Irrigation canals form a sort of circulatory system for global agriculture. Across the world, millions of kilometers of human-made canals carry water from lakes, rivers and reservoirs downhill to farmland. Canals are vitally important to human food production, yet many agricultural areas lack the means to monitor the ebb and flow of water through these waterways. This forces farmers and water managers to rely on scattered gauges, inspections and local reports to make decisions about water use.
A new study from the University of Washington suggests, however, that the tools needed for more comprehensive monitoring may already exist. In the study, researchers analyzed data from a NASA satellite built to track over time the water level of oceans, lakes, rivers and other large bodies of water. That project was never designed to track smaller bodies like canals. Yet, when the researchers looked at roughly 800,000 kilometers of canals in Asia, they found that the satellite could measure water levels with moderate to high confidence at more than 85% of locations.
The discovery could give farmers around the world a powerful new tool to manage precious water resources, prepare for droughts and improve food security in their communities.
"What we are seeing is not simply a new satellite capability," said co-author Faisal Hossain, a UW professor of civil and environmental engineering. "It may represent a fundamentally new way of managing the water conveyance systems that sustain modern agriculture."
The study was published Sept. 30 in Geophysical Research Letters.
The satellite at the center of the study, the Surface Water and Ocean Topography (SWOT) mission, is a joint effort of NASA and international partners. In orbit since December 2022, the satellite monitors the Earth's surface water at least once every three weeks using a tool called interferometric radar, which compares multiple radar scans over time to detect elevation changes.
Oceans, lakes and other large bodies of water are relatively straightforward to track in this manner. Irrigation canals, on the other hand, are frequently narrower than 20 meters, which puts them at the extreme low end of the satellite's observation power. The project's architects assumed that fluctuations within small canals would be invisible to the satellite.
Hossain wasn't so sure. In 2025, he and collaborators published the Global Registry of Agricultural Irrigation Networks (GRAIN), a global dataset that used open-source mapping data and machine learning to chart 3.8 million kilometers of canal networks. Lead author Mridul Sharma, a UW graduate research assistant in civil and environmental engineering, then overlaid SWOT's radar data on top of the canal map to see whether he could find meaningful elevation changes where there were canals.
"It was quite the accidental and pleasant discovery to see that SWOT is able to track flow direction and water levels in most of the canals skillfully," Sharma said. "GRAIN mapped where canals are. SWOT revealed what was happening inside them. They complemented each other beautifully."
As a proof of concept, the team focused on canals in 22 countries across Asia - a region where irrigation plays a critical role in feeding roughly 3 billion people. Researchers looked at the satellite's data along 800,000 kilometers of canals within that region. They then assigned a confidence score to each kilometer based on how closely the satellite's view of the canal water matched the known contours of the land and how strong the signal was compared to the noise in the data. As an additional check, they compared the satellite's data to real canal measurements in the United States and found that their confidence levels matched the system's actual abilities.
Of all the kilometers studied, the team designated 37.5% as highly observable, 46.9% as moderately observable and 15.6% as poorly observable. The highest-confidence areas corresponded to well-organized, wider canals, smooth slopes and open surroundings. Dense vegetation around canals proved the satellite's biggest obstacle. The satellite's performance is expected to improve over time as Hossain and other scientists working with SWOT data build better algorithms and analysis methods.
The next step for researchers is to build practical tools that leverage SWOT data. Hossain and collaborators at the UW are already building such systems to monitor and improve water delivery in South Asia and to aid in canal management in the western United States.
In time, farmers around the world could use SWOT-powered tools to monitor which canals are carrying sufficient water; see where water levels drop unexpectedly or where canal flow might be stagnant; and understand the evolution of water delivery across an entire growing season. For example, a farmer preparing to plant rice might see through SWOT that there won't be enough water arriving to their field to support the growth of the crop, and switch instead to a less water-intensive crop like corn or wheat.
Altogether, this new view of agriculture's circulatory system could improve drought preparedness, strengthen food security and help researchers map humanity's change to the global freshwater cycle.
"Some of the most important scientific discoveries happen when a tool designed for one purpose unexpectedly reveals something else," Hossain said. "SWOT may be offering a similar surprise.
"A mission built to study oceans and rivers has begun revealing the hidden dynamics of the canals that sustain agricultural production around the globe. What was once invisible is becoming measurable."
Co-authors include Sanchit Minocha and Shahzaib Khan, UW graduate research assistants in civil and environmental engineering; Sarath Suresh, a postdoctoral researcher at Virginia Tech who completed this research as a UW doctoral student of civil and environmental engineering; and Tamlin Pavelsky at University of North Carolina Chapel Hill.
This research was funded by NASA.
