Paper
7 September 2018 Radiometric quality assessment of GOES-16 ABI L1b images
Author Affiliations +
Abstract
The Advanced Baseline Imager (ABI) onboard NOAA’s GOES-16 satellite has been operational as GOES-East since December 18th, 2017. It is a multi-channel passive imaging radiometer with 16 spectral bands covering the visible, near infrared and infrared (IR) spectra, to captured variable area imagery and radiometric information of the Earth’s surface, atmosphere and cloud cover. The Level 1B (L1b) radiance images of these channels are geometrically and radiometrically corrected to provide high quality input data to the user communities. Three series of tests are undertaken to validate the product maturity levels: Post-launch Test (PLT), Post-launch Product Test (PLPT) and Extended Validation (EV). Engineering-focused metrics reflecting the radiometric quality of ABI L1b radiance image are assessed in these tests, such as signal-to-noise ratio (SNR)/noise-equivalent-differential temperature (NEdT), background coherent noise pattern, detector dynamic range, detector linearity, etc. Direct Earth view image analysis using image processing tool such as Fourier transform can also reveal information about its quality. In this presentation, initial results of selected PLPTs undertaken by GOES-R Calibration Working Group (CWG) are provided with the focus for IR bands. The results show that the general criterion for product maturity have been largely met. Occasional artifacts still existing at smaller scale are reported. There has been continuous effort to monitor, analyze and resolve these artifacts to further improve the L1b image quality.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhipeng Wang, Xiangqian Wu, Haifeng Qian, Fangfang Yu, Robert Iacovazzi, Xi Shao, Vladimir Kondratovich, and Hyelim Yoo "Radiometric quality assessment of GOES-16 ABI L1b images", Proc. SPIE 10764, Earth Observing Systems XXIII, 107641T (7 September 2018); https://doi.org/10.1117/12.2322896
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Cited by 2 scholarly publications.
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KEYWORDS
Sensors

Calibration

Signal to noise ratio

Clouds

Infrared imaging

Satellites

Image quality

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