Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems
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Summary
This summary is machine-generated.Mapping total dissolved solids (TDS) in inland waters is challenging. This study uses Google Earth Engine, Sentinel-2 imagery, and machine learning to create detailed TDS maps, showing RF models are effective for water quality monitoring.
Area Of Science
- Environmental Science
- Remote Sensing
- Water Quality Monitoring
Background
- Total Dissolved Solids (TDS) are key water quality indicators in inland systems.
- In situ data collection for TDS is costly and labor-intensive, limiting spatial and temporal resolution.
- Advanced remote sensing and machine learning offer potential for efficient TDS mapping.
Purpose Of The Study
- To develop and validate a spatially explicit method for mapping TDS using Google Earth Engine (GEE) and Sentinel-2 imagery.
- To compare the effectiveness of Random Forest (RF) and Support Vector Machines (SVM) for TDS classification.
- To analyze temporal TDS variations and their correlation with land cover and rainfall in the Little Miami River (LMR).
Main Methods
- Integration of GEE, Sentinel-2 satellite imagery, and machine learning algorithms (RF and SVM).
- Extraction of spectral indices from Sentinel-2 data to train classification models.
- Analysis of TDS levels across the LMR from August to November over three years (2020-2023).
Main Results
- The RF model outperformed SVM, achieving high accuracy (up to 0.88 overall accuracy, 0.85 Kappa coefficient).
- Elevated TDS levels were identified, particularly in the midstream LMR sections.
- TDS fluctuations correlated with temporal rainfall patterns and were inversely related to natural cover (forests, wetlands).
Conclusions
- The GEE-based approach with Sentinel-2 and RF provides an effective tool for mapping TDS and monitoring water quality.
- Natural land cover enhances resilience to TDS fluctuations, highlighting the importance of conservation for water quality management.
- Findings offer valuable spatial insights for managing eutrophic issues in the LMR.
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