Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery

• A Sentinel-2 algorithm for deriving suspended sediment concentrations was developed. • SSCs derived from Sentinel-2 and Landsat were spatiotemporally consistent. • The Yellow River tributaries showed notable spatiotemporal variability in SSC. • Spatiotemporal coverage of Sentinel-2 improved by 107.15% and 204.08% over Landsat. • Combining Sentinel-2 could boost the observation frequency of rivers by 35.29%. Yellow River is famous for its exceptionally higher suspended sediment concentrations (SSC), displaying significant spatiotemporal heterogeneity across diverse sections. Although SSC monitoring of the Yellow River and some of its tributaries has been achieved using Landsat data, it remains unclear whether the inclusion of higher spatial resolution satellites can expand the spatiotemporal monitoring capabilities for the Yellow River and most of its tributaries. In this study, we employed Sentinel-2 imagery, offering superior spatiotemporal resolution, to develop a higher-accurate SSC model and quantitatively evaluated its potential to improve the spatiotemporal coverage of SSC monitoring compared to Landsat satellites. For the Yellow River in the Loess Plateau, the optimized Sentinel-2 model exhibited superior accuracy, achieving R 2 = 0.91, root mean square error of 728.76 mg/L, and unbiased percentage difference of 16.75%. Notably, distinct SSC distribution differences were observed across different rivers, indicating significant spatial heterogeneity (SSC: 0.58 – 3.01 × 10 5 mg/L). Moreover, Sentinel-2 showed a significant increase in observation frequency and spatial coverage (204.08% and 107.15%, respectively) compared to Landsat. An additional 35.29% increase in observation frequency was achieved through the combined satellite observation method. Furthermore, based on river width statistics, we found that upgrading the spatial resolution from 10 m to 1 m enhanced the coverage of observable river segments in the Loess Plateau by approximately 47.96%, and by about 50.56% globally. This study established a crucial scientific foundation for integrating Sentinel-2 and Landsat, enabling finer-scale monitoring and management of river sediment.

Authors

Institutions

Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2024-10-11
DOI
https://doi.org/10.1016/j.jag.2024.104209
Citations
7
Primary Topic
Soil erosion and sediment transport
Type
article
Field-Weighted Citation Impact
2.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery

Hongtao Duan, Zhiqiang Qiu, Yang Chen, Dong Liu et al.
7 citations
International Journal of Applied Earth Observation and Geoinformation
Soil erosion and sediment transport
2.34
article

Improving the observations of suspended sediment concentrations in rivers from Landsat to Sentinel-2 imagery

Hongtao Duan, Zhiqiang Qiu, Yang Chen, Dong Liu, Panpan Chen, Chenxue Zhang, Nuoxiao Yan
article en
7 citations

Abstract

• A Sentinel-2 algorithm for deriving suspended sediment concentrations was developed. • SSCs derived from Sentinel-2 and Landsat were spatiotemporally consistent. • The Yellow River tributaries showed notable spatiotemporal variability in SSC. • Spatiotemporal coverage of Sentinel-2 improved by 107.15% and 204.08% over Landsat. • Combining Sentinel-2 could boost the observation frequency of rivers by 35.29%. Yellow River is famous for its exceptionally higher suspended sediment concentrations (SSC), displaying significant spatiotemporal heterogeneity across diverse sections. Although SSC monitoring of the Yellow River and some of its tributaries has been achieved using Landsat data, it remains unclear whether the inclusion of higher spatial resolution satellites can expand the spatiotemporal monitoring capabilities for the Yellow River and most of its tributaries. In this study, we employed Sentinel-2 imagery, offering superior spatiotemporal resolution, to develop a higher-accurate SSC model and quantitatively evaluated its potential to improve the spatiotemporal coverage of SSC monitoring compared to Landsat satellites. For the Yellow River in the Loess Plateau, the optimized Sentinel-2 model exhibited superior accuracy, achieving R 2 = 0.91, root mean square error of 728.76 mg/L, and unbiased percentage difference of 16.75%. Notably, distinct SSC distribution differences were observed across different rivers, indicating significant spatial heterogeneity (SSC: 0.58 – 3.01 × 10 5 mg/L). Moreover, Sentinel-2 showed a significant increase in observation frequency and spatial coverage (204.08% and 107.15%, respectively) compared to Landsat. An additional 35.29% increase in observation frequency was achieved through the combined satellite observation method. Furthermore, based on river width statistics, we found that upgrading the spatial resolution from 10 m to 1 m enhanced the coverage of observable river segments in the Loess Plateau by approximately 47.96%, and by about 50.56% globally. This study established a crucial scientific foundation for integrating Sentinel-2 and Landsat, enabling finer-scale monitoring and management of river sediment.

International Journal of Applied Earth Observation and GeoinformationVol. 134
Chinese Academy of Sciences (CN), Nanjing University of Information Science and Technology (CN), Northwest University (CN), Nanjing Institute of Geography and Limnology (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences
Openalex Percentile: Top 12%
Soil erosion and sediment transport
2.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.