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
复制标题
使用堆叠集成方法改进基于多个环境变量的土壤厚度估计
DOI：
10.3390/rs12213609
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发表时间：
2020-11-01
期刊：
影响因子：
5
通讯作者：
Niu, Yun
中科院分区：
文献类型：
--
作者：
Li, Xinchuan;Luo, Juhua;Niu, Yun
大尺度的空间连续土壤厚度数据通常不容易获得，而且获取起来往往很困难且昂贵。各种机器学习算法在数字土壤测绘中已变得非常流行，用于预测和绘制土壤特性的空间分布。识别土壤厚度的控制环境变量并选择合适的机器学习算法对于建模至关重要。本研究选取了11个定量和4个定性环境变量来探讨影响土壤厚度的主要变量。将四种常用的机器学习算法（多元线性回归（MLR）、支持向量回归（SVR）、随机森林（RF）和极限梯度提升（XGBoost））作为单独的模型进行评估，分别预测并获得中国河南省的土壤厚度分布图。此外，对使用最小绝对收缩和选择算子（LASSO）和广义提升回归模型（GBM）的两种堆积集成模型进行了测试并应用以构建最可靠和准确的估计模型。结果表明，变量选择地形湿度指数（TWI）、坡度、高程、土地利用和增强植被指数（EVI）是土壤厚度建模中最重要的环境变量。比较结果表明，XGBoost 模型优于 MLR、RF 和 SVR 模型，尤其是在使用 GBM 时，所提出的叠加方法解释了 64.0% 的土壤厚度变化。研究为绘制土壤厚度提供了有用的替代方法，并具有与其他土壤特性一起使用的潜力。
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.