CHAMPAIGN, Ill. -- Using a new algorithm, University of Illinois researchers may have found the solution to an age-old dilemma plaguing satellite imagery - whether to sacrifice high spatial resolution in the interest of generating images more frequently, or vice versa.
The team's new tool eliminates this trade-off by fusing high-resolution and high-frequency satellite data into one integrated product, and can generate 30-meter daily continuous images going back to the year 2000.
In agricultural applications, imaging at 10- to 30-meter resolution is critical for farmers to see field-level rapid and subtle changes in crop conditions that affect yield, such as crop stress and disturbance after extreme weather events.
Existing data have either insufficient spatial resolution or low frequency, the researchers said.
"We struggled to find public satellite data that has both high spatial resolution and high frequency in our own research - it simply did not exist," said natural resources and environmental sciences professor and study co-author Kaiyu Guan.
Guan, a Blue Waters professor at the National Center for Supercomputing Applications at Illinois, teamed up with professor Jian Peng and graduate student Yunan Luo of computer science to develop an algorithm that fuses satellite images from multiple sources into continuous, daily high-resolution images.