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Stacking ensemble learning coupled with multi-source remote sensing data: Enhancing soil salinity inversion accuracy in barley-cultivated salinized soils
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DOI：10.1016/j.agwat.2025.109959.png)
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Abstract
En 中文
• Fusion of multi-source remote sensing data improves soil salinity estimation accuracy. • Random forest combined with recursive feature elimination effectively selects features relevant to soil salinity. • Ensemble learning outperforms single machine learning models in salinity estimation. • The study provides a robust, fine-scale approach for monitoring salinization in croplands.
Keywords:
GPR
Gaussian process regression
SVM
Support vector machine
ELM
Extreme learning machine
BP-MLP
Backpropagation-trained multilayer perceptron
RF-RFE
Random forest importance and recursive feature elimination
RR
Ridge regression
St-RR
Two-layer stacking with ridge regression
Weighted
Weighted averaging ensemble
Average
Arithmetic averaging ensemble
Multi-source data fusion
Feature selection
Machine learning
Ensemble learning
Soil salinity estimation
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