Atmospheric correction (AC) is the basis for quantitative water remote sensing, and adjacency effects form an important part of AC for medium- and high-spatial-resolution optical satellite images. The 6S radiative transfer model is widely used, but its background reflectance function does not take the radiance changes at water–land boundaries into account. If the observed land possesses bright features, the radiance of the adjacent water will be affected, leading to deviations in the AC results and increasing the uncertainty of water depth-based optical quantitative remote sensing. In this paper, we propose a model named WL-AE (a correction model for water–land boundary adjacency effects), which is based on the obvious radiance differences at water–land boundaries. This model overcomes the problem by which the background reflectance calculation is not terminated due to the highlighting pixel. We consider the influences of different $R_{ns}$ (neighborhood space) on the target pixel. The effective calculation of the equivalent background reflectance of the target pixel is realized, and the influence of the land area anomaly highlighting the pixel on the adjacent water is avoided. The results show that WL-AE can effectively improve the entropy and contrast of the input image and that the water–land boundary is greatly affected by adjacency effects, especially in the green and near-infrared bands, where the $M_{rc}$ (mean rate of change) are as high as 14.2% and 20.1%, respectively. In the visible wavelength, the $Sd$ of $R_{rc}$ (the relative rate of change) is positively correlated with $R_{ns}$, and the $Sd$ reaches 16.9%. Although the adjacency effect is affected by ground object types, its influence area remains within 3 km offshore. Based on the WL-AE and 6S results, the comparative test regarding bathymetric inversion shows that the influence is significant in the 0–5 m depth section. In Penang, the MRE of the 0–4 m inversion results is 31.4%, which is 10.5% lower than that of the 6S model.

Mesosphere is the core region of atmospheric wave activities, where fine temperature data are essential for analyzing its dynamic processes. We use an interpretable Data Diffusion Fusion Model to fuse sparse Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) observations with coarse‐resolution Modern‐Era Retrospective Analysis for Research and Applications V2. By combining spatiotemporal properties, F10.7 and Kp, the mesospheric temperature is reconstructed with high‐precision, increasing resolution by 5 times. Results show that Denoising Diffusion Fusion Model outputs incorporates more information from SABER data, improving the Peak Signal‐to‐Noise Ratio by 5.02 dB. Our reconstruction model demonstrates excellent global stability ( R ² > 0.94). In the January 2019 Sudden Stratospheric Warming event, the model not only accurately reproduces the small‐scale features, but also significantly reduces the deviation from 21.7% to 14.9% with SABER data. This outcome fully proves its superior performance in handling extreme events. Importantly, even in the absence of observations, our model can still deliver high‐precision data set to support scientific investigations.

Near‐space temperature is crucial for atmospheric dynamics and aerospace applications, yet data sets with refined vertical resolution remain scarce. We present the Atmospheric Spatio‐Temporal Reconstruction Network (AtmoST‐Net), which integrates high‐resolution SABER observations with dual‐source reanalysis, combining non‐uniform vertical enhancement and explicit physical forcing factors (as input features) to reconstruct temperature at 20–80 km, improving the mid‐to‐upper atmosphere vertical resolution by 3.6 km. AtmoST‐Net achieves mean absolute error <3K at 20–50 km and improves accuracy by 5.0% in the transition zone (∼65 km). It reliably reproduces the vertical structure of Antarctic sudden stratospheric warming and recovers QBO (Quasi‐Biennial Oscillation) signals in the 50–60 km region that are underrepresented in traditional reanalysis data sets, with the first EOF (empirical orthogonal function) principal component showing correlation with QBO of 0.498 ( p < 0.01) and variance contribution reaching 96.06%. Our data set fills critical observational gaps and provides support for atmospheric dynamics research.