The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song Mountain in central China, where ground-based remote sensing measurements of aerosol properties are still very limited. The seasonal variations of AODs are significant in the Song Mountain region, with higher AODs in spring and summer and lower AODs in autumn and winter. Annual mean AODs (0.55 µm) vary in the range of 0.5–0.7, which indicates particle matter (PM) pollutions in this mountain region. Validation against one-year ground-based measurements shows that AOD retrievals from the MODIS onboard Aqua satellite are better than those from the Terra satellite in Song Mountain. The 3 km and 10 km AODs from DT algorithms are comparable over this region, while the AOD accuracy of DB algorithm is relatively lower. However, the spatial coverage of DB products is higher than that of 10 km DT products. Moreover, the optical and microphysical characteristics of aerosols at Song Mountain are analyzed on the basis of SONET observations. It suggests that coarse-mode aerosol particles dominate in spring, and fine-mode particles dominate in summer. The aerosol property models are also established and compared to aerosol types used by MODIS algorithm.

The current understanding of columnar aerosol optical and microphysical properties of different regions and seasons in China is insufficient due to the lack of measurements. Aiming to improve descriptions of aerosol models over China, this paper presents a systematic aerosol characterization of different sites based on a newly developed remote sensing network for aerosol observation, the Sun-sky radiometer Observation NETwork (SONET). One year of ground-based solar and sky radiation measurements of four typical sites of SONET (Beijing–urban-industrial site, Zhangye—rural site, Minqin—desert site, Zhoushan–oceanic site) are used to retrieve aerosol properties using similar inversion algorithms with AErosol RObotic NETwork (AERONET), including aerosol optical depth, Ångström exponent, volume size distribution, complex refractive index, single scattering albedo, and percentage of spherical particles. The retrieved properties among sites and seasons are found to be different in terms of magnitude, spectral dependence, and partition of fine and coarse mode, which can be primarily explained by different aerosol composition and mixing states that closely relate to the local climate, the natural environment, and most importantly, the ubiquitous anthropogenic impacts. For example, large dust particles greatly contribute to the low fine mode fraction in both volume concentration and optical depth for the Minqin site through the entire year, while abundant small particles that mainly come from emission sources dominate the size distribution and light extinction of aerosol in the summer at the Beijing site. The results also show general agreements with other studies on the aerosol properties at each site, however, some unique features are still noticeable, especially at the desert site and oceanic site (e.g., the unusually strong aerosol absorptivity indicated by the large imaginary refractive index and low single scattering albedo at the Minqin and Zhoushan sites), which can be partly attributed to the existence of absorbing particles coming from anthropogenic sources.

The fine-mode fraction (FMF) of aerosol optical depth (AOD) is a key optical parameter that represents the proportion of fine particles relative to total aerosols in the atmosphere. However, in comparison to ground-based measurements, the FMF is still difficult to retrieve from satellite observations, as attempted by a Moderate-resolution Imaging Spectroradiometer (MODIS) algorithm. In this paper, we introduce the retrieval of FMF based on Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) data. This method takes advantage of the coincident multi-angle intensity and polarization measurements from a single satellite platform. In our method, we use intensity measurements to retrieve the total AOD and polarization measurements to retrieve the fine-mode AOD. The FMF is then calculated as the ratio of the retrieved fine-mode AOD to the total AOD. The important processes in our method include the estimation of the surface intensity and polarized reflectance by using two semi-empirical models, and the building of two sets of aerosol retrieval lookup tables for the intensity and polarized measurements via the 6SV radiative transfer code. We apply this method to East Asia, and comparisons of the retrieved FMFs for the Beijing, Xianghe and Seoul_SNU sites with those of the Aerosol Robotic Network (AERONET) ground-based observations produce correlation coefficients (R²) of 0.838, 0.818, and 0.877, respectively. However, the comparison results are relatively poor (R² = 0.537) in low-AOD areas, such as the Osaka site, due to the low signal-to-noise ratio of the satellite observations.

An improved aerosol retrieval algorithm based on the Advanced Multi-angular Polarized Radiometer (AMPR) is presented to illustrate the utility of additional 1640-nm observations for measuring aerosol optical depth (AOD) over land using look-up table approaches. Spectral neutrality of the polarized surface reflectance over visible to short-wavelength infrared bands is verified, and the 1640-nm measurements corrected for atmospheric effects are used to estimate the polarized surface reflectance at shorter wavelengths. The AMPR measurements over the Beijing-Tianjin-Hebei region in north China reveal that the polarized surface reflectance of 670, 865 and 1640 nm are highly correlated with correlation slopes close to one (0.985 and 1.03) when the scattering angle is less than 145°. The 1640-nm measurements are then employed to estimate polarized surface reflectance at shorter wavelengths for each single viewing direction, which are then used to improve the retrieval of AOD over land. The comparison between AMPR retrievals and ground-based Sun-sky radiometer measurements during three experimental flights illustrates that this approach retrieves AOD at 865 nm with uncertainties ranging from 0.01 to 0.06, while AOD varies from 0.05 to 0.17.

The Cimel new technologies allow both daytime and nighttime aerosol optical depth (AOD) measurements. Although the daytime AOD calibration protocols are well established, accurate and simple nighttime calibration is still a challenging task. Standard lunar-Langley and intercomparison calibration methods both require specific conditions in terms of atmospheric stability and site condition. Additionally, the lunar irradiance model also has some known limits on its uncertainty. This paper presents a simple calibration method that transfers the direct-Sun calibration constant, $V_{0,\text{Sun}}$, to the lunar irradiance calibration coefficient, $C_{\text{Moon}}$. Our approach is a pure calculation method, independent of site limits, e.g., Moon phase. The method is also not affected by the lunar irradiance model limitations, which is the largest error source of traditional calibration methods. Besides, this new transfer calibration approach is easy to use in the field since $C_{\text{Moon}}$ can be obtained directly once $V_{0,\text{Sun}}$ is known. Error analysis suggests that the average uncertainty of $C_{\text{Moon}}$ over the 440–1640 nm bands obtained with the transfer method is 2.4%–2.8%, depending on the $V_{0,\text{Sun}}$ approach (Langley or intercomparison), which is comparable with that of lunar-Langley approach, theoretically. In this paper, the Sun–Moon transfer and the Langley methods are compared based on site measurements in Beijing, and the day–night measurement continuity and performance are analyzed.

With the rapid development of the economy and society, fine particulate matter (PM_2.5) has not only caused severe environmental problems, but also posed a threat to public health. In order to improve the estimated accuracy of PM_2.5, the input data fine mode fraction (FMF), a key parameter to the PM_2.5 remote sensing method (PMRS), should be improved due to its significant errors. In this study, we merge the observations of the fine mode fraction (FMF) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Aerosol Robotic Network (AERONET) and the Sun-sky radiometer Observation Network (SONET) using the universal kriging (UK) method to obtain accurate FMF distribution over eastern China. PM_2.5 mass concentration is estimated by the fusion and MODIS FMF distributions using the PMRS model. The results show that the parameters in the variogram are relatively stable except for significant differences in correlation lengths in summer. The FMF in the Winter of 2015 shows that the mean error decreases from 0.38 to 0.13 compared with that from MODIS using leave-one-out cross-validation, with the maximum error decreasing from 0.75 to 0.34, indicating that the UK method can provide better estimates of FMF. We also find that PM_2.5 estimated from FMF fusion results is closer to the in situ PM_2.5 from the Ministry of Environmental Protection (MEP) (87.2 vs. 88.9 μg/m³).