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自然资源遥感  2023, Vol. 35 Issue (4): 178-185    DOI: 10.6046/zrzyyg.2022294
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基于多源环境变量和随机森林模型的江西省耕地土壤pH值空间预测
钟骁勇1,2(), 李洪义3,4, 郭冬艳2, 谢模典3,4, 赵婉如3,4, 胡碧峰3,4()
1.江西财经大学财税与公共管理学院,南昌 330013
2.中国自然资源经济研究院,北京 101149
3.江西财经大学旅游与城市管理学院,南昌 330013
4.江西财经大学财经数据科学重点实验室,南昌 330013
Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model
ZHONG Xiaoyong1,2(), LI Hongyi3,4, GUO Dongyan2, XIE Modian3,4, ZHAO Wanru3,4, HU Bifeng3,4()
1. School of Finance and Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China
2. Chinese Academy of Natural Resources Economics, Beijing 101149, China
3. School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
4. Key Laboratory of Data Science in Finance and Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China
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摘要 

研究多源环境变量条件下随机森林(random forest,RF)模型和普通克里格(ordinary Kriging,OK)模型对大尺度耕地土壤pH值空间预测的性能差异,研究分析RF模型对提升土壤pH值预测精度的参考价值。以中国江西省为研究区,基于气候、地形和植被等环境协变量信息,结合土壤属性和耕地利用条件,利用RF模型对江西省耕地土壤pH值进行空间预测,识别土壤pH值空间变异的影响因素,并与OK模型计算精度进行对比。结果表明,增加土壤属性和耕地利用条件作为环境变量的RF-A模型预测耕地土壤pH值的精度优于以地形、气候、植被属性作为环境变量的RF-B模型和OK模型的预测结果,气候因素是决定土壤pH值的最重要因素,地形地貌因子和人为因素对pH值变异也有重要影响。研究结果表明该方法对提升大尺度耕地土壤pH值预测制图精度具有一定的理论和现实意义。
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钟骁勇
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谢模典
赵婉如
胡碧峰
关键词 ： 耕地,  土壤pH值,  随机森林,  江西省,  影响因素    
Abstract：

This study aims to compare the accuracy of random forest(RF) and ordinary kriging(OK) model for predicting spatial distribution of soil pH in arable land of Jiangxi Province using different covariates combination, and assess the feasibility and potential of RF method for improving the prediction accuracy of soil pH value. The RF algorithm is used to predict the pH value of cultivated soil in Jiangxi Province based on environmental covariate such as climate, topography and vegetation, combined with soil properties and cultivated land use conditions, identify the main influencing factors. The results produced by the RF was compared with the classical OK interpolation model. Our results showed that the accuracy of RF-A model with soil properties and cultivated land use conditions as environmental variables is better than that of RF-B model which only including terrain, climate and vegetation attributes as environmental variables. Climatic condition is the dominate factor which control the spatial variation of soil pH. the topographic factors and anthropogenic factors also have essential effect on spatial variability of soil pH. Thus, this study proved RF method has theoretical and practical significance for improving the accuracy of soil pH prediction at large-scale.
Key words： arable land    soil pH    random forest    Jiangxi Province    influencing factor
收稿日期: 2022-07-27      出版日期: 2023-12-21
ZTFLH:  TP79  
基金资助:国家自然科学基金项目“南疆滴灌棉田水盐四维时空变异性与管理风险评价研究”(42071068);江西省高校人文社会科学研究项目“江西省自然资源生态系统服务价值评估”(GL21217);江西省教育厅科技项目“近20年江西省耕地生态系统服务价值时空变化及驱动因子分析”(GJJ210541);江西省社会科学基金项目“江西省耕地资源资产价值核算及时空变异特征研究”(21YJ43D)
通讯作者: 胡碧峰(1992-),男,博士,副教授,主要从事土地生态与环境、资源环境遥感与信息技术、时空统计分析与建模研究。Email: hbfddmm297@163.com。
作者简介: 钟骁勇(1990-),男,博士,副研究员,主要从事自然资源权益管理和土地资源核算研究。Email: zhongxy0509@126.com。
引用本文:   
钟骁勇, 李洪义, 郭冬艳, 谢模典, 赵婉如, 胡碧峰. 基于多源环境变量和随机森林模型的江西省耕地土壤pH值空间预测[J]. 自然资源遥感, 2023, 35(4): 178-185.
ZHONG Xiaoyong, LI Hongyi, GUO Dongyan, XIE Modian, ZHAO Wanru, HU Bifeng. Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model. Remote Sensing for Natural Resources, 2023, 35(4): 178-185.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022294      或      https://www.gtzyyg.com/CN/Y2023/V35/I4/178
Fig.1  研究区土壤采样点分布
变量类别 具体指标 数据来源
地形变量 地貌类型、高程、坡度、坡向、剖面曲率、平面曲率、沟谷深度、地形湿度指数、垂直到沟谷距离和多分辨率谷底平坦度 中国科学院资源环境科学数据中心(http://www.resdc.cn/)
气候变量 年均气温、年降水量 中国科学院资源环境科学数据中心(http://www.resdc.cn/)
植被指数 归一化植被指数 中国科学院资源环境科学数据中心(http://www.resdc.cn/)
土壤属性 土壤类型、有机质、有效磷、速效钾、全氮、全磷、全钾、阳离子交换量、成土母质和耕层质地 2018年江西省耕地质量等别数据库、1980年和2010年江西省农业测土配方项目实测数据
耕地利用条件 灌溉保证率、排水条件、氮肥用量、磷肥P2O5用量、钾肥K2O用量、秸秆还田比例和秸秆还田量 2018年江西省耕地质量等别数据库
Tab.1  土壤pH值空间预测的环境变量及数据来源
组别 mtry ntree 训练集r 验证集r
1组 2 300 0.969 0.551
600 0.970 0.558
900 0.970 0.556
1 200 0.970 0.559
1 500 0.970 0.555
2组 4 300 0.970 0.567
600 0.970 0.574
900 0.970 0.573
1 200 0.971 0.573
1 500 0.971 0.572
3组 6 300 0.969 0.579
600 0.969 0.578
900 0.970 0.579
1 200 0.970 0.580
1 500 0.970 0.580
4组 8 300 0.969 0.582
600 0.970 0.582
900 0.970 0.583
1 200 0.970 0.582
1 500 0.969 0.581
Tab.2  RF模型中节点分裂次数和决策树数量的筛选
预测模型 抽样比 训练集 验证集
r ME MAE RMSE r ME MAE RMSE
RF-A 8∶2 0.970 -0.004 0.121 0.167 0.599 -0.009 0.291 0.392
7∶3 0.970 -0.004 0.121 0.167 0.567 -0.014 0.299 0.401
6∶4 0.970 -0.004 0.122 0.168 0.566 -0.018 0.302 0.406
RF-B 8∶2 0.966 -0.005 0.127 0.175 0.548 -0.023 0.307 0.413
7∶3 0.967 -0.005 0.127 0.175 0.525 -0.019 0.308 0.418
6∶4 0.967 -0.005 0.127 0.174 0.504 -0.014 0.314 0.425
OK 8∶2 0.679 0.002 0.261 0.364 0.559 0.009 0.311 0.409
7∶3 0.683 0.001 0.259 0.357 0.588 0.007 0.289 0.394
6∶4 0.652 0.002 0.278 0.379 0.541 0.010 0.328 0.414
Tab.3  使用不同训练集时RF和OK模型预测精度比较
Fig.2  RF-A模型变量相对重要性
Fig.3  基于RF-A,RF-B和OK模型的耕地pH值空间分布
Fig.4  RF-A模型预测值与实测值散点图
Fig.5  pH值空间变异的影响因素关系
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