Proper calibration of airborne hyperspectral imagery is essential for maximizing the quantitative utility of remotely-sensed imagery, especially when distinguishing subtle changes in spectral curves related to specific plant physiological properties (e.g. chlorophyll and water). Many studies use the empirical line approach with reference reflectance taken from dark and bright targets to calibrate airborne images. However, few have paid attention to the issue of sensor oversaturation due to the exposure setting of the imaging sensor, and no studies have investigated the effects this has placed on image calibration. With limited radiometric resolution, a sensor would become saturated by energy reflected from bright targets when its exposure is set to maximize signals reflected from a feature of interest, for example vegetation. This would result in large bias in the reflectance calibration process, and should be addressed for enormous amounts of high spatial and spectral resolution data that have been increasingly taken by manned or unmanned aircraft. In this study, we test the exposure setting of a hyperspectral sensor for maximizing vegetation signal and investigate potential reference targets in an airborne scene, and propose a more suitable airborne hyperspectral imaging and an empirical line atmospheric correction procedure by taking into account: 1) imaging sensor exposure setting, 2) spectral extrapolation, 3) sensor saturation of targets’ signal, and 4) optimal materials and grey levels for field reference reflectance for the empirical line method. The imaging experiment was conducted over a grassland field with the Micro-Hyperspec VNIR sensor. Using field hyperspectral data to validate the calibration results, we found that our proposed empirical line calibration approach improved the reflectance accuracy significantly. Vegetation indices calculated from the calibrated spectra were able to estimate chlorophyll content with success. Our work offers insights into image calibration and describes a feasible method to maximize quantitative utilities of airborne Hyperspectral imagery for vegetation studies.

机载高光谱图像的正确校准对于最大化遥感图像的定量效用至关重要，尤其是在区分与特定植物生理特性（例如叶绿素和水）有关的光谱曲线的细微变化时。许多研究使用经验线方法，并结合了从暗物和亮物获得的参考反射率来校准机载图像。但是，由于成像传感器的曝光设置，很少有人关注传感器过饱和的问题，并且还没有研究调查这对图像校准的影响。在有限的辐射分辨率下，将传感器的曝光设置为最大化从感兴趣的特征（例如植被）反射的信号时，传感器将被明亮目标反射的能量饱和。这将导致反射率校准过程中出现较大偏差，因此应针对有人或无人飞机越来越多地获取的大量高空间和光谱分辨率数据进行处理。在这项研究中，我们测试了用于最大化植被信号的高光谱传感器的曝光设置，并研究了空中场景中的潜在参考目标，并通过考虑以下因素提出了一种更合适的空中高光谱成像和经验线大气校正程序：1）成像传感器曝光设置，2）光谱外推，3）目标信号的传感器饱和度和4）经验线法的场参考反射率的最佳材料和灰度级。使用Micro-Hyperspec VNIR传感器在草地上进行了成像实验。使用现场高光谱数据验证校准结果，我们发现我们提出的经验线校准方法显着提高了反射率精度。由校准光谱计算得出的植被指数能够成功估算出叶绿素含量。我们的工作提供了对图像校准的见解，并描述了一种可行的方法，可最大化机载高光谱图像在植被研究中的定量效用。