Remote Sensing Monitoring of Soil Salinization Based on Bootstrap-Boruta Feature Stability Assessment: A Case Study in Minqin Lake Region.
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| Title: | Remote Sensing Monitoring of Soil Salinization Based on Bootstrap-Boruta Feature Stability Assessment: A Case Study in Minqin Lake Region. |
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| Authors: | Gao, Yukun1,2,3 (AUTHOR), Zhao, Dan1,2,3 (AUTHOR), Liang, Bing1,2,3 (AUTHOR), Yang, Xiya1,2,3 (AUTHOR), Xue, Xian1,2 (AUTHOR) xianxue@lzb.ac.cn |
| Source: | Remote Sensing. Jan2026, Vol. 18 Issue 2, p245. 32p. |
| Subjects: | Soil salinization, Remote sensing, Arid regions, Feature selection, Prediction models, Machine learning, Ensemble learning |
| Abstract: | Highlights: What are the main findings? A novel Bootstrap-Boruta framework is proposed to assess feature stability frequency rather than just importance, effectively filtering spurious correlations in small-sample data. The XGBoost ensemble strategy demonstrated superior stability compared to neural networks, achieving the highest conditional accuracy (R2 = 0.66) and convergence rate under rigorous bootstrap validation. What are the implications of the main findings? Prioritizing feature stability enhances model robustness and generalization in complex environments compared to traditional feature selection. Identifying stable spectral features enables more cost-effective and targeted long-term monitoring strategies in arid regions. Data uncertainty and limited model generalization remain critical bottlenecks in large-scale remote sensing of soil salinization. Although the integration of multi-source data has improved predictive potential, conventional deterministic feature selection methods often overlook stochastic noise inherent in environmental variables, leading to models that overfit spurious correlations rather than learning stable physical signals. To address this limitation, this study proposes a Bootstrap–Boruta feature stability assessment framework that shifts feature selection from deterministic "feature importance" ranking to probabilistic "feature stability" evaluation, explicitly accounting for uncertainty induced by data perturbations. The proposed framework is evaluated by integrating stability-driven feature sets with multiple machine learning models, including a Back-Propagation Neural Network (BPNN) optimized using the Red-billed Blue Magpie Optimization (RBMO) algorithm as a representative optimization strategy. Using the Minqin Lake region as a case study, the results demonstrate that the stability-based framework effectively filters unstable noise features, reduces systematic estimation bias, and improves predictive robustness across different modeling approaches. Among the tested models, the RBMO-optimized BPNN achieved the highest accuracy. Under a rigorous bootstrap validation framework, the quality-controlled ensemble model yielded a robust mean R2 of 0.657 ± 0.05 and an RMSE of 1.957 ± 0.289 dS/m. The framework further identifies eleven physically robust predictors, confirming the dominant diagnostic role of shortwave infrared (SWIR) indices in arid saline environments. Spatial mapping based on these stable features reveals that 30.7% of the study area is affected by varying degrees of soil salinization. Overall, this study provides a mechanism-driven, promising, within-region framework that enhances the reliability of remote-sensing-based soil salinity inversion under heterogeneous environmental conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A novel Bootstrap-Boruta framework is proposed to assess feature stability frequency rather than just importance, effectively filtering spurious correlations in small-sample data. The XGBoost ensemble strategy demonstrated superior stability compared to neural networks, achieving the highest conditional accuracy (R2 = 0.66) and convergence rate under rigorous bootstrap validation. What are the implications of the main findings? Prioritizing feature stability enhances model robustness and generalization in complex environments compared to traditional feature selection. Identifying stable spectral features enables more cost-effective and targeted long-term monitoring strategies in arid regions. Data uncertainty and limited model generalization remain critical bottlenecks in large-scale remote sensing of soil salinization. Although the integration of multi-source data has improved predictive potential, conventional deterministic feature selection methods often overlook stochastic noise inherent in environmental variables, leading to models that overfit spurious correlations rather than learning stable physical signals. To address this limitation, this study proposes a Bootstrap–Boruta feature stability assessment framework that shifts feature selection from deterministic "feature importance" ranking to probabilistic "feature stability" evaluation, explicitly accounting for uncertainty induced by data perturbations. The proposed framework is evaluated by integrating stability-driven feature sets with multiple machine learning models, including a Back-Propagation Neural Network (BPNN) optimized using the Red-billed Blue Magpie Optimization (RBMO) algorithm as a representative optimization strategy. Using the Minqin Lake region as a case study, the results demonstrate that the stability-based framework effectively filters unstable noise features, reduces systematic estimation bias, and improves predictive robustness across different modeling approaches. Among the tested models, the RBMO-optimized BPNN achieved the highest accuracy. Under a rigorous bootstrap validation framework, the quality-controlled ensemble model yielded a robust mean R2 of 0.657 ± 0.05 and an RMSE of 1.957 ± 0.289 dS/m. The framework further identifies eleven physically robust predictors, confirming the dominant diagnostic role of shortwave infrared (SWIR) indices in arid saline environments. Spatial mapping based on these stable features reveals that 30.7% of the study area is affected by varying degrees of soil salinization. Overall, this study provides a mechanism-driven, promising, within-region framework that enhances the reliability of remote-sensing-based soil salinity inversion under heterogeneous environmental conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18020245 |
