**1. Introduction**

Aerosols, suspended particulate matter in air, act as a crucial factor in global climatic fluctu‐ ations [1]. Aerosols can affect the climate through absorption and scattering of solar radiation [2] and therefore perturb the radiation budget and contribute to radiative forcing [3]. Aerosols may change the size and density of cloud droplets, thus modify the cloud albedo, the cloud lifetime and the precipitation [4]. Aerosols also influence air quality and therefore affect human health [5]. Current uncertainties of aerosols in the Earth radiation budget limit our under‐ standing of the climate system and the potential for global climate change [1]. Satellite observations are needed to understand the distribution and impact of aerosols on regional and global scales [6]. Satellites can monitor some aerosol optical properties, e.g. aerosol optical thickness (AOT) and Angstrom exponent, the key factors for climate change research [7]. In fact, these properties can be retrieved during the atmospheric correction of satellite images.

Ever since Gordon [8] designed an atmospheric correction approach based on the black ocean assumption (BOA) at two near‐infrared (NIR) bands, this approach has been widely applied to process satellite ocean colour remote sensing data. The performance of the approach was improved significantly [9, 10]. However, this approach still faces problems in Case 2 waters [11]. Some other algorithms of atmospheric corrections have been developed especially for the coastal waters. These include the use of the assumption of spatial homogeneity of the NIR band ratio [12], the spectral shape matching methods [13], an iterative fitting algorithm with the bio‐ optical models [14], the BOA method using the short wave infrared (SWIR) bands over turbid waters [15] or the algorithm using the ultraviolet bands [16].

The atmospheric correction over land meets more complicated situations. Similar to the BOA approach for the ocean colour remote sensing, the dark target (DT) approach has been widely used to estimate the optical properties of aerosols over land [17]. Other approaches have been developed using different methods, for example, the invariant object approach by Hall et al. [18], the histogram matching by Richter [19] and the radiative transfer model by Gao et al. [20]. Traditionally, different approaches of the atmospheric correction are necessary for land and ocean to optimize each case. Recently, Mao et al. [21, 22] developed an approach to estimate the aerosol scattering reflectance over turbid waters based on a look‐up table (LUT) of in situ measurements. Following this approach, a unified atmospheric correction (UAC) approach is developed for both land and ocean [23].

Over the last several decades, satellite remote sensing has provided an increasingly detailed view of aerosols and clouds [24] but limited with column‐averaged aerosol properties. Aerosols in the lowest part of the atmosphere are likely to be removed quickly by the rain, those in higher altitudes are much more likely to travel long distances and affect air quality in distant regions. The Cloud‐Aerosol Lidar and infrared pathfinder satellite observation (CALIPSO) satellite provides new capabilities to distinguish aerosol optically thin boundary layer from cloud by considering the vertical thickness and location of the layers as well as from the spectral behaviour of the lidar backscatter [25], useful in studying the interactions between aerosols and clouds with their roles in the climate system.
