Aerosol properties, including aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), single scattering albedo (SSA), and fine mode fraction (FMF), are essential in studying aerosol climate effects. Spectral multiangle polarimetry (MAP) has been recognized as a promising technique for comprehensive retrievals of global aerosol optical properties from space. As one of the very few MAP sensors in space, the Directional Polarimetric Camera (DPC) onboard the GaoFen (GF)-5 satellite has great potential to provide these critical aerosol parameters. However, retrievals of aerosol parameters from DPC, especially SSA and AAOD, still remain limited. This study introduces a machine-learning algorithm using the eXtreme gradient boosting (XGBoost) model to retrieve AOD, AAOD, SSA, FMF, as well as surface albedo (expressed as the directional hemispherical reflectance, DHR) over land from DPC multiangle reflectances and degree of linear polarization (DOLP), using AERONET aerosol measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) DHR data as the training target. Cross-validation indicates high retrieval accuracy, with correlations exceeding 0.75 for all parameters under sufficient aerosol loading. Notably, the accuracy of SSA retrieval is comparable to that of the Polarization and Directionality of the Earth’s Reflectance (POLDER) products, with 73% of the independently retrieved 670-nm SSA falling within the ±0.03 error envelope (EE) when 670-nm AOD is above 0.30. Gridded products also effectively capture the spatial and seasonal variability of aerosol properties worldwide, such as in regions dominated by biomass burning and dust. This study confirms the capability of DPC for aerosol property retrievals, which could serve as an important technique and data source for global aerosol and climate monitoring.

中文翻译：

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基于机器学习的高分五号定向偏振相机测量数据反演陆地上的气溶胶和表面特性


气溶胶特性，包括气溶胶光学深度 (AOD)、气溶胶吸收光学深度 (AAOD)、单散射反照率 (SSA) 和精细模式分数 (FMF)，对于研究气溶胶气候影响至关重要。光谱多角度偏振测量（MAP）已被认为是一种从太空全面反演全球气溶胶光学特性的有前景的技术。作为太空中极少数的 MAP 传感器之一，高分 (GF)-5 卫星上搭载的定向偏振相机 (DPC) 在提供这些关键气溶胶参数方面具有巨大潜力。然而，从 DPC 中检索气溶胶参数，尤其是 SSA 和 AAOD 仍然有限。本研究介绍了一种机器学习算法，使用 eXtreme 梯度增强 (XGBoost) 模型从 DPC 多角度反射率和度数检索 AOD、AAOD、SSA、FMF 以及陆地表面反照率（表示为定向半球反射率，DHR）线性偏振（DOLP），使用AERONET气溶胶测量和中分辨率成像光谱仪（MODIS）DHR数据作为训练目标。交叉验证表明反演精度很高，在足够的气溶胶负荷下，所有参数的相关性超过 0.75。值得注意的是，SSA 反演的精度与地球反射的偏振和方向性 (POLDER) 产品相当，独立反演的 670 nm SSA 中 73% 在 670 nm 时落在 ±0.03 误差包络 (EE) 范围内AOD在0.30以上。网格产品还可以有效捕获全球气溶胶特性的空间和季节变化，例如在以生物质燃烧和灰尘为主的地区。 这项研究证实了DPC的气溶胶性质反演能力，可以作为全球气溶胶和气候监测的重要技术和数据源。