Integration of Google Earth Engine, Sentinel-2 images, and machine learning for temporal mapping of total dissolved solids in river systems.

ABSTRACT: One of the important indicators of water quality (WQ) in inland water systems is total dissolved solids (TDS). Collecting and maintaining in situ TDS data with high spatial and temporal resolution is time and money-consuming. This study highlights an advanced approach integrating Google Earth Engine (GEE), remote sensing techniques using Sentinel-2 imagery, and machine learning algorithms to map TDS in a spatially explicit manner. We extracted relevant spectral indices and used them to train machine learning models, specifically Random Forest (RF) and Support Vector Machines (SVM), to classify TDS levels across the stretch of the Little Miami River (LMR). We analyzed TDS for August, September, October, and November, and over three years, from 2020 to 2023. Results showed RF to be more effe

SUBMITTER: Salas EAL 

PROVIDER: S-EPMC12307934 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature