The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is widely used as an advanced model dataset for the understanding of global climate change. However, independent validation and comparison of MERRA-2 are both insufficient and always desired. Therefore, in this study, the quantitative evaluation of MERRA-2 aerosol products was conducted over China for Aerosol Optical Depth (AOD), Angstrom exponent (AE), absorbing AOD (AAOD) and chemical components (black carbon (BC) and dust (DU)) using Sun sky radiometer Observation NETwork (SONET) ground-based measurements and POLDER-3/PARASOL satellite products generated by the GRASP algorithm. The available MERRA-2 monthly dataset and PARASOL/GRASP monthly and seasonal products were intercompared over China. MERRA-2 AOD (550 nm) show general good agreement with SONET and PARASOL/GRASP. For example, the correlation coefficients are usually 0.6–0.85 with SONET and 0.75–0.85 with PARASOL/GRASP, the bias is usually −0.293 to +0.008 with SONET. For AE and AAOD, the agreement is still reasonable. MERRA-2 is found to overestimate fine mode AE and to display a general underestimation of aerosol absorption over China. In addition, MERRA-2 BC and DU mass concentrations show spatial and quantitative consistency with PARASOL/GRASP components climatological products. The relatively high columnar BC mass concentration is observed around 1.5–2 mg/m³ over the East China industrial region and high DU mass concentration is around 150 mg/m³ near Taklimakan desert. MERRA-2 shows slightly higher BC and lower DU concentration than PARASOL/GRASP over East China. The evaluations with in situ BC measurements near surface verify the overestimation (MAE = +0.44 µg/m²) of MERRA-2 and underestimation (MAE = −0.38 µg/m²) of PARASOL/GRASP. The analysis demonstrates multi-source datasets, such as ground-based, space-borne remote sensing, in situ measurements, model simulation as well as reanalysis data, complement each other and can be used to refine aerosol characterization.

Single-angle sensors typically utilize radiative transfer simulations based on Lambertian surface, even though the surface reflectance obtained exhibits directional characteristics. Fully accounting for the contribution of surface directional characteristics to the Top of atmosphere (TOA) reflectances can further improve the accuracy of aerosol retrievals. In this study, we propose an enhanced aerosol retrieval algorithm for the Particulate Observing Scanning Polarimeter (POSP), by further considering the surface directional characteristics. Combined with an updated aerosol model, this approach achieves high-accuracy retrievals. We used historical Bidirectional Reflectance Distribution Function (BRDF) products to construct stable surface constraints. By exploring the strong empirical statistical relationships between adjacent blue bands, we have realized the joint inversion of multiple blue bands. Additionally, we employed an optimization algorithm that incorporates boundary constraints, simultaneously accounting for errors in the surface constraint model, and satellite observation errors. The global aerosol optical thickness (AOD) at 550nm over land was retrieved from November 2021 to April 2022. Validation of POSP AOD versus AErosol RObotic NETwork (AERONET) data shows a high consistency, with correlation coefficient (R) of 0.93, root mean square error (RMSE) of 0.086, bias of 0.004, fraction within expected error (EE) of 80.8%, and fraction within Global Climate Observing System (GCOS) of 52.1%. Comparison with MODIS aerosol products shows that the accuracy of POSP AOD is better than that of MODIS AOD. According to the matching results, for DB, R of 0.936/0.907, fraction within EE of 82.2%/74.9% (POSP/MODIS DB); for DT, R of 0.937/0.915, fraction within EE of 83.5%/ 72.0% (POSP/MODIS DT). The spatial distribution difference between POSP AOD and DB AOD is small, indicating good consistency, and POSP AOD captured the intensity of aerosol pollution well. In summary, the enhanced aerosol algorithm achieves reliable high-precision AOD retrieval and because of its generality could also be applied to other sensors.

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.

As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring a high-speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network-based AOD retrieval model (named the AODNet) that takes full advantage of the multispectral measurements of PSAC for AOD retrieval at a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well-trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either the Sun-sky radiometer Observation NETwork (SONET) AOD or the AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85, and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS dark target (DT) and deep blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

GaoFen-5 (02) (GF5-02) is a new Chinese operational satellite that was launched on 7 September 2021. The Directional Polarimetric Camera (DPC) is one of the main payloads and is mainly used for the remote sensing monitoring of atmospheric components such as aerosols and water vapor. At present, the DPC is in the stage of on-orbit testing, and no public DPC precipitable water vapor (PWV) data are available. In this study, a PWV retrieval algorithm based on the spectral characteristics of DPC data is developed. The algorithm consists of three parts: (1) the construction of the lookup table, (2) the calculation of water vapor absorption transmittance (WVAT) in the band at 910 nm, and (3) DPC PWV retrieval. The global PWV results derived from DPC data are spatially continuous, which can illustrate the global distribution of water vapor content well. The validation based on the Aerosol Robotic Network (AERONET) PWV data shows that the DPC PWV data have accuracy similar to that of Moderate-resolution Imaging Spectroradiometer (MODIS) PWV data, with coefficient correlation of determination ($R^2$), mean absolute error (MAE), and relative error (RE) of 0.32, 0.30, and 0.93 using the DPC and 0.23, 0.36, and 0.96 using the MODIS, respectively. The results show that our proposed DPC PWV retrieval algorithm is feasible and has high accuracy. By analyzing the errors, we found that the calibration coefficients of the DPC in the 865 nm and 910 nm bands need to be updated.