A remote sensing-based approach to suspended sediment estimation in rivers with scarce in situ data

Juliana Andrade Campos; Cintia B. Uvo; Thais Fujita; Luz Adriana Cuartas; Paulo Rógenes Monteiro Pontes

doi:10.1016/j.jhydrol.2026.135779

May 1, 2026Journal of HydrologyOpen Access

A remote sensing-based approach to suspended sediment estimation in rivers with scarce in situ data

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Key Points

This research aims to develop a remote sensing approach for estimating suspended sediment concentration (SSC) in rivers where in situ data is limited.
Artificial neural networks (ANNs) combined reflectance data, rainfall patterns, and soil characteristics.
Models capture SSC spikes during clear days and ensure continuity during cloudy periods.
Spatiotemporal analyses were conducted across multiple basins with little in situ data.
The reflectance models effectively captured event-driven SSC spikes on cloud-free days.
The rain/soil model provided consistent daily SSC estimates, even during cloudy weather.
The models collectively support continuous monitoring and analysis of SSC across basins.

Implication

Remote sensing measures suspended sediment in rivers using advanced models, highlighting its potential for data-scarce areas.

Abstract

• ANNs fuse reflectance, rainfall (CHIRPS) and soils to estimate river SSC. • Reflectance models capture event-driven SSC spikes on cloud-free days. • Rainfall/soil model provides daily SSC continuity during cloudy periods. • Complementary models enable continuous and event-sensitive SSC monitoring. • Models enable spatiotemporal analyses across basins with scarce in situ data.

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Cite This Study

Campos et al. (2026) studied this question.

synapsesocial.com/papers/6a1e728f30b38c64201b5c2d https://doi.org/10.1016/j.jhydrol.2026.135779

View Full Paper

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A remote sensing-based approach to suspended sediment estimation in rivers with scarce in situ data

Key Points

Implication

Abstract

Discussion

Cite This Study

A remote sensing-based approach to suspended sediment estimation in rivers with scarce in situ data

Key Points

Implication

Abstract

Discussion

Cite This Study

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