Quantifying river morphological changes using multi‐satellite observations and <i>in situ</i> measurements

Summary Field surveys for monitoring river morphology are resource‐intensive, especially in large river systems. Earth observation (EO) satellites provide valuable alternatives, yet the integrated use of multiple missions remains underexplored. This study develops a framework that combines Landsat, Sentinel‐1, Sentinel‐2 and Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2) data to estimate periodically inundated bed topography and morphological change in the large alluvial Jamuna River. The method achieved an accuracy of m of RMSE in topography estimation, with a tendency to overestimate elevations with a mean bias of 1.24 m while preserving slope. By relating water occurrence probability to elevation, morphological changes were quantified for elevation bands, enabling the estimation of area and volume dynamics while accounting for interannual hydrological variability. Compared with the earlier EO‐based morphological monitoring tool—which tends to overestimate erosion and accretion areas—the proposed framework reduced false detections (%) by accounting for hydrological variability and refining change‐classification thresholds. Estimated volumes were comparable in magnitude to EO‐based estimates reported in the literature. However, validation with field data revealed that erosion and accretion volumes were underestimated primarily due to the inability to detect changes in aperiodic bed and an inconsistent temporal scale of field and satellite data. Despite these limitations, the method successfully captured dominant reach‐scale morphological processes and is suitable for deriving first‐order estimates of morphological changes in large rivers with seasonally fluctuating water levels. While further research is needed to better quantify errors/uncertainties and validate the SWOT‐based water surface elevation profile correction, this work lays a foundation for using multi‐satellite EO data for large‐scale river morphological assessments.

Authors

Institutions

Publication Details

Journal
Earth Surface Processes and Landforms
Published
2026-02-26
DOI
https://doi.org/10.1002/esp.70263
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantifying river morphological changes using multi‐satellite observations and in situ measurements

Tek Narayan Bhattarai, Amgad Omer, Maarten Bakker, Sanjay Giri et al.
Earth Surface Processes and Landforms
Flood Risk Assessment and Management
article

Quantifying river morphological changes using multi‐satellite observations and in situ measurements

Tek Narayan Bhattarai, Amgad Omer, Maarten Bakker, Sanjay Giri, Allen Bateman, Antonio Moreno‐Rodenas
article en

Abstract

Summary Field surveys for monitoring river morphology are resource‐intensive, especially in large river systems. Earth observation (EO) satellites provide valuable alternatives, yet the integrated use of multiple missions remains underexplored. This study develops a framework that combines Landsat, Sentinel‐1, Sentinel‐2 and Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2) data to estimate periodically inundated bed topography and morphological change in the large alluvial Jamuna River. The method achieved an accuracy of m of RMSE in topography estimation, with a tendency to overestimate elevations with a mean bias of 1.24 m while preserving slope. By relating water occurrence probability to elevation, morphological changes were quantified for elevation bands, enabling the estimation of area and volume dynamics while accounting for interannual hydrological variability. Compared with the earlier EO‐based morphological monitoring tool—which tends to overestimate erosion and accretion areas—the proposed framework reduced false detections (%) by accounting for hydrological variability and refining change‐classification thresholds. Estimated volumes were comparable in magnitude to EO‐based estimates reported in the literature. However, validation with field data revealed that erosion and accretion volumes were underestimated primarily due to the inability to detect changes in aperiodic bed and an inconsistent temporal scale of field and satellite data. Despite these limitations, the method successfully captured dominant reach‐scale morphological processes and is suitable for deriving first‐order estimates of morphological changes in large rivers with seasonally fluctuating water levels. While further research is needed to better quantify errors/uncertainties and validate the SWOT‐based water surface elevation profile correction, this work lays a foundation for using multi‐satellite EO data for large‐scale river morphological assessments.

Earth Surface Processes and LandformsVol. 51(3)
IHE Delft Institute for Water Education (NL), Deltares (NL), Royal HaskoningDHV (Netherlands) (NL), Carnegie Mellon University (US), Universitat Politècnica de Catalunya (ES)
Openalex Percentile: Top 43%
Flood Risk Assessment and Management
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.