Atmospheric correction (AC) of satellite-measured polarized radiances is crucial due to the intricate radiative transfer processes within the atmospheric-ocean system and the diverse applications of polarimetric ocean color remote sensing. This study presents the intelligent polarization AC (IPAC) algorithm that efficiently handles multiangle, multispectral, and polarimetric satellite observations to derive polarized water-leaving reflectance, as well as aerosol properties (coarse-mode and fine-mode) and water inherent optical properties in open-ocean waters. To develop the IPAC algorithm, we simulated top-of-atmosphere (TOA) vector apparent reflectances over the open ocean using a vector radiative transfer simulation (VRTS) model. These simulations employed statistical results from satellite Level-3 products and precalculated lookup tables of polarization water-leaving radiance transmittances. Generating approximately 60 million TOA vector apparent reflectances and 89 inversion submodels facilitated the construction of the IPAC model. Performance assessment of IPAC included three wavelengths (490, 670, and 865 nm), three polarization states, and 13 solar-sensor geometries for each satellite pixel. Validation analysis revealed that the IPAC algorithm significantly improved the accuracy of retrieved ocean color products compared to standard AC algorithms. The mean absolute percentage error in the retrieved polarized apparent water-leaving reflectance and chlorophyll concentration relative to the measurement data was below 34.43% and 37.66%, respectively. Overall, the IPAC model proves its capability to deal with multiangle, multispectral, and polarimetric satellite data and advance the current ocean color work for many applications.

Multiangle polarization measurements in the near-infrared band from space were illustrated as suitable for the inversion of aerosol vertical distribution (AVD) information. In this study, we reported the interference of the AVD to linearly polarized radiances (${\rho }_{Q}$and${\rho }_{U}$) and scalar radiance (${\rho }_{I}$) under different simulation configurations, and the sensitivity discrepancies of${\rho }_{I}$,${\rho }_{Q}$, and${\rho }_{U}$to the aerosol scale height were analyzed by theoretical calculation of Mie scattering theory. Furthermore, the high correlation between polarization measurements in the near-infrared band and AVD inspired to perform polarization measurement inversion of the aerosol layer height (ALH). The constructed model was based on nonlinear optimization and the total-absorption ocean assumption. By fitting the linearly polarized radiances obtained by polarization observations in the near-infrared band, the AVD information were retrieved. The evaluation indicators showed that the root mean square error (RMSE) of the retrievals is less than 1 km for three typical sea areas, which demonstrates that polarization inversion is a valuable addition to light dectection and ranging (Lidar) data for AVD measurement.

The adjacency effect (AE) caused by the surrounding land cannot be ignored for satellite remote sensing of coastal and inland waters, especially for rivers with a narrow width. However, there is currently a lack of comprehensive understanding of AE for rivers, much less the precise correction methods for these effects. Here, a 3-D vector radiative transfer model for a 3-D radiative transfer model for a nonuniform underlying surface (NUS-MC) was developed. The ability of the NUS-MC to accurately simulate AEs in the case of Rayleigh scattering was validated by comparing its results with those from existing models for both uniform and nonuniform underlying surface cases. The mean absolute percentage deviations (MAPDs) for the simulated radiance are within 0.14% for a uniform water underlying and within 0.34% for a uniform land underlying surface. Based on the NUS-MC, AEs on Rayleigh scattering radiance for river waters under various conditions were systematically quantified. The results show that$\rho _{\mathrm {AE}}$decreases with increasing solar zenith angle (SZA) but increases with increasing view zenith angle (VZA). Even for a large river, 10 km across, at a wavelength of 443 nm, the mean${\rho }_{\mathrm {AE}}$at the river center is 3.97% at an SZA of 0° when the land albedo is 0.1, increasing to 18.19% and 33.37% at land albedos of 0.3 and 0.5, respectively. Overall, AEs significantly affect Rayleigh scattering even in rivers with widths of up to 10 km. In addition, the angle between the river channel and the observation plane as well as the distance from the riverbank to the point of view in the river can also affect the AEs. These findings suggest that the impact of land AEs on Rayleigh scattering must be thoroughly considered to retrieve water-leaving radiance in rivers accurately, and it is essential to develop atmospheric correction methods capable of removing AEs.

We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll$a$(Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with$R^{2} = 0.962$, root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.

Satellite ocean color products have been widely used to monitor spatiotemporal variations in marine ecological environments from regional to global oceans. However, current satellite ocean color products fail to provide effective records during the winter in high–latitude oceans, limiting understanding of the marine ecological environment during the winter season. In this study, we proposed an atmospheric correction model, namely, the Neural Network Atmospheric Correction algorithm for the Southern Hemisphere (NN–AC–SH), and recover winter satellite ocean color products for the high–latitude oceans of the Southern Hemisphere from 2003 to 2020. The accuracy of the NN–AC–SH model was verified based on the in situ data from the Aerosol Robotic Network–Ocean Color (AERONET–OC). The results indicate that the NN–AC–SH model performed better than the traditional near–infrared (NIR) iterative atmospheric correction algorithm, e.g., in the 443, 488 and 531 nm bands, the relative deviations of the NN–AC–SH model were 23.11%, 20.96% and 23.14%, respectively, while the values of the NIR model were 30.72%, 22.85%, and 24.81%, respectively. Under the observation condition of a high solar zenith angle (SZA), the NN–AC–SH model performed better than the NIR model in terms of the amount of effective data (94 vs. 78 data points) and model inversion accuracy (a relative deviation 32.83% vs. 43.60%). Moreover, in situ chlorophyll concentration data from the NASA bio–Optical Marine Algorithm Dataset (NOMAD) and Chinese Antarctic Research Expedition (CHINARE) were used to verify the accuracy of the restored chlorophyll concentration products, and the results reveal that the NN–AC–SH model resulted in more effective records of higher accuracy than those obtained with the NASA–distributed chlorophyll concentration products. Overall, for the first time, this study recovered long time–series (2003–2020) ocean color products for the high–latitude oceans of the Southern Hemisphere (≥50°S) in the winter, which could provide unique satellite products for investigating the marine ecological environment of the Antarctic and sub–Antarctic oceans in the winter.

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce$\sim 4$%–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to$\sim 12$%–15% and$\sim 30$%–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.