areas and less hot, smoldering sections of land. CTI’s observations of severe fires in Australia, the Amazon, India, and the U.S. detected the shape and location of fire fronts while also providing information on their distance from settled areas. For first responders, this data could influence life-saving evacuation plans. Furthermore, CTI made fire observations with 20 times more detail than NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) and with 190 times more detail than NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). A fleet of CTI-like sensors could capture detailed measurements of wildfires several times a day, filling current gaps in coverage.
Image credit: NASA’s Earth Observatory
cuttingedge • goddard’s emerging technologies
Volume 17 • Issue 4 • Summer 2021
CTI captured several images of the unusually severe fires in Australia that burned for four months in 2019-20. With its 80-meter (260 foot) per pixel resolution, CTI detected the shape and location of fire fronts and how far they were from settled areas — information critically important to first responders. Scientists have generally relied upon coarser resolution (375–1000 m) thermal data from the satellitebased Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors to monitor fire activity from above.
CTI’s technology pathfinder mission leveraged the collaborative efforts of multiple Goddard directorates as well as miniature integrated detector cooler assembly development by New Hampshire-based QmagiQ LLC, funded through NASA’s SBIR program. ESTO supported and funded CTI instrument development under the Sustainable Land Imaging Technology program. ESTO has funded a follow-on instrument, CTI-
2, which is currently in development. CTI-2 will incorporate optical filters directly attached to an SLS detector that provides multi-spectral data. Two additional internal Goddard programs are supporting the development of detectors and instruments with different filters attached to the SLS detector assembly. v CONTACT Murzy.D.Jhabvala@nasa.gov or 301-286- 5232
Early Career Innovator: Bethany Theiling Finds Adaptation is Key Two years ago, Dr. Bethany Theiling arrived at Goddard with a horde of data, an open mind, and an inclination to make new connections. Only hours into her first day at Goddard, a chance meeting directed her path towards new lines of investigation. Theiling recalled her first moments at Goddard’s orientation, striking up a conversation with Brian Powell, who happened to be a machine learning expert. Theiling discussed a curiosity in machine learning for small laboratory data sets, and Powell took an interest in her work. “I will pretty much talk to anybody. You never know what’s going to happen, right? I mean, if it doesn’t work
out, it doesn’t work out, but it could be a great friendship or a partnership or anything,” Theiling said. An early career innovator, Theiling now leads two machine learning projects focused on the chemical analysis of ocean worlds. Theiling is currently using machine learning to determine the composition of an ocean world, using algorithms that target what she has termed “predictive features”, which are defining characteristics of data from a specific chemical system that makes the data look the way they do. Using the data brought with her from her professorship at the University of Tulsa, as well as a vast amount of data from the National Science Foundation, Theiling and Continued on page 7
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