中温黑体是红外谱段常用的高精度辐射标尺设备, 而近红外是处于其有效辐射范围边缘的谱段, 所以该谱段的定标研究相对较少。 研究了基于中温黑体的近红外光纤光谱仪辐射定标方法, 主旨是探讨定标精度如何受定标模型的结构参数选择的影响, 进而为近红外光谱辐射计量溯源提供技术参考。 采用50～1 050 ℃的可调中温黑体对近红外光纤光谱仪(950～1 700 nm)进行辐射定标。 针对定标的关键环节重点讨论了两个内容, 首先是辐射传输模型的几何因子匹配问题, 比较分析了传统的双圆盘辐射传输模型和光纤直接耦合模型。 对于光纤光谱仪的辐射定标来讲, 采用光纤直接耦合形式的辐射传输模型, 结构上更简单, 耦合效率更高。 其次重点分析了辐射定标中模型的结构参数对定标精度的影响, 其影响的原因是定标数据本身属性中的尺度结构特征, 即通常所说的非线性问题。 因此对于定标精度要求较高时, 需要采用非线性定标模型进行校正, 并尽可能保证测试点采样的尺度均衡, 这是小样本数据解释非线性结构关系时无法回避的样本选择问题。 数据分析结果表明, 定标方程的不同结构参数的选择策略对定标精度有显著影响, 校正方程的样本残差标准差带变化范围为±0.1%～±1%。

The medium temperature black body (MTBB) is conventional high precision equipment used as spectral radiometric scale in infrared spectral region. However, in near-infrared (NIR) spectral region, there are few papers about spectral radiometric calibration by using MTBB, that is because NIR spectral region is the borderland of its effective spectral region. The main research of this paper is spectral radiometric calibration method by using MTBB in NIR spectral region. Accordingly, this paper is devoted mostly to a discussion of how the calibration precision could be affected by selecting different structural parameters of calibration model. The purpose of this paper is to present the results of research and provide technical reference for improving the traceability in NIR spectral radiometric calibration. In this paper, a NIR fiber coupled spectrometer, whose wavelength range covers from 950 to 1 700 nm, has been calibrated by a MTBB with adjustable temperature range from 50 to 1 050 ℃. Concentrating on calibration process, two key points have been discussed. For one thing, the geometric factors of radiation transfer model of the calibration systems have been compared between traditional structure and fiber direct-coupled structure. Because the fiber direct-coupled model is simple and effective, it has been selected instead of traditional model based on the radiation transfer between two coaxial discs. So, it is an advantaged radiation transfer model for radiometric calibration of fiber coupled spectrometer. For another thing, the relation between calibration accuracy and structural parameters of calibration model has been analyzed intensively. The root cause is scale feature of attribute of calibration data itself, which is the nonlinear structure in scales of spectral data. So, the high precision calibration needs nonlinear calibration model, and the uniform sampling for scale feature is also very important. Selecting sample is an inevitable problem when the nonlinear model is explained by small sample dataset. As the analytic results, there are obviously influences for the calibration precision among different strategies of selecting model’s structural parameters. The calibration precision, which is mathematical described by standard deviation of spectral data for calibration, could be from ±0.1% to ±1%.