## 1 Introduction  
Atmospheric aerosols are multiphase systems consisting of solid and liquid particles and gaseous carriers suspended in the atmosphere with a size of approximately \(10^{-3}\)-\(10^{2}\)\(\upmu\)m [1]. Interactions among aerosols with radiation and clouds, including scattering and absorbing solar radiation and modifying cloud properties and lifetimes, are considered the most significant factors that generate uncertainty in the global climate system [2]. The aerosol optical depth (AOD) is a critical parameter for characterizing the spatiotemporal distribution of aerosols and can generally be acquired using ground-based and satellite observations. Ground-based observations (e.g., Aerosol Robotic Network, AERONET) can provide relatively accurate AOD values and regional analyses at a particular location; however, the sparse and uneven distribution of observation sites is not necessarily adequate for evaluating the continuous spatial variability of aerosols at an appropriate scale for atmospheric applications [3; 4]. Satellite observations have the characteristic of contiguously mapping AOD in space to provide supplementary measurementsfor ground-based observations [5]. Some satellite-derived AOD products (including but not limited to sensors such as the moderate resolution imaging spectroradiometer (MODIS), multi-angle imaging spectro-radiometer (MISR), and visible infrared imaging radiometer suite (VIIRS)) have been widely applied to reveal the diurnal variation law of atmospheric aerosols [6; 7; 8; 9]. However, the aerosol variation is relatively swift and can hardly be captured by the abovementioned polar-orbiting satellites because they pass through the same domain at most twice a day (but mostly two days or more) [10]. With the launch and operation of new-generation meteorological geostationary satellites, their high temporal resolution observation, up to the hour or even minute level, provides the possibility to monitor the spatiotemporal distribution and dynamic variability of aerosols [5; 11; 12]. This contributes to comprehend their mechanisms of formation and dissipation [13; 14; 15; 16; 17] and more precisely assessing their effects in predicting climate change, atmospheric environment, biochemical cycle, and human health [18; 19; 20]. Nevertheless, the current operational AOD products derived from GEO satellite sensors generally suffer from substantial data blanks from the perspective of spatial completeness, probably leading to the misestimation of the real aerosol loading level in scientific applications [21; 22]. Additionally, the data missing in AOD products derived from the GEO satellites are mainly caused by the inability of the passive sensor to retrieve AOD under cloud obscuration and the intrinsic drawback of the retrieval algorithm on high-reflectance surfaces, such as snow-covered regions [23; 24]. Therefore, it is necessary to improve spatial completeness and enhance the availability of AOD observation data retrieved from GEO satellites. According to previous studies, each type of satellite sensor has its own limitations in providing complete aerosol loading information; however, different types of satellite sensors could compensate for each other in terms of coverage and accuracy to a certain extent [25; 26; 27; 28]. For instance, at any observation moment for a GEO satellite sensor, there generally exists AOD data blank in the sun glint region, which hardly occurs for LEO satellite sensors at the same time [29]. Additionally, the active sensor onboard the LEO satellite (e.g., cloud-aerosol lidar with orthogonal polarization, CALIOP) has been proven to assist passive sensors in restoring the real aerosol information under thin clouds and haze conditions to a certain extent [24]. Thus, introducing LEO satellite-based observations possesses huge potential for optimally filling gaps in AOD products derived from GEO satellites.