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A longitudinal analysis of soil salinity changes using remotely sensed imageries.
Soraya Bandak1, Seyed Alireza Movahedi-Naeini2, Saeed Mehri3
1Department of Soil Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. soraya.bandak@gmail.com.
Scientific Reports
|May 6, 2024
Summary
Remote sensing effectively monitors soil salinity using indices from Landsat 8 and Sentinel 2 data. Decision Tree models accurately predict salinity, aiding land management and conservation in affected regions.
Area of Science:
- Environmental Science
- Agricultural Science
- Remote Sensing
Background:
- Soil salinization severely impacts agricultural productivity, causing desertification and land degradation.
- Large-scale field and laboratory studies for soil salinity are labor-intensive and costly.
- Remote sensing offers a viable alternative for large-scale soil salinity assessment.
Purpose of the Study:
- To evaluate the effectiveness of various soil salinity indices derived from remote sensing data.
- To compare the performance of machine learning models for predicting soil electrical conductivity.
- To identify key topographic and moisture-related indices for soil salinity mapping.
Main Methods:
- Utilized Landsat 8 (L8) and Sentinel 2 (S2) satellite imagery.
- Employed machine learning algorithms: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), and Support Vector Machine (SVR).
- Correlated derived salinity indices with 280 ground-truth soil electrical conductivity (EC) samples across 24,000 hectares.
Main Results:
- The Decision Tree (DT) model demonstrated superior performance, achieving R² values of 0.85 (L8) and 0.86 (S2).
- Key influential salinity indices identified include Multi-resolution Valley Bottom Flatness (MrVBF), moisture index, Topographic Wetness Index (TWI), and Topographic Position Indicator (TPI).
- Time series analysis revealed significant reductions in soil salinity and sodium levels in areas with implemented drainage systems.
Conclusions:
- Remote sensing, coupled with machine learning, provides an accurate and efficient method for assessing soil salinity.
- Topographic and moisture indices are crucial for understanding and predicting soil salinity patterns.
- Effective land management strategies, such as drainage systems, can successfully mitigate soil salinization.
