基于XGBoost模型的湟水流域耕地土壤养分遥感反演

摘要/Abstract

图/表 11

参考文献 34

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doi:10.12118/j.issn.1000-6060.2023.034

摘要：

湟水流域是河湟谷地重要的组成部分，协同环境因素预测土壤养分空间分布对农业土壤养分管理尤为重要。土壤养分反演研究中对于参数对模型结果的影响和模型适用性的研究较少。选取研究区地形因子、土壤pH及光谱反射率共28个因子，结合贝叶斯优化算法构建人工神经网络（ANN）、支持向量机（SVM）和极端梯度提升（XGBoost）3种机器学习模型预测耕地土壤养分空间分布，计算决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）评价3种模型的精度。结果表明：（1）基于贝叶斯优化超参数的XGBoost模型对全氮（TN）含量预测精度优于其他模型（R²=0.893，RMSE=0.359，RPD=2.470），预测土壤有机质（SOM）、速效磷（AP）和速效钾（AK）含量时，XGBoost模型验证集R²分别为0.801、0.509、0.442。（2）对比3种模型的寻优次数和误差发现，BOA-XGBoost模型参数优化次数少、效率高，具有更好的鲁棒性。对于不同的养分，ANN和SVM模型预测精度存在差异，SVM模型预测SOM含量时精度更高（RPD=1.580），而ANN模型预测TN时精度最佳（RPD=2.460）。基于贝叶斯算法进行超参数优化构建的XGBoost模型预测精度高，可以达到良好的预测效果，可为湟水流域精准农业施肥提供参考。

关键词: 土壤养分, XGBoost, 空间分布, 环境因子, 湟水流域

Abstract:

The Huangshui River Basin is an important part of the Huangshui Valley. Additionally, collaborative environmental factors that predict the spatial distribution of soil nutrients are particularly important for managing soil nutrients. Moreover, less attention is paid to the effect of model parameters on the results obtained from soil nutrient inversion studies. In this study, the Huangshui River Basin in Qinghai Province (China) was selected as the study area, and 28 factors, including elevation, aspect, slope, plane curvature, section curvature, relief degree of land surface, topographic wetness index, soil pH, and spectral reflectance, were selected. In addition, these factors were combined with the Bayesian optimization algorithm (BOA) to construct artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGBoost) machine learning models for predicting the spatial distribution of four soil nutrients in farmlands: soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK), respectively. Further, the prediction accuracy of these three models was evaluated based on the model coefficient of determination (R²), root-mean-square error (RMSE), and relative percent deviation (RPD). The results revealed that: (1) All four soil nutrients exhibited a moderate degree of variability, with TN showing the highest variability of 69.481%. The XGBoost model based on the Bayesian optimized hyperparameter combination was better than other models in predicting the TN content (R², RMSE, and RPD were 0.893, 0.359, and 2.470, respectively). The R² values of the XGBoost model validation set for estimating the SOM, AK, and AP contents were 0.801, 0.509, and 0.442, respectively, and the corresponding RPD values were 2.152, 1.210, and 1.274, respectively. Moreover, this model exhibited a better prediction capability. (2) The comparison of the number of optimizations and errors of the three models revealed that the BOA-XGBoost model exhibited minimum number of parameter optimizations, higher efficiency, and better robustness. The ANN and SVM models demonstrated different prediction accuracies for different nutrients; additionally, the SVM model predicted the SOM content with high accuracy (RPD=1.580), while the ANN model predicted TN efficiently (RPD=2.460). Based on Landsat 8 remote sensing images, the XGBoost inversion model developed by combining 28 factors of the Huangshui River Basin was found to be more suitable for application in soil nutrient inversion research; furthermore, it can more accurately describe the spatial distribution pattern of the soil nutrient inversion in the Huangshui River Basin, better ensure precise agriculture fertilization, improve the fertilizer utilization rate and crop yield, and provide a reference for precise agriculture fertilization in the Huangshui River Basin.

Key words: soil nutrient, XGBoost, spatial distribution, environmental factor, Huangshui River Basin

刘尊方, 雷浩川, 盛海彦. 基于XGBoost模型的湟水流域耕地土壤养分遥感反演[J]. 干旱区地理, 2023, 46(10): 1643-1653.

LIU Zunfang, LEI Haochuan, SHENG Haiyan. Remote sensing inversion of soil nutrient on farmland in Huangshui River Basin based on XGBoost model[J]. Arid Land Geography, 2023, 46(10): 1643-1653.

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土壤养分	样点数/个	最小值/g·kg^-1	最大值/g·kg^-1	平均值/g·kg^-1	标准差/g·kg^-1	变异系数/%
SOM	107	4.310	59.789	28.377	11.168	39.356
TN	107	0.372	5.874	1.232	0.856	69.481
AP	107	0.001	0.162	0.055	0.032	58.182
AK	107	0.048	0.488	0.204	0.087	42.647

土壤养分	高程	坡向	坡度	平面曲率	剖面曲率	地形湿度指数	地形起伏度	pH
SOM	0.422^**	0.223^*	-0.022	0.101	0.052^*	0.010^*	0.095^*	-0.338^**
TN	0.595^**	0.238^*	-0.174^*	-0.015	0.089^*	0.052^*	0.276^**	-0.485^**
AP	-0.048	-0.066^*	-0.028	0.080^*	-0.036	-0.048	-0.098^*	0.026
AK	0.052	0.041	-0.080^*	0.029	0.015	-0.015	-0.013	-0.106^*

波段	SOM	TN	AP	AK	波段	SOM	TN	AP	AK
b1	-0.390^**	-0.343^**	0.118^*	-0.039	lg5	-0.283^**	-0.158	0.088	-0.034
b2	-0.391^**	-0.334^**	0.108^*	-0.040	lg6	-0.259^**	-0.185	0.082	-0.125^*
b3	-0.393^**	-0.311^**	0.083	-0.070	lg7	-0.251^**	-0.247^*	0.069	-0.169^*
b4	-0.379^**	-0.280^**	0.085	-0.084^*	1/b1	0.278^**	0.424^**	-0.134	0.046
b5	-0.267^**	-0.084	0.068	-0.041	1/b2	0.334^**	0.485^**	-0.151^*	0.034
b6	-0.194^*	-0.018	0.033	-0.120	1/b3	0.376^**	0.491^**	-0.142^*	0.044
b7	-0.174^*	-0.073	0.008	-0.180^*	1/b4	0.385^**	0.489^**	-0.143^*	0.063
lg1	-0.387^**	-0.495^**	0.150^*	-0.024	1/b5	0.300^**	0.247^*	-0.112	0.029
lg2	-0.392^**	-0.452^**	0.139^*	-0.019	1/b6	0.317^**	0.361^**	-0.128	0.119^*
lg3	-0.394^**	-0.407^**	0.113	-0.051	1/b7	0.316^**	0.422^**	-0.126	0.139^*
lg4	-0.387^**	-0.386^**	0.114	-0.071

模型	参数	土壤养分
模型	参数	SOM	TN	AP	AK
ANN	隐藏层节点/个	80	100	120	120
	最大迭代次数/次	1000	1500	500	500
	激活函数	S型函数	S型函数	修正线性单元函数	修正线性单元函数
SVM	惩罚系数	3.12	2.50	0.35	1.10
SVM	核参数	0.42	0.73	0.91	0.92
XGBoost	树的最大深度	4	5	4	4
	最大树数目	50	30	75	96
	学习率	0.03	0.07	0.05	0.04
	最小叶节点样本权重和	4	4	2	1

土壤养分	模型	建模集		验证集
土壤养分	模型	R²	RMSE	R²	RMSE	RPD
SOM	ANN	0.753	5.363	0.643	7.401	1.214
	SVM	0.822	5.122	0.632	5.648	1.580
	XGBoost	0.910	3.791	0.801	4.321	2.152
TN	ANN	0.885	0.245	0.803	0.306	2.460
	SVM	0.871	0.415	0.735	0.252	1.886
	XGBoost	0.958	0.235	0.893	0.359	2.470
AP	ANN	0.491	0.040	0.382	0.030	1.002
	SVM	0.468	0.023	0.441	0.029	1.213
	XGBoost	0.692	0.022	0.509	0.026	1.210
AK	ANN	0.514	0.063	0.419	0.064	1.321
	SVM	0.486	0.061	0.354	0.072	1.260
	XGBoost	0.692	0.043	0.442	0.055	1.274