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.
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References
DOI
10.34133/remotesensing.1026
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.