model together with terrain attributes well explained the spatial variability of soil available iron in the area. The result of the study provided valuable information for studies on predicting the spatial distribution of trace elements in soils in hilly areas.
Key words:Terrain attribute/
Random forest model/
Soil available iron/
Spatial distribution/
Hilly farmland

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图1研究区DEM、土地利用及样点分布图
Figure1.Map of DEM, land use and samples sites of the study area


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图2研究区土壤有效铁含量的随机森林自变量影响力评价
图中IncMSE为均方误差增加量; Slo为坡度; Twi为地形湿度指数; Hc为平面曲率; Rpi为相对坡位指数; Pc为剖面曲率; Ci为汇聚指数; Ele为高程; Vd为谷深; Asp为坡向。
Figure2.Influence evaluation of independent variables by Random Forest of soil available iron content in the study area
In the graph, IncMSE is increase in mean squared error; Slo is the slope; Twi is the topographic wetness index; Hc is the horizontal curvature; Rpi is the relation position index; Pc is the profile curvature; Ci is the Convergence index; Ele is the elevation; Vd is the valley depth; Asp is the aspect.


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图3研究区土壤有效铁含量空间分布预测图
Figure3.Prediction map of soil available iron content in the study area


下载: 全尺寸图片幻灯片

表1研究区采样点土壤有效铁含量描述性统计
Table1.Descriptive statistics of soil available iron content of the study area

样本组 Sample group	样本数 Number of samples	最小值 Minimum (mg·kg-1)	最大值 Maximum (mg·kg-1)	中值 Median (mg·kg-1)	平均值 Mean (mg·kg-1)	标准差 Std. deviation (mg·kg-1)	变异系数 Coefficient of variation	偏度 Skewness
训练集?Calibration samples	224	3.00	276.97	27.36	49.94	57.35	1.15	1.80
验证集?Validation samples	85	4.20	208.38	26.44	49.55	53.85	1.09	1.49

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表2研究区土壤有效铁的半方差模型参数
Table2.Semivariance parameters of soil available iron in the study area

拟合模型 Model	块金值 Nugget (C0)	基台值 Sill (C0+C)	块基比 Proportion [C0/(C0+C)] (%)	变程 Range (m)	决定系数R2
指数模型 Exponential	560	3 858	14.5	561	0.898

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表3研究区土壤有效铁含量与地形因子的相关性
Table3.Correlations between soil available iron content and terrain attributes in the study area

	高程 Elevation	坡度 Slope	坡向 Aspect	谷深 Valley depth	平面曲率 Horizontal curvature	剖面曲率 Profile curvature	汇聚指数 Convergence index	相对坡位指数 Relation position index	地形湿度指数 Topographic wetness index
土壤有效铁 Soil available iron	-0.116	-0.371**	0.086	0.298**	-0.327**	-0.228**	-0.174**	-0.428**	0.592**
??表中数据为训练集统计分析结果。*和**表明地形因子与土壤有效铁含量的相关性达显著(P < 0.05)和极显著(P < 0.01)水平。Data in the table are the results calculated based on the data of calibration samples. * and ** stand for that the relationships between terrain attributes and soil available iron content are significant at P < 0.05 and P < 0.01, respectively.

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表4研究区旱地和水田土壤有效铁含量的比较
Table4.Comparison of soil available iron content between dryland and paddy field

土地利用类型 Land use type	样本数 Number of samples	最小值 Minimum (mg·kg-1)	最大值 Maximum (mg·kg-1)	中值 Median (mg·kg-1)	平均值 Mean (mg·kg-1)	标准差 Std. deviation (mg·kg-1)	变异系数 Coefficient of variation
旱地?Dry land	190	3.00	156.60	21.24	31.85	30.37	0.95
水田?Paddy field	34	3.85	276.97	155.22	151.04	67.27	0.45
??表中的数据为训练集统计分析结果。Data in the table are the results calculated based on the data of training of set.

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表5研究区土壤有效铁含量模型预测精度
Table5.Prediction accuracies of fitted models of soil available iron content in the study area

预测模型 Prediction model	验证集?Validation dataset
	MAE (mg·kg-1)	RMSE (mg·kg-1)	R2
普通克里格?Ordinary Kriging	32.97	52.38	0.11
多元线性回归 Multiple linear regression,	23.58	35.25	0.60
随机森林?Random forest	22.33	27.98	0.76

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