A recent study demonstrates that the Surface Water and Ocean Topography (SWOT) satellite can track lake volume changes across China with high accuracy, offering greatly improved monitoring of small lakes that were often missed by earlier methods. By combining simultaneous measurements of lake level and area with supporting bathymetric data, researchers identified clear seasonal patterns and an overall rise in lake volume, driven primarily by natural and larger lakes.
Lake volume is a key indicator of water security, ecosystem health, flood management, and climate response. Yet traditional field measurements are sparse—particularly in remote regions—and earlier satellite approaches often struggled to measure lake level and area simultaneously. Small lakes have been especially challenging to monitor due to limited spatial resolution, cloud interference, and mismatched observation timing across sensors. These gaps have made it difficult to build reliable, large-scale records of inland water changes.
To address these challenges, a team from the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), the International Research Center of Big Data for Sustainable Development Goals, and the University of Chinese Academy of Sciences investigated whether the SWOT mission could provide a practical solution for large-scale lake volume monitoring. Their study was published on January 29, 2026, in the Journal of Remote Sensing .
The researchers assessed whether the officially released SWOT lake products could overcome long-standing challenges in tracking inland water storage, particularly for small lakes and regions with incomplete conventional observations. They found that SWOT significantly enhances the monitoring of Chinese lakes, especially smaller ones, producing volume estimates with high reliability. Validation against in situ reservoir records showed that most errors were within 10%, with the best case reaching only 3.92%.
Using their workflow, the team generated lake-volume estimates for 1,596 lakes—1,556 derived directly from SWOT observations and 40 supplemented with external bathymetric data. Statistically significant volume trends were identified in 583 lakes, revealing an overall increase of 0.7754 Gt per month. About 85% of this change came from natural lakes, while large and super-large lakes contributed most to the increase.
To build the dataset, the team analyzed SWOT's 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 the lake, they applied constant-area, linear, quadratic, or cubic models and incorporated external bathymetric datasets where SWOT alone could not fully cover larger lakes. In validation tests, lake-level-based estimates outperformed area-based estimates, showing lower errors and better stability.
The results also revealed strong regional seasonality: lakes in eastern China generally rise from winter to summer and decline 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 data products and processing methods continue to improve, satellite-based lake monitoring could become a more powerful tool for water management and climate studies."
The team leveraged SWOT KaRIn lake products, the SWOT Prior Lake Database, in situ validation records, and supporting bathymetric and hydrologic datasets, including DAHITI, GRBD, and GLWS. They filtered observations by quality flags, removed anomalies, fit lake level–area relationships, estimated volume changes through curve integration, and assessed trend uncertainty using Monte Carlo sampling.
The study suggests that SWOT could become an essential 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 coverage remains limited for the smallest water bodies, future data releases and improved processing are expected to enhance both accuracy and coverage. This advancement could make near-real-time monitoring of inland water storage far more feasible at regional and national scales.
Lake extent derived from SWOT L2 HR LakeSP data compared to Sentinel-2 optical images in case lakes. (Image by AIRCAS)
Research News
SWOT Opens New Era for Lake Monitoring
A recent study demonstrates that the Surface Water and Ocean Topography (SWOT) satellite can track lake volume changes across China with high accuracy, offering greatly improved monitoring of small lakes that were often missed by earlier methods. By combining simultaneous measurements of lake level and area with supporting bathymetric data, researchers identified clear seasonal patterns and an overall rise in lake volume, driven primarily by natural and larger lakes.
Lake volume is a key indicator of water security, ecosystem health, flood management, and climate response. Yet traditional field measurements are sparse—particularly in remote regions—and earlier satellite approaches often struggled to measure lake level and area simultaneously. Small lakes have been especially challenging to monitor due to limited spatial resolution, cloud interference, and mismatched observation timing across sensors. These gaps have made it difficult to build reliable, large-scale records of inland water changes.
To address these challenges, a team from the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), the International Research Center of Big Data for Sustainable Development Goals, and the University of Chinese Academy of Sciences investigated whether the SWOT mission could provide a practical solution for large-scale lake volume monitoring. Their study was published on January 29, 2026, in the Journal of Remote Sensing .
The researchers assessed whether the officially released SWOT lake products could overcome long-standing challenges in tracking inland water storage, particularly for small lakes and regions with incomplete conventional observations. They found that SWOT significantly enhances the monitoring of Chinese lakes, especially smaller ones, producing volume estimates with high reliability. Validation against in situ reservoir records showed that most errors were within 10%, with the best case reaching only 3.92%.
Using their workflow, the team generated lake-volume estimates for 1,596 lakes—1,556 derived directly from SWOT observations and 40 supplemented with external bathymetric data. Statistically significant volume trends were identified in 583 lakes, revealing an overall increase of 0.7754 Gt per month. About 85% of this change came from natural lakes, while large and super-large lakes contributed most to the increase.
To build the dataset, the team analyzed SWOT's 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 the lake, they applied constant-area, linear, quadratic, or cubic models and incorporated external bathymetric datasets where SWOT alone could not fully cover larger lakes. In validation tests, lake-level-based estimates outperformed area-based estimates, showing lower errors and better stability.
The results also revealed strong regional seasonality: lakes in eastern China generally rise from winter to summer and decline 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 data products and processing methods continue to improve, satellite-based lake monitoring could become a more powerful tool for water management and climate studies."
The team leveraged SWOT KaRIn lake products, the SWOT Prior Lake Database, in situ validation records, and supporting bathymetric and hydrologic datasets, including DAHITI, GRBD, and GLWS. They filtered observations by quality flags, removed anomalies, fit lake level–area relationships, estimated volume changes through curve integration, and assessed trend uncertainty using Monte Carlo sampling.
The study suggests that SWOT could become an essential 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 coverage remains limited for the smallest water bodies, future data releases and improved processing are expected to enhance both accuracy and coverage. This advancement could make near-real-time monitoring of inland water storage far more feasible at regional and national scales.
Lake extent derived from SWOT L2 HR LakeSP data compared to Sentinel-2 optical images in case lakes. (Image by AIRCAS)