Modulation transfer function characterization for GOES-16 advanced baseline imager using lunar observations

Truman Wilson; Xiaoxiong Xiong

doi:10.1117/12.2528138

9 September 2019 Modulation transfer function characterization for GOES-16 advanced baseline imager using lunar observations

Truman Wilson, Xiaoxiong Xiong

Author Affiliations +

Proceedings Volume 11127, Earth Observing Systems XXIV; 111271Y (2019) https://doi.org/10.1117/12.2528138
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

GOES-16 is a geostationary satellite with a number of Earth- and Sun-observing instruments. Among these, the primary Earth-imaging instrument is the Advanced Baseline Imager (ABI), which contains 16 spectral bands that range in wavelength from 0.47 um to 13.3 um. In addition to Earth-observations, ABI also performs special data collects which observe the Moon for radiometric calibration validation and spatial characterization performance assessments. In this work, we use lunar images in order to calculate the Modulation Transfer Function (MTF) of the optical systems of GOES-16 ABI for bands 1 − 6. The lunar limb against the dark background of space provides a good data set for performing a modified slanted-edge MTF calculation on-orbit. Using the location of the lunar day/night terminator, we select illuminated regions of the image near the lunar limb in both the North-South and East-West directions to calculate the MTF. For this work, we combine the edge profiles from successive lunar images on the same date in order to maximize the number of edge profiles used in the calculation. We also compare the MTF results for lunar observations throughout the GOES-16 mission to assess the stability of the MTF over time.

Conference Presentation

Citation Download Citation

Truman Wilson and Xiaoxiong Xiong "Modulation transfer function characterization for GOES-16 advanced baseline imager using lunar observations", Proc. SPIE 11127, Earth Observing Systems XXIV, 111271Y (9 September 2019); https://doi.org/10.1117/12.2528138

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