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Propagation of spectral characterization errors of imaging spectrometers at Level-1 and its correction within a Level-2 recalibration scheme

机译:1级成像光谱仪的光谱表征误差的传播及其在2级重新校准方案中的校正

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The uncertainties in the knowledge of the Instrument Spectral Response Function (ISRF), barycenter of the spectral channels and bandwidth / spectral sampling (spectral resolution) are important error sources in the processing of satellite imaging spectrometers within narrow atmospheric absorption bands. The exhaustive laboratory spectral characterization is a costly engineering process that differs from the instrument configuration in-flight given the harsh space environment and harmful launching phase. The retrieval schemes at Level-2 commonly assume a Gaussian ISRF, leading to uncorrected spectral stray-light effects and wrong characterization and correction of the spectral shift and smile. These effects produce inaccurate atmospherically corrected data and are propagated to the final Level-2 mission products. Within ESA's FLEX satellite mission activities, the impact of the ISRF knowledge error and spectral calibration at Level-1 products and its propagation to Level-2 retrieved chlorophyll fluorescence has been analyzed. A spectral recalibration scheme has been implemented at Level-2 reducing the errors in Level-1 products below the 10% error in retrieved fluorescence within the oxygen absorption bands enhancing the quality of the retrieved products. The work presented here shows how the minimization of the spectral calibration errors requires an effort both for the laboratory characterization and for the implementation of specific algorithms at Level-2.
机译:仪器光谱响应函数(ISRF)知识,光谱通道的重心以及带宽/光谱采样(光谱分辨率)知识的不确定性是在狭窄的大气吸收带内处理卫星成像光谱仪时的重要误差源。考虑到恶劣的太空环境和有害的发射阶段,详尽的实验室光谱表征是一项昂贵的工程过程,不同于飞行中的仪器配置。级别2的检索方案通常采用高斯ISRF,从而导致未校正的光谱杂散光效应,错误的表征以及光谱偏移和笑容的校正。这些影响会产生不正确的大气校正数据,并传播到最终的2级任务产品。在ESA的FLEX卫星任务活动中,分析了ISRF知识错误和1级产品光谱校准及其对2级检索到的叶绿素荧光的传播的影响。在Level-2实施了光谱重新校准方案,将Level-1产品的误差降低到氧气吸收带内回收荧光的10%误差以下,从而提高了回收产品的质量。此处介绍的工作表明,如何最小化频谱校准误差既需要实验室表征又需要在Level-2上实施特定算法。

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