Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (L_w) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the L_w DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the L_w DOP on the retrieval of oceanic constituents and IOPs.

Using the near-space platform to conduct radiometric calibrations of ocean color sensors is a promising method for refining calibration precision, but there is knowledge gap about the radiance contributions above near-space over the open ocean. We used the radiative transfer (RT) model (PCOART) to assess the contributions (L_R) of the upwelling radiance received at the near-space balloons to the total radiance (L_t) measured at the top of the atmosphere (TOA). The results indicated that the L_R displayed distinct geometric dependencies with exceeding 2% across most observation geometries. Moreover, the L_R increased with wavelengths under the various solar zenith angles, and the L_R values fell below 1% only for the two near-infrared bands. Additionally, the influences of variations in oceanic constituents on L_R were negligible across various azimuth angles and spectral bands, except in nonalgal particle (NAP)-dominated waters. Furthermore, the influences of aerosol optical thicknesses (AOTs) and atmospheric vertical distributions on L_R were examined. Outside glint-contaminated areas, the atmosphere-associated L_R variations could exceed 2% but declined substantially as AOTs increased under most observation geometries. The mean height of the vertically inhomogeneous layer (h_m) significantly influenced L_R, and the differences in L_t could exceed 5% when comparing atmospheric vertical distributions following homogeneous versus Gaussian-like distributions. Finally, the transformability from near-space radiance to L_t was examined based on a multiple layer perceptron (MLP) model, which exhibited high agreement with the RT simulations. The MAPD averaged 0.420% across the eight bands, ranging from 0.218% to 0.497%. Overall, the radiometric calibration utilizing near-space represents a significant innovation method for satellite-borne ocean color sensors.

Reliable multimodal image analysis is challenging when complementary sensors exhibit class-dependent strengths and local prediction uncertainty. This study presents a reliability-aware hybrid decision-fusion framework for fine-grained tidal-flat land-cover mapping as a demanding application of multisource image processing. A pyramid scene parsing network (PSPNet) with a 50-layer residual network (ResNet-50) backbone extracts multi-scale spatial context from high-resolution multispectral patches, while a Random Forest models 166-dimensional hyperspectral reflectance signatures. Instead of applying uniform score averaging, the framework combines validation-derived class reliability, pixel-level confidence, and branch disagreement to preserve trusted spatial predictions and selectively introduce spectral correction in uncertain regions; morphological refinement improves local mask continuity. Experiments used GF-2, GF-7, ZY1-02E/F, and ground-reference data acquired from 2022 to 2024, comprising 23 classes, 12,666 image samples, and 452,156 spectral samples. The method achieved 94.64% overall accuracy, 92.85% mean class recall (MCR), 85.47% mean intersection over union (mIoU), 91.72% macro-averaged F1 score (Macro-F1), and 85.88% Small-object F1. Relative to the PSPNet and Random Forest branches, mIoU increased by 4.73 and 3.84 percentage points, while Small-object F1 increased by 5.36 and 6.92 points. The results demonstrate that reliability-aware hybrid fusion can improve both spectrally ambiguous surface classification and spatially fragmented object recognition in complex multimodal scenes.