Remote Sensing of Sediment Discharge in Rivers Using Sentinel-2 Images and Machine-Learning Algorithms
نویسندگان
چکیده
The spatio-temporal dynamism of sediment discharge (Qs) in rivers is influenced by various natural and anthropogenic factors. Unfortunately, most are only monitored at a limited number stations or not gauged all. Therefore, this study aims to provide remote-sensing-based alternative for Qs monitoring. at-a-station hydraulic geometry (AHG) power–law method was compared the at-many-stations (AMHG) method; addition, novel AHG machine-learning (ML) introduced estimate water three gauging Tisza (Szeged Algyő) Maros (Makó) Rivers Hungary. surface reflectance Sentinel-2 images correlated situ suspended concentration (SSC) support vector machine (SVM), random forest (RF), artificial neural network (ANN), combined algorithms. best performing SSC models were employed Qs. Our ML gave estimations (Szeged: R2 = 0.87; Algyő: 0.75; Makó: 0.61). Furthermore, RF (R2 0.9) 0.82) showed Rivers. highest detected during floods; however, there usually clockwise hysteresis between discharge, especially River.
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ژورنال
عنوان ژورنال: Hydrology
سال: 2022
ISSN: ['2330-7609', '2330-7617']
DOI: https://doi.org/10.3390/hydrology9050088