Remote Sensing Monitoring of Soil Salinization Based on Bootstrap-Boruta Feature Stability Assessment: A Case Study in the Minqin Lake Region

This dataset supports the study “Remote Sensing Monitoring of Soil Salinization Based on Bootstrap-Boruta Feature Stability Assessment: A Case Study in Minqin Lake Region.” It integrates satellite imagery, ground sample point data, and custom programming functions for reproducible modeling and analysis.
Research Hypothesis
Evaluating the stability of remote sensing features, rather than just their importance, and combining these with an advanced optimization algorithm (RBMO-BPNN) would yield more reliable and precise predictions of soil salinity in arid, complex environments.

Data Summary
Satellite imagery: Sentinel-1 SAR and Sentinel-2 MSI data processed at 10 m resolution, including salinity, vegetation, and moisture indices.
Ground data: 144 soil samples (June–July 2024) with measured electrical conductivity (EC) for calibration and validation.
Terrain and climate data: DEM-derived terrain features and 40-year climate averages to enhance environmental modeling.
Programming functions: Custom scripts for feature stability assessment, model training, and salinity mapping workflows.

Notable Findings
Identified 11 stable remote sensing features (e.g., NDSI, MNDWI, CRSI) with consistent predictive power.
The Bootstrap-Boruta + RBMO-BPNN approach achieved the highest accuracy (R² = 0.83, RMSE = 1.23), outperforming traditional methods by up to 24%.
Spatial prediction showed 31.5% of the region affected by salinization, with severe cases concentrated along oasis edges.

Interpretation & Usage
This dataset enables:
Reproduction of the feature stability framework and predictive models.
High-resolution mapping of soil salinity to guide land management, agricultural planning, and ecological restoration.
Adaptation of the methodology to other arid or semi-arid regions with appropriate environmental adjustments.