基于XGBoost模型的湟水流域耕地土壤养分遥感反演

doi:10.12118/j.issn.1000-6060.2023.034

Abstract

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

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.

Figures/Tables 11

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References 34

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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

Remote sensing inversion of soil nutrient on farmland in Huangshui River Basin based on XGBoost model

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养分	建模因子
SOM	高程、土壤pH、地形起伏度、lg3、1/b4
TN	高程、坡向、剖面曲率、地形起伏度、土壤pH、lg1、1/b3
AP	坡向、平面曲率、地形起伏度、lg1、1/b2
AK	坡度、地形湿度指数、土壤pH、b7

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