Based on the optimal estimation (OE) theory and information content analysis method, we discuss the ability to include the multi-angle satellite ultraviolet polarization channel to retrieve the aerosol layer height (ALH) for ten typical aerosol types in the China region. We also quantitatively evaluate the effects of polarization measurements and the number of viewing angles on ALH retrieval under different conditions (aerosol model, aerosol optical depth, etc.). By comparing the different degree of freedom for signal (DFS) results of ALH caused by the theoretical retrieval error changes in different microphysical parameters in the aerosol and surface model, we identify the key factors affecting ALH retrieval. The results show that the extended ultraviolet band provides important information for ALH retrieval and is closely related to the scattering and absorption characteristics of aerosol models. The polarization measurements in fine mode reduce the posterior error of ALH retrieval by 6–39%; however, this is relatively small for coarse mode. In particular, when it is applied to the transported dust and background dust aerosol types, the posterior error is only reduced by 1–8% after adding polarization measurements. For these two aerosol types with weak absorption at the ultraviolet band, increasing the number of angles observed in addition to increasing the polarization channel will more effectively improve ALH inversion. Compared with other aerosol and surface model parameters, the retrieval errors for the total volume column, effective variance, real part of the complex refractive index, and surface reflectance are the main factors affecting ALH retrieval. Therefore, reducing the theoretical retrieval error of these parameters will be helpful.

Satellite multi-angle polarimetric (MAP) observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols. Recent advancements in multi-angle, multispectral, and polarized satellite observations have increased data content and complexity. While traditional methods like look-up tables and optimal estimation face challenges in fully utilizing these advanced datasets, deep learning approaches offer substantial advantages. However, deep learning models also have limitations, particularly regarding physical interpretability and the efficiency of processing high-dimensional observational data. To address these challenges, we propose MAP_CapsNet, a deep learning algorithm based on Capsule Networks (CapsNets) for aerosol multi-parameter retrieval. This algorithm combines the multi-dimensional modeling capabilities of CapsNets with vector radiative transfer models to retrieve aerosol optical and microphysical parameters. We applied it to MAP measurements from the Directional Polarimetric Camera (DPC) onboard the Gaofen-5(02) satellite to retrieve different aerosol parameters over China in 2022. The results were validated against Aerosol Robotic Network and Sun/sky-radiometer Observation Network data. The correlation coefficients ( R ) for aerosol optical depth and fine mode fraction exceed 0.935 and 0.782, respectively. The single scattering albedo also showed a moderate correlation ( R = 0.691). Compared with Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite products, the DPC exhibited good spatial consistency and an enhanced ability to characterize aerosol properties due to higher spatial resolution and MAP capability. These findings highlight the DPC instrument’s potential for high-resolution, real-time monitoring of dust and haze pollution events.

Accurately estimating land surface reflectance (LSR) is critical for separating reflected solar irradiance from the total surface energy in the mid-infrared (MIR) spectral region. Existing approaches for MIR LSR retrieval typically rely on synergistic observations from multiple MIR and thermal infrared channels. However, in the 3–5 μm spectral range, many sensors are equipped with only a single high-transmittance channel for Earth observation. To address this limitation, this study proposes an innovative physically based single-channel retrieval algorithm for LSR, utilizing MIR data acquired by the Advanced Geostationary Radiation Imager (AGRI) onboard China’s next-generation geostationary meteorological satellite Fengyun-4B (FY-4B). Considering different solar zenith angles (SZAs), a simulation dataset was generated using MODTRAN 5.2 radiative transfer model through iterative combinations of reasonable atmospheric and surface parameters, in order to determine the coefficients of the proposed algorithm. In practical retrieval, the algorithm coefficients are only related to the SZA and land surface temperature (LST). A detailed sensitivity analysis found that the errors from atmospheric parameters and instrument noise are almost below the 10^-4 order of magnitude, and adding a 1 K uncertainty in LST can introduce a maximum LSR retrieval error of 4×10^-3. Therefore, the proposed algorithm can be considered to have good robustness. The accuracy of MIR LSR is validated in two ways in this study. First, the LSR is directly validated using a simulation dataset generated by MODTRAN, with a root mean square error (RMSE) of < 1.6×10^-3 and a bias within the range of ±2×10^-4. Second, LSR is indirectly validated using the MODIS emissivity product MYD11C1 and AGRI LST product, with an RMSE of < 4.7×10^-3 and a maximum bias of −1.2×10^-3. Overall, the proposed method can accurately retrieve LSR from AGRI single-channel MIR data, which helps promote its application in related fields.