Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

	Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods
	Li, Xinchuan; Luo, Juhua; Jin, Xiuliang; He, Qiaoning; Niu, Yun
刊名	REMOTE SENSING
	2020
卷号	12 期号:21
英文摘要	Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.
内容类型	期刊论文
源URL	[http://159.226.73.51/handle/332005/20080]
专题	中国科学院南京地理与湖泊研究所
推荐引用方式 GB/T 7714	Li, Xinchuan,Luo, Juhua,Jin, Xiuliang,et al. Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods[J]. REMOTE SENSING,2020,12(21).
APA	Li, Xinchuan,Luo, Juhua,Jin, Xiuliang,He, Qiaoning,&Niu, Yun.(2020).Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods.REMOTE SENSING,12(21).
MLA	Li, Xinchuan,et al."Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods".REMOTE SENSING 12.21(2020).