数字农科院2.0

A Stacked-PLSR ensemble learning method for estimating heavy metals contents in farmland using spectral response features

文献类型： 外文期刊

作者： Chen, Xuying;Li, Long;Li, Xuqing;Ou, Huiping;Liu, Tianjiao;Gu, Huitao;Wang, Tingxuan

作者机构：

关键词： Hyperspectral imagery;farmland;soil heavy metals;band screening;Stacking

期刊名称： INTERNATIONAL JOURNAL OF REMOTE SENSING

ISSN： 0143-1161

年卷期： 2025 年

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Human health and food security are seriously threatened by heavy metal pollution of farming soil. Consequently, to timely and accurately determine the heavy metal concentration of agriculture soil, efficient monitoring techniques needs to be put in place. Traditional monitoring methods often prove inadequate in meeting the demands of large-scale and high-timeliness monitoring. This study proposes a Stacked-PLSR inversion model for the assessment of heavy metal content in agricultural soils. Using ZY-01-02D hyperspectral image data, the model inverts the Cu, As, and Ni contents in soil using eXtreme Gradient Boosting(XGBoost), Random Forest(RF), and Multilayer Perceptron(MLP) as the base model and Partial Least Squares Regression(PLSR) as the metal model. The study employs logarithmic and inverse re-first-order differential processing to enhance the spectral features and combines the Particle Swarm Optimization(PSO) algorithm with the Pearson correlation coefficient(PCC) for feature optimization. The experimental findings demonstrate that the Stacked-PLSR model outperforms the conventional machine learning model with respect to stability and prediction accuracy. The validation set R2 values for Cu, As, and Ni are 0.890, 0.724, and 0.844, respectively.Based on the model's inversion results, the heavy metals' spatial distribution map shows that the research area's agricultural soils are largely uncontaminated. However, there is a notable elevated concentration around the industrial area, indicating that the industrial area may serve as a potential source of pollution. This finding suggests that the industrial area may be a contributing factor to the observed levels of pollution.A new technical method for high-precision, large-scale remote sensing monitoring of heavy metal pollution in agricultural soils is proposed in this work. This approach is of significant importance in achieving precise pollution prevention and control, and ensuring food and ecological environment safety.

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