Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas absorption to derive an accurate surface reflectance that can be compared across time and space. In practice, routine correction procedures are often customized for various platforms without fully reporting or checking the errors systematically in the atmospheric correction. Such errors can have significant effects on downstream analyses such as vegetation indices or trait retrievals, and not all subsequent analyses are equally affected by the accuracy of reflectance retrievals. In this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference. Our results revealed that the individual correction methods had different effects on the reflectance retrievals that impacted downstream measurements. Including spectral measurements from ground vegetation targets in addition to painted calibration targets improved the performance of the ELC method. The hybrid method yielded reflectance spectra that most closely matched the spectra of the ground validation data. The errors in vegetation indices differed with the methods, and certain indices (such as PRI) were more affected than indices that rely on stable, broader spectral features (e.g., NDVI). Plant pigment retrievals via partial least squares regression were less sensitive to errors in atmospheric correction. These findings demonstrate that obtaining high-quality, field spectral measurements over well-characterized calibration targets and representative land cover types within the scene is critical for accurate surface reflectance and subsequent downstream products, such as vegetation indices or plant traits.

高光谱机载图像可以提供植物生理和结构特性的丰富信息，其尺度介于近端和卫星遥感之间，在评估生态系统功能和生物多样性方面具有广泛的应用。机载高光谱数据的一个关键处理步骤是对路径辐射、气溶胶效应和气体吸收进行补偿的大气校正，以获得可以跨时间和空间进行比较的准确表面反射率。在实践中，经常为各种平台定制常规校正程序，而没有完全报告或系统检查大气校正中的错误。此类错误会对下游分析（例如植被指数或性状检索）产生重大影响，并非所有后续分析都同样受到反射率检索准确性的影响。在这项研究中，我们检查了三种大气校正方法的误差，包括辐射传输模型 (RTM)、经验线校正 (ELC) 和通过贝叶斯推理将两者的元素相结合的混合方法。我们的结果表明，各个校正方法对影响下游测量的反射率反演有不同的影响。除了绘制的校准目标外，还包括来自地面植被目标的光谱测量，提高了 ELC 方法的性能。混合方法产生的反射光谱与地面验证数据的光谱最接近。植被指数的误差因方法而异，某些指数（如 PRI）比依赖于稳定、更广泛光谱特征的指数（如 NDVI）受到的影响更大。通过偏最小二乘回归进行的植物色素检索对大气校正中的错误不太敏感。这些发现表明，在场景中表征良好的校准目标和代表性土地覆盖类型上获得高质量的现场光谱测量对于准确的表面反射率和后续的下游产品（例如植被指数或植物性状）至关重要。