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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 178-185     DOI: 10.6046/zrzyyg.2022294
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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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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.
Keywords arable land      soil pH      random forest      Jiangxi Province      influencing factor     
ZTFLH:  TP79  
Issue Date: 21 December 2023
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Xiaoyong ZHONG
Hongyi LI
Dongyan GUO
Modian XIE
Wanru ZHAO
Bifeng HU
Cite this article:   
Xiaoyong ZHONG,Hongyi LI,Dongyan GUO, et al. Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model[J]. Remote Sensing for Natural Resources, 2023, 35(4): 178-185.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022294     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/178
Fig.1  Sampling locations in study area
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  Data sources of environmental covariates used for predicting pH
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  Optimization of critical parameters for RF model
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  Comparison of model performance with different training and validation dataset ratios
Fig.2  Relative importance of covariates for RF-A model
Fig.3  Distribution of soil pH produced by RF-A, RF-B and OK model
Fig.4  Scatter plot of soil pH predicted and measured by RF-A model
Fig.5  Relevance of pH and influencing factors
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