Combining parallel data from separate satellites can be like trying to make a peanut butter and jelly sandwich.
For the sandwich, you want rich and sweet flavors, blended into a smooth, creamy texture and you want it all in one convenient package. That's similar to how you want the satellite data, and Bo Yang, a University of Cincinnati graduate student in geography, has a formula for crafting a deeply informative and easily utilized satellite sandwich.
He'll present his research, "Spatiotemporal Cokriging Images Fusion of Multi-Sensor Land Surface Temperature over Thaw Lakes on North Alaska," at the Association of American Geographers annual meeting to be held April 9-13 in Los Angeles. The interdisciplinary forum is attended by more than 7,000 scientists from around the world and features an array of geography-related presentations, workshops and field trips.
For his master's thesis, Yang studied thermal data from two different types of polar-orbiting satellite systems. One system frequently records large images of a region on Earth but in little detail. Another system records small images less frequently but in much greater detail. Analyzing two massive sets of parallel data and finding a way to make them overlap can be complicated and time-consuming. Yang is developing a method to simplify the process.
"In an easy-to-understand way, I am trying to derive both very high-definition and high-frequency revisiting imagery from two satellite-carried sensors," Yang says. "I use the spatial statistics technique known as co-kriging to fuse multi-sensor land surface temperature images."
Yang uses an algorithm he devised to fill the spatiotemporal gaps between the two data sets. The result is an intricately detailed map covering a large surface area that allows geographers to quickly derive daily even hourly surface temperature and emissivity information. These environmental parameters are important to agriculture and
|Contact: Tom Robinette|
University of Cincinnati