Aerosol vertical distribution is decisive and hard to be constrained. It is of great significance for the study of atmospheric climate and environment. Oxygen absorption A-bands (755–775 nm) provide a unique opportunity to acquire vertical aerosol profiles from satellites over a large spatial coverage. To investigate the ability of O₂ A-bands in retrieving aerosol vertical distribution, the dependence of retrieval on satellite observation geometry, spectral resolution, signal-to-noise ratio (SNR), size distribution, and a priori knowledge is quantified using information content theory. This work uses the radiative transfer model UNL to simulate four aerosol modes and the instrument noise model. The simulations show that a small scattering angle leads to an increase in the total amount of observed aerosol profile information, with the degrees freedom of signal (DFS) of a single band increasing from 0.4 to 0.85 at high spectral resolution (0.01 nm). The total DFS value of O₂ A-bands varies accordingly between 1.2–2.3 to 3.8–5.1 when the spectral resolution increases from 1 nm to 0.01 nm. The spectral resolution has a greater impact on DFS value than the impact from SNR (an improvement of roughly 41–53% resulted from the change in spectral resolution and the SNR led to 13–18%). The retrieval is more sensitive to aerosols with a coarse-dominated mode. The improvement in spectral resolution on information acquisition is demonstrated using the DFS and the posterior error at various previous errors and resolutions.

Enhancing the accuracy of paddy rice mapping is crucial for bolstering global food security. Prior research incorporating Sentinel imagery with phenological characteristics has identified paddy rice fields effectively. However, challenges such as reliance on a single index, cloud cover interference, and a lack of sufficient training samples continue to complicate the mapping of paddy rice. This study introduces a comprehensive paddy rice mapping framework that incorporates annual phenological features throughout the entire growth phase. This was achieved by expanding the sample size through the extraction of phenological features, and the visually verified samples were then integrated with distinct phenological phases and relevant indices, utilizing hybrid Sentinel-1/2 imagery to map paddy rice distribution. The accuracy of the generated rice map was validated against trusted samples, corroborative agricultural statistics, and another high-resolution 10 m mapping product. Compared with ground-truth samples, the algorithm has achieved an overall accuracy of approximately 92% in most rice production regions with a confusion matrix. Additionally, the estimated rice area in Anhui and several other rice-producing regions shows less than 10% error when compared with governmental statistical records from the yearbook. When compared with another recent paddy rice map at the same spatial resolution (10 m), our approach provided cleaner details and more effectively reduced omission errors. It received values of $R^2$ = 0.991 and slope = 1.08 in a prefecture-level statistical comparison with a counterpart. Our proposed approach is proven to be valid and is expected to offer significant benefits to agricultural sustainability and technological applications in farming.

The polarization crossfire (PCF) suite carried onboard the Chinese GaoFen-5B satellite is composed of a Particulate Observing Scanning Polarimeter (POSP) and a Directional Polarimetric Camera (DPC), which can provide multi-angle, multi-spectral, and polarization data. In this paper, the influence of polarization and the directionality of reflectance in open oceans on the inversion of chlorophyll a (Chla) concentrations are investigated, from 410 nm to 670 nm. First, we exploit a vector radiative transfer model to simulate the absolute and relative magnitudes of the water-leaving radiance signal (I) and the parallel polarization radiance (PPR) to the top-of-atmosphere (TOA) radiation field. The simulation results show that the PPR can enhance the relative contribution of the water-leaving signal, especially in sunglint observation geometry. The water-leaving signal for PPR exhibits significant directional and spectral variations relative to the observation geometries, and the maximum value of the water-leaving signal for PPR occurs in the backscattering direction. In addition, the sensitivity of the PPR to the Chla concentration is sufficient. The synthetic datasets are utilized to develop retrieval algorithms for the Chla concentrations based on the back-propagation neural network (BPNN). The inversion results show that the PCF strategy improves the accuracy of Chla retrieval, with an RMSE of 0.014 and an RRMSE of 6.57%. Thus, it is an effective method for retrieving the Chla concentration in open oceans, by utilizing both the directionality and polarization of the reflectance.

The Chinese HuanjingJianzai-2 (HJ-2) A/B satellites are equipped with advanced sensors, including a Multispectral Camera (MSC) and a Polarized Scanning Atmospheric Corrector (PSAC). To address the challenges of atmospheric correction (AC) for the MSC’s wide-swath, wide-field images, this study proposes a pixel-by-pixel method incorporating Bidirectional Reflectance Distribution Function (BRDF) effects. The approach uses synchronous atmospheric parameters from the PSAC, an atmospheric correction lookup table, and a semi-empirical BRDF model to produce surface reflectance (SR) products through radiative, adjacency effect, and BRDF corrections. The corrected images showed significant improvements in clarity and contrast compared to pre-correction images, with minimum increases of 55.91% and 35.63%, respectively. Validation experiments in Dunhuang and Hefei, China, demonstrated high consistency between the corrected SR and ground-truth data, with maximum deviations below 0.03. For surface types not covered by ground measurements, comparisons with Sentinel-2 SR products yielded maximum deviations below 0.04. These results highlight the effectiveness of the proposed method in improving image quality and accuracy, providing reliable data support for applications such as disaster monitoring, water resource management, and crop monitoring.