## 5 Discussion  
This study introduces a pixel-by-pixel synchronous atmospheric correction method for wide-swath and wide-field imaging, specifically tailored to the HJ-2A/B MSC, while incorporating BRDF effects. Atmospheric parameters were first retrieved using the PSAC, followed by spatiotemporal matching to synchronize these parameters with the target remote sensing images. An atmospheric correction lookup table was then constructed using a radiative transfer model, taking into account the spectral response functions and band configurations of the MSC. Using this lookup table, pixel-level atmospheric parameters were derived based on the viewing and solar geometries to perform synchronous atmospheric correction for the wide-field remote sensing images. Finally, BRDF correction was implemented by leveraging MODIS surface reflectance products and land cover-based pure-pixel matching, ensuring accurate and reliable surface reflectance retrieval. Compared to traditional atmospheric correction methods, the proposed approach benefits from the real-time acquisition of atmospheric parameters, avoiding the inaccuracies introduced by fixed models or interpolated atmospheric data. The validation experiments confirmed that the corrected images showed notable improvements in visual quality, with contrast increasing by at least 55.91% and clarity by 146.46% in the visible bands. The infrared bands also exhibited significant enhancements, with contrast and clarity improvements of at least 35.63% and 90.51%, respectively. These findings highlight the effectiveness of the method in reducing atmospheric interference and improving surface reflectance retrieval. Synchronous satellite-ground validation experiments were carried out at locations including the Dunhuang site and Hefei in China, focusing on typical land cover types \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Imaging Location** & **Imaging Time** & **Band** & **SR of Sentinel-2** & **SR of HJ-2A/B** & **Absolute Error** \\ \hline Buildings in & 23 December 2022 & B1 & 0.1914 & 0.1779 & 0.0135 \\ Xianning & (HJ-2A) & B2 & 0.2147 & 0.1972 & 0.0175 \\ (29.8952\({}^{\circ}\)N, & and & B3 & 0.2305 & 0.1948 & 0.0357 \\
114.3308\({}^{\circ}\)E) & 25 December 2022 & B4 & 0.2405 & 0.1994 & 0.0411 \\  & (S2A) & B5 & 0.2391 & 0.2192 & 0.0199 \\ \hline Vegetation in & 23 December 2022 & B1 & 0.0328 & 0.0361 & 0.0033 \\ Xianning & (HJ-2A) & B2 & 0.0535 & 0.0435 & 0.0010 \\ (29.9119\({}^{\circ}\)N, & and & B3 & 0.0378 & 0.0321 & 0.0057 \\
114.3410\({}^{\circ}\)E) & 25 December 2022 & B4 & 0.0857 & 0.1097 & 0.0240 \\  & (S2A) & B5 & 0.2723 & 0.2326 & 0.0397 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._such as the Gobi Desert, vegetation, and water bodies. Ground-measured reflectance was compared with the surface reflectance values obtained after atmospheric correction, showing retrieval deviations of less than 0.03. For surface types not covered by ground experiments, Sentinel-2 surface reflectance products were used as validation references, with maximum deviations below 0.043. The accuracy of the reflectance products was consistent with Sentinel-2 and MODIS products [13,31], confirming the reliability of the proposed method. Despite these promising results, several challenges remain. The retrieval of atmospheric parameters may be affected by extreme atmospheric conditions, such as high aerosol loads or cloud contamination. Additionally, the BRDF correction approach, while effective for most land cover types, requires further refinement for complex terrains such as mountainous regions, where topographic effects introduce additional uncertainties. The practical applications of this method span various domains, including environmental monitoring, disaster response, and agricultural assessments. The improved accuracy of surface reflectance retrieval enhances land cover classification, vegetation monitoring, and water quality assessments.