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On-Orbit Spectral Calibration Method of Grating Dispersive Imaging Spectrometer |
WANG Hong-bo1,2,HUANG Xiao-xian1,FANG Chen-yan1,2,ZHANG Teng-fei1,2,WEI Jun1 |
1. Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China
2. University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Precise spectral calibration is the premise and base for quantitative radiance inversion of Earth scenes. The grating dispersive visible near-infrared imaging spectrometer (VNS) is used for ocean color remote sensing and coastal zones monitoring. A push-broom method is applied by this instrument. It is operated in the solar-reflected spectrum with wavelength range from 400 to 1 040 nm. 256 spectral channels with a nominal 2.5 nm interval and 1024 cross-track pixels, corresponding to spectral and spatial dimensions, are arranged on the focal plane. Spectral parameters including the center wavelength and bandwidth of the hyperspectral instrument may vary after launching, due to external environmental changes or self-performance degenerations. For the sake of coefficients update, an on-orbit spectral calibration method is presented in this contribution. The algorithm is based on a spectrum-matching technique using atmospheric absorption features, solar Fraunhofer lines and Pr-Nd characteristic spectra of the on-board calibrator. Last squares and correlation coefficients are applied to process the data collected in the on-orbit spectral calibration simulation experiments. The procedure is introduced by taking an example of the oxygen absorption 763 nm band. Fraunhofer lines 517 nm, Pr-Nd glass characteristic spectra 685 nm and oxygen absorption 763 nm are selected as three typical bands, corresponding to three channels of the visible near-infrared imaging spectrometer (VNS). Their spectral recalibration results are reported as follows. Cross-track smile effect amplitudes are similar, about 0.6 nm while different center wavelength shifts, 0.707, -0.369 and 0.293 nm respectively. The standard deviation of the channel of 763 nm is smaller than the other two, deriving from second order polynomial fits to measurements across-track, and spectral position precisions of the three channels are better than 0.176 nm. A practical on-orbit spectral calibration algorithm is proposed for the imaging spectrometer.
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Received: 2015-09-13
Accepted: 2016-04-29
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[1] MA Liang, WEI Jun, HUANG Xiao-xian, et al(马 亮, 危 峻, 黄小仙,等). Laser & Optoelectronics Progress(激光与光电子学进展), 2013,(2): 198.
[2] Green R O. Applied Optics, 1998, 37(4): 683.
[3] Ramon D, Santer R P, Dubuisson P. Proc. SPIE, 2003, 4891: 505.
[4] Uchikata T, Tanaka K, Okamura Y, et al. 2014, 9264: 92640Q.
[5] Xiong X, Chiang K, Esposito J, et al. Metrologia, 2003, 40(1): S89.
[6] Neville R A, Sun L, Staenz K. Proc. SPIE, 2003, 5093: 144.
[7] Barry P, Shepanski J, Segal C. Proc. SPIE, 2002, 4480: 231.
[8] Gao B C, Montes M J, Davis C O. Remote Sensing of Environment, 2004, 90(4): 424.
[9] Guanter L, Richter R, Moreno J. Applied Optics, 2006, 45(10): 2360.
[10] Guanter L, Segl K, Sang B, et al. Optics Express, 2009, 17(14): 11594.
[11] LI Zhan-feng, WANG Shu-rong, HUANG Yu, et al(李占峰, 王淑荣, 黄 煜,等). Acta Optica Sinica(光学学报), 2013,(2): 0228002.
[12] Wang Hongbo, Huang Xiaoxian, Ma Liang, et al. Proc. SPIE, 2014, 9261: 92611O. |
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