UW study finds NASA satellite can map 85% of canals

A NASA satellite initially designed to chart oceans and lakes is now revealing the hidden circulatory system of global agriculture. Researchers at the University of Washington discovered the Surface Water and Ocean Topography (SWOT) mission can measure water levels with moderate to high confidence at 85% of locations along roughly 800,000 kilometers of irrigation canals in Asia.

This unexpected capability could offer a powerful new tool for water management, allowing farmers to better prepare for droughts and improve food security. “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.”

SWOT Satellite Initially Designed for Ocean, Lake Monitoring

Launched in December 2022 as a joint NASA and international project, SWOT uses interferometric radar to detect elevation changes in surface water, a technique initially intended for oceans, lakes and rivers. This capability extends to much smaller waterways, revealing a previously unobservable network important for global agriculture. The success hinges on SWOT’s ability to repeatedly scan the Earth’s surface, at least once every three weeks, and compare radar signals over time.

While tracking large bodies of water presents a straightforward application of this technology, the resolution needed for canals was considered beyond the mission’s scope. “Some of the most important scientific discoveries happen when a tool designed for one purpose unexpectedly reveals something else,” explained a researcher involved in the study.

This accidental discovery complements existing mapping efforts, such as those undertaken by GRAIN, which identified canal locations, while SWOT quantified the water flowing within them. “GRAIN mapped where canals are. SWOT revealed what was happening inside them. They complemented each other beautifully.” The implications of this expanded capability extend beyond mere observation; it offers a potential shift in water management practices. Current systems rely heavily on scattered gauges, ground inspections and localized reports, a fragmented approach that limits comprehensive understanding.

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.

Mridul Sharma, UW graduate research assistant in civil and environmental engineering

GRAIN Dataset and SWOT Data Reveal Canal Networks

Lead author Mridul Sharma, a graduate research assistant in civil and environmental engineering, overlaid SWOT’s radar data onto the Global Registry of Agricultural Irrigation Networks (GRAIN) map to assess the satellite’s ability to detect meaningful changes in canal elevations. Researchers assigned a confidence score to each kilometer of canal based on signal strength and alignment with land contours, then validated the results against real-world measurements in the United States.

Of the total kilometers studied, 37.5% were designated as highly observable, while 46.9% fell into the moderately observable category and 15.6% were deemed poorly observable. This tiered assessment demonstrates the satellite’s varying capacity to accurately monitor canals based on their size and surrounding terrain. The team focused on Asia, a region where irrigation sustains approximately 3 billion people, highlighting the potential impact of this expanded observational capability.

The combination of GRAIN and SWOT proved particularly effective; “GRAIN mapped where canals are.” Sharma explained. This combination of existing mapping data with the satellite’s radar measurements provides a comprehensive view of irrigation networks previously unavailable. The findings, published in Geophysical Research Letters, suggest a new approach to water management, offering a detailed understanding of canal dynamics and potentially improving resource allocation for global agriculture.

It may represent a fundamentally new way of managing the water conveyance systems that sustain modern agriculture.

85% of Asian Canals Mapped with Moderate-High Confidence

The satellite data revealed water levels with high confidence along 37.5% of the 800,000 kilometers of Asian canals studied, exceeding expectations for a mission initially focused on larger water bodies. Researchers validated these confidence levels against ground-based measurements in the United States, confirming the satellite’s ability to accurately assess canal conditions where signal strength permitted.

This corroboration is critical, as the satellite was never designed for this level of granular monitoring, and the team needed to establish a baseline for data reliability. Approximately 46.9% of the studied canals fell into the moderately observable category, while 15.6% were designated as poorly observable, demonstrating a variable capacity for accurate monitoring.

Some of the most important scientific discoveries happen when a tool designed for one purpose unexpectedly reveals something else.

Faisal Hossain, UW professor of civil and environmental engineering

SWOT Data Potential for Improved Water Management & Food Security

The ability to remotely monitor these vital irrigation channels, previously reliant on scattered gauges and local reports, offers a new level of comprehensive oversight. Researchers are now developing practical tools to use this data, beginning with systems to monitor water delivery in South Asia and improve canal management in the western United States.

QuAIL applies quantum optimisation, simulation and machine learning to complex NASA mission problems, enhancing data processing and analytical power. Dense vegetation around canals presented the most significant obstacle to accurate readings, but researchers anticipate improvements as algorithms are refined and analysis methods evolve.

GRAIN mapped where canals are. SWOT revealed what was happening inside them. They complemented each other beautifully.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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