Key research themes
1. How can instrumental calibration and radiometric accuracy be improved using absolutely calibrated infrared stellar spectra?
Infrared (IR) calibration of sensors across ground-based to airborne and satellite platforms critically depends on accurately characterized stellar sources as radiometric standards. Generating absolutely calibrated continuous stellar spectra spanning near- to far-infrared bands enables cross-instrument calibration consistency and establishes a foundation for traceable spectral irradiance standards. This research theme investigates methodologies to construct, combine, and validate composite stellar spectra across disparate observational datasets and instruments, addressing correction factors, systematic uncertainties, and spectral stitching techniques.
2. What are the advances in instrumentation and methodological approaches for high-resolution infrared spectral measurements and their applications to atmospheric and astronomical studies?
High-resolution infrared spectroscopy, especially covering mid- and far-infrared regions, critically informs atmospheric composition, climate research, and astrophysical source characterization. Advances include development of novel spectrometers with high throughput, mobile and airborne observation systems to complement forthcoming space missions, and novel algorithms for radiative transfer and spectral retrieval that account for line-by-line physics. This theme encompasses instrumental design, calibration validation, data inversion techniques, and computational simulation codes facilitating the analysis of Earth's radiation budget, planetary atmospheres, and molecular detections in astrophysical environments.
3. How can spectral and chemometric methods advance non-destructive infrared spectroscopic analysis and traceability in materials and agricultural products?
This theme covers methodological advances in applying infrared spectroscopy combined with chemometrics for non-destructive analysis of biological tissues, food aging, chemical identification of agricultural products, and quality control. Work includes optimization of spectral preprocessing, feature extraction, spectral data fusion across multiple sources, machine learning classification, and relationship with molecular composition and physical properties. The goal is to develop rapid, accurate, and non-invasive techniques for quality, origin traceability, and process monitoring.