Prediction of S-NPP VIIRS DNB gains and dark offsets

Chengbo Sun; Thomas Schwarting; Hongda Chen; Kwofu Chiang; Xiaoxiong Xiong

doi:10.1117/12.2274118

5 September 2017 Prediction of S-NPP VIIRS DNB gains and dark offsets

Chengbo Sun, Thomas Schwarting, Hongda Chen, Kwofu Chiang, Xiaoxiong Xiong

Proceedings Volume 10402, Earth Observing Systems XXII; 104021U (2017) https://doi.org/10.1117/12.2274118
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

We describe the methodology for predicting the S-NPP VIIRS Day-Night-Band (DNB) detector gains and dark offsets. During the first 5 years of operation, the DNB has shown recognizable patterns in these calibration parameters. These patterns can be decomposed into two distinctive components: degradation and oscillation. We fit the historical data using a periodic function of time superimposed on an exponential function of time to capture both sources of the variation. The results of the fit showed good agreement with the measured data, indicating that the functions may be useful as a forward model for predicting these calibration parameters for calibration updates. As a test, predictions made in April, 2016 were examined against newly obtained measurement data at monthly intervals. Through April, 2017, the prediction errors have been smaller than 1.5% in the gains and 0.5% in the offsets, with the largest errors observed in the end-of-scan aggregation modes of the high-gain stage. The oscillatory features seen in the measured gains will be analyzed to isolate possible causes and to determine the relevance of its inclusion in the model. Comparisons with the results using the existing predictions of the gain and offset Look-Up-Tables (LUTs) will also be presented.

Conference Presentation

Citation Download Citation

Chengbo Sun, Thomas Schwarting, Hongda Chen, Kwofu Chiang, and Xiaoxiong Xiong "Prediction of S-NPP VIIRS DNB gains and dark offsets", Proc. SPIE 10402, Earth Observing Systems XXII, 104021U (5 September 2017); https://doi.org/10.1117/12.2274118

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