This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TASO has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries; constraints of surface reflectance and aerosol model parameters are obtained respectively from k-means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) from GEO-TASO measured spectra until state vector converges. The comparison of AODs between GEO-TASO retrievals (y) and 7 AERONET (x) sites reveals that the iterative updates of surface PCs (and so surface reflectance) improve the inversions of fine-mode AOD, fine-mode fraction of AOD, Ångström exponent, and AOD at all (440, 550, 550, 675 nm) wavelengths. At 440 nm, the linear fitting equation, the Pearson correlation coefficient (R²), and mean absolute error are improved respectively from y = 0.72x + 0.11, 0.53, and 0.05 (without update of PCs) to y = 1.055x + 0.01, 0.76, and 0.033. Future work is to prepare the algorithm for TEMPO that carries an enhanced version of GEO-TASO instrument.

本文介绍了一系列研究的第三部分，以开发可同时从GEOstationary微量气体和气溶胶传感器优化（GEO-TASO）仪器中检索气溶胶参数和表面光谱反射率的算法。由于该算法是为未来的高光谱和对地静止卫星传感器（例如“对流层排放：污染监测”（TEMPO））设计的，因此该算法适用于作为韩美航空一部分通过不同航班在同一地区测量的GEO-TASO数据在2016年进行了质量研究（KORUS-AQ）野外活动。尽管GEO-TASO在可见光中的光谱采样间隔为〜0.28 nm，但考虑到大气透射率和不同表面类型，通过波段选择方法来精简其数据，这产生了该算法将使用20个公共频谱带。该算法以从各种光谱库中提取的4个通用主成分（PC）开始，以获取表面光谱反射率。分别从瑞利校正的GEO-TASO光谱和AERONET数据的k均值聚类分析中获得了表面反射率和气溶胶模型参数的约束。然后，该算法使用最佳估计方法进行迭代，以更新PC并从GEO-TASO测量的光谱中检索气溶胶光学深度（AOD），直到状态向量收敛为止。GEO-TASO检索之间AOD的比较（分别从瑞利校正的GEO-TASO光谱和AERONET数据的k均值聚类分析中获得了表面反射率和气溶胶模型参数的约束。然后，该算法使用最佳估计方法迭代进行，以更新PC并从GEO-TASO测量的光谱中检索气溶胶光学深度（AOD），直到状态向量收敛为止。GEO-TASO检索之间AOD的比较（分别从瑞利校正的GEO-TASO光谱和AERONET数据的k均值聚类分析中获得了表面反射率和气溶胶模型参数的约束。然后，该算法使用最佳估计方法进行迭代，以更新PC并从GEO-TASO测量的光谱中检索气溶胶光学深度（AOD），直到状态向量收敛为止。GEO-TASO检索之间AOD的比较（y）和7个AERONET（x）站点表明，表面PC的迭代更新（以及表面反射率）可改善精细模式AOD，精细模式AOD分数，Ångström指数和AOD的反演（440、550） ，550、675 nm）的波长。在440 nm处，线性拟合方程，Pearson相关系数（R ²）和平均绝对误差分别从y  = 0.72 x  + 0.11、0.53和0.05（无需更新PC）提高到y  = 1.055 x  + 0.01 ，0.76和0.033。未来的工作是为TEMPO准备算法，该算法带有GEO-TASO仪器的增强版本。