New satellite model maps Yellow River’s turbidity
image:
Map of the Yellow River (YR) basin showing the distribution of major rivers, reservoirs, and sampling sites.
view moreCredit: Journal of Remote Sensing
A new satellite-based study has reconstructed suspended sediment concentration across the Yellow River over nearly 40 years, revealing not only a long-term decline but also a recent break in that downward trend. By combining Landsat imagery with a newly designed retrieval model, the research shows how reservoirs, tributaries, vegetation recovery, and check dams have jointly reshaped sediment patterns along one of the world’s most sediment-laden rivers.
Suspended sediment concentration is a core indicator of river health because it affects channel evolution, delta formation, flood risk, and aquatic ecosystems. In the Yellow River, sediment has long been a defining feature, especially after the river crosses the erosion-prone Loess Plateau. Although previous station measurements showed a major decline in sediment load in recent decades, those observations covered only limited river sections and could not capture full basin-wide patterns over time. Based on these challenges, deeper research into long-term, river-wide sediment dynamics was needed.
Researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences, the International Research Center of Big Data for Sustainable Development Goals, and collaborating institutions reported (DOI: 10.34133/remotesensing.0940) the study in the Journal of Remote Sensing on February 10, 2026. The work addresses a persistent challenge in river science: how to monitor sediment change continuously across the entire Yellow River, rather than relying only on scattered hydrological stations.
The team found that sediment concentration generally increased from the upper reaches toward the estuary, but with sharp local drops near major reservoirs and spikes where sediment-rich tributaries joined the main channel. Over time, the river passed through three phases: rising sediment from 1986 to 1997, a strong decline from 1997 to 2016, and a more variable pattern after 2016, indicating a recent shift from the earlier long-term decrease. The study also showed that human activities dominated these changes, especially reservoir trapping, vegetation-driven stabilization, and check dam retention.
To make the reconstruction possible, the team developed a piecewise retrieval algorithm tailored to the Yellow River’s unusually wide sediment range. Using Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI imagery, they processed 12,426 cloud-filtered surface reflectance scenes from 1986 to 2023. The OLI model was calibrated with 65 field-matched samples collected in 2022–2023, then extended to earlier sensors through cross-sensor matching. Validation showed strong performance, with R² values of 0.94 for OLI, 0.96 for ETM+, and 0.95 for TM. Spatially, average suspended sediment concentration was about 742.7 mg/L in the upper reaches, 899.8 mg/L in the middle reaches, and 1,244.8 mg/L in the lower reaches. The river-wide peak occurred in 1996–1997 at 1,265.7 mg/L, while the minimum appeared in 2016–2017 at 530.5 mg/L. Statistical analysis further suggested that trap efficiency of reservoirs contributed 58.6% of interannual variation, vegetation cover 23.7%, and check dams 6.7%, exceeding the influence of precipitation, wind speed, and runoff.
The authors concluded that their work provides a comprehensive spatiotemporal picture of suspended sediment along the full Yellow River and reveals a recent shift in the long-term decreasing trend. They argue that these findings offer valuable scientific support for basin management, especially where water regulation, erosion control, and ecological restoration must be coordinated.
The study combined field sampling, satellite remote sensing, and statistical modeling. Water samples were collected from 0 to 50 cm below the surface at sites in the upper, middle, and lower reaches during Landsat-8 overpasses in 2022–2023. The researchers then built a three-segment spectral inversion model using red, green, and near-infrared bands, identified water bodies with MNDWI, mapped river centerlines every two years, and analyzed trends with Theil–Sen and Mann–Kendall methods.
This approach could help support sediment forecasting, reservoir management, river restoration, and ecological monitoring not only in the Yellow River but also in other highly turbid river systems. By turning decades of satellite records into long-term sediment maps, the method offers a practical way to detect regime shifts that conventional station networks may miss. It may also improve future decision-making under climate change and expanding human river engineering.
###
References
DOI
Original Source URL
https://spj.science.org/doi/10.34133/remotesensing.0940
Funding Information
The research was supported by the National Natural Science Foundation of China (No. 42271363) and the Future Star Program of Aerospace Information Research Institute Chinese Academy of Sciences.
About Journal of Remote Sensing
The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
Satellite-Observed Spatiotemporal Variations of Suspended Sediment Concentration in the Yellow River over the Past 40 Years: A Recent Shift in the Long-Term Decreasing Trend
SWOT opens a new era for lake monitoring
image:
Lake extent derived from SWOT L2 HR LakeSP data compared to Sentinel-2 optical images in case lakes. The case lakes were also accompanied by their reference extents from the PLD dataset. (A) Part of Huitingshan Reservoir located in Hubei Province. (B) Daxi Reservoir located in Jiangsu Province. (C) Bayi Reservoir located in Xinjiang Province. (D) Nam Co located in Xizang Province. Ground track of SWOT orbit 495 is also shown to indicate nadir positions. Date of SWOT overpass was 2023 October 30, corresponding to orbit 495, cycle 005. Sentinel-2 image was composited within the year 2023.
view moreCredit: Journal of Remote Sensing
A new study shows that the Surface Water and Ocean Topography (SWOT) satellite can track lake volume changes across China with strong accuracy while greatly improving the monitoring of small lakes that were often missed before. By combining synchronous measurements of lake level and area with supporting bathymetric data, the researchers found clear seasonal patterns and an overall rise in lake volume, driven mainly by natural and larger lakes.
Lake volume is a key indicator for water security, ecosystem stability, flood control, and climate response. Yet traditional field measurements are sparse, especially in remote regions, and earlier satellite approaches often struggled to capture lake level and lake area at the same time. Small lakes have been particularly difficult to monitor because of limited spatial resolution, cloud interference, and mismatched observation timing across sensors. These gaps have made it hard to build reliable, large-scale records of inland water change. Based on these challenges, deeper research was needed to evaluate whether the new Surface Water and Ocean Topography (SWOT) mission could provide a practical solution for large-scale lake volume monitoring.
Researchers from the Aerospace Information Research Institute, Chinese Academy of Sciences, the International Research Center of Big Data for Sustainable Development Goals, and the University of Chinese Academy of Sciences published (DOI: 10.34133/remotesensing.1026) the study on January 29, 2026 in the Journal of Remote Sensing. The team examined whether officially released SWOT lake products could overcome long-standing problems in tracking inland water storage, especially for small lakes and regions where conventional observations remain incomplete.
The study found that SWOT significantly strengthened the monitoring of Chinese lakes, especially smaller ones, and produced volume estimates with high reliability. Validation against in situ reservoir records showed that most errors stayed within 10%, with the best case reaching just 3.92%. Using their workflow, the researchers generated lake-volume estimates for 1,596 lakes, including 1,556 calculated directly from SWOT observations and 40 supplemented with external bathymetric data. They also identified statistically significant volume trends in 583 lakes and found that the monitored lake system showed an overall increase of 0.7754 Gt per month. About 85% of the total change came from natural lakes, while large and super-large lakes contributed most to the increase.
To build the dataset, the team analyzed SWOT "Level 2 Lake Single-Pass Vector Data Product" records from April 2023 to December 2024 for lakes larger than 0.0625 km² in China. They filtered low-quality observations, removed outliers, and matched lake-level and lake-area measurements to construct hypsometric models that convert water-surface changes into volume change. Depending on lake behavior, they used constant-area, linear, quadratic, or cubic models, and added external bathymetric datasets where SWOT alone could not fully cover large lakes. In validation tests, lake-level-based estimates outperformed area-based estimates, showing much lower error and better stability. The results also revealed strong regional seasonality: lakes in eastern China generally rose from winter to summer and declined from summer to autumn, while ice cover caused winter monitoring gaps in plateau and northern regions. Even so, SWOT showed high observation frequency for small lakes and broad spatial coverage across China's five major lake regions.
Suggested quote for a news release: "Our results show that SWOT is already capable of providing a much clearer picture of lake-volume change across China, especially for smaller lakes that were previously difficult to observe. As the data products and processing methods continue to improve, satellite-based lake monitoring could become a more powerful tool for water management and climate studies." This wording is adapted from the paper's conclusions rather than quoted verbatim.
The team used SWOT KaRIn lake products, the SWOT Prior Lake Database, in situ validation records, and several supporting bathymetric and hydrologic datasets, including DAHITI, GRBD, and GLWS. They filtered observations by quality flags, removed anomalous values statistically, fit lake level–area relationships, estimated volume changes through curve integration, and assessed trend uncertainty with Monte Carlo sampling.
The study suggests that SWOT could become an important tool for basin-scale water accounting, drought and flood assessment, reservoir management, and climate-change research. Although current area measurements may still overestimate some lakes and volume coverage remains limited for the smallest water bodies, future data releases and improved processing are expected to expand both accuracy and coverage. That would make near-real-time monitoring of inland water storage far more feasible at regional to national scales.
###
References
DOI
Original Source URL
https://doi.org/10.34133/remotesensing.1026
Funding information
This research was supported by the National Key R&D Plan of China (grant 2024YFF0808302) and the National Natural Science Foundation of China (grant 41871256).
About Journal of Remote Sensing
Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
Journal
Journal of Remote Sensing
Subject of Research
Not applicable
Article Title
Exploring the Performance of SWOT Satellite to Monitor Lake Volumes: A Case Study of Chinese Lakes
No comments:
Post a Comment