Algorithm overcomes compromise in acquisition of satellite imagery

Now, researchers at the University of Illinois have developed a new algorithm that is said to eliminate this compromise by fusing high-resolution and high-frequency satellite data into one integrated product, and can generate 30m daily continuous images going back to the year 2000.

Monitoring of agriculture, urban development, environmental quality and economic development are just a few ways people make use of satellite data. The new algorithm is claimed to be generic enough for use in virtually any application.

In agricultural applications, imaging at 10- to 30m 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. Farmers usually require information with both high-resolution and near real-time occurrence.

"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. "So we took the initiative to produce it ourselves."