Abstract
Terrestrial sediment transport through large rivers exerts a significant impact on coastal morphology, marine ecosystems, and human livelihoods. Accurately measuring these sediment discharges has long been a challenge. Traditional in-situ methods fall short of providing comprehensive and continuous assessments of sediment dynamics due to spatiotemporal and economic constraints. While remote sensing techniques using satellite imagery have offered valuable insights into sediment transportation and deposition, their scope is primarily restricted to observing suspended sediment loads rather than total loads. Sediment accumulation at river margins will cause gravity increases observable by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions. Previous efforts to observe sediment signals lacked proper corrections for various GRACE/GFO issues, including leakage of signals from surrounding land, variations in nearby ocean mass, and noise levels that typically exceed sediment signal magnitudes. In this study, we develop a new approach to obtain a satellite gravity estimate of sediment accumulation along the Amazon coast where the largest amount of sediment deposition is expected. We address limitations in previous studies using three steps: Forward modeling to suppress leakage of signal from land to oceans; adjusting ocean mass change via the sea level equation; and filtering using empirical orthogonal functions. The estimated accumulation rate of sediment on the Amazon continental shelf is approximately 1301 Mtons per year for the period June 2002 to May 2023. This estimate is slightly higher than the results from field-based studies, which fall in the range 550 to 1030 Mtons per year.
Original language | English |
---|---|
Article number | 114688 |
Journal | Remote Sensing of Environment |
Volume | 321 |
DOIs | |
Publication status | Published - 1 May 2025 |
Keywords
- Amazon River
- Coastal geodesy
- Satellite gravimetry
- Sediment transport
ASJC Scopus subject areas
- Soil Science
- Geology
- Computers in Earth Sciences