Estimating River Suspended Sediment Concentrations From Sentinel‐2: Expanding the Training Set and Applicability to Multi‐Dammed Systems
- 1Department of Earth and Environmental Sciences Boston College Chesnut Hill MA USA
- 2Department of Geology Colby College Waterville ME USA
- 3Department of Geology and Environmental Geosciences Dayton University Dayton OH USA
Abstract
In situ measurements of riverine suspended sediment concentrations (SSC) are often sparse, limiting assessment of important patterns in space and changes in time. SSC measurements based on satellite data are a potential solution to this limitation, but training algorithms to estimate SSC from satellite data also requires substantial in situ data. This study presents new efforts to use Sentinel‐2 satellite data to infer SSC in large rivers (≥60 m width). Sentinel‐2 reflectance data has the benefit of a high relative frequency, with a nominal return period of 5 days, improving the likelihood that in situ SSC data was collected coincident with satellite overpass. We generated additional SSC training data using discharge—sediment flux rating curves and estimates derived from Landsat data using Dethier et al. (2020, https://doi.org/10.1029/2019jf005033) algorithms. The resulting algorithms were calibrated using 19,345 SSC observations, 23% of which were from this unconventional training data set. Despite the varied training base, these Sentinel‐2 algorithms can accurately predict suspended‐sediment concentrations (68% relative error). A case study on the heavily dammed Chattahoochee‐Apalachicola watershed accurately reproduced sparse in situ results and extended spatial and temporal coverage, highlighting the potential for improved understanding of river processes using Sentinel‐2‐based algorithms.
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BibTeX
@article{valencius2026estimating,
title = {{Estimating River Suspended Sediment Concentrations From Sentinel‐2: Expanding the Training Set and Applicability to Multi‐Dammed Systems}},
author = {Ilan Valencius and Evan Dethier and Christopher Sheehan and Noah Snyder},
journal = {Water Resources Research},
year = {2026},
month = oct,
doi = {10.1029/2025wr041855},
url = {https://doi.org/10.1029/2025wr041855},
note = {CC BY},
}
RIS
TY - JOUR
TI - Estimating River Suspended Sediment Concentrations From Sentinel‐2: Expanding the Training Set and Applicability to Multi‐Dammed Systems
AU - Ilan Valencius
AU - Evan Dethier
AU - Christopher Sheehan
AU - Noah Snyder
PY - 2026
DA - 2026/10/28
JO - Water Resources Research
DO - 10.1029/2025wr041855
UR - https://doi.org/10.1029/2025wr041855
AB - In situ measurements of riverine suspended sediment concentrations (SSC) are often sparse, limiting assessment of important patterns in space and changes in time. SSC measurements based on satellite data are a potential solution to this limitation, but training algorithms to estimate SSC from satellite data also requires substantial in situ data. This study presents new efforts to use Sentinel‐2 satellite data to infer SSC in large rivers (≥60 m width). Sentinel‐2 reflectance data has the benefit of a high relative frequency, with a nominal return period of 5 days, improving the likelihood that in situ SSC data was collected coincident with satellite overpass. We generated additional SSC training data using discharge—sediment flux rating curves and estimates derived from Landsat data using Dethier et al. (2020, https://doi.org/10.1029/2019jf005033) algorithms. The resulting algorithms were calibrated using 19,345 SSC observations, 23% of which were from this unconventional training data set. Despite the varied training base, these Sentinel‐2 algorithms can accurately predict suspended‐sediment concentrations (68% relative error). A case study on the heavily dammed Chattahoochee‐Apalachicola watershed accurately reproduced sparse in situ results and extended spatial and temporal coverage, highlighting the potential for improved understanding of river processes using Sentinel‐2‐based algorithms.
SN - 1944-7973
SN - 0043-1397
ER -