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Atmospheric correction of optical satellite imagery is crucial for quantitative and multitemporal remote sensing applications. Synchronous atmospheric correction methods resolve the spatiotemporal matching problem of correction parameters for the high-spatial-resolution sensor using spatiotemporally registered observations from an atmospheric corrector on the same platform, thereby providing higher-accuracy surface reflectance retrieval. However, for satellite imagery with meter-scale or higher spatial resolution, existing synchronous atmospheric correction (SAC) methods often overlook local-scale atmospheric heterogeneity, which can introduce nonnegligible errors in surface reflectance retrieval. To address this limitation, we propose an Enhanced Synchronous Atmospheric Correction (ESAC) method and validate it by correcting the high-spatial-resolution (HSR) optical imagery from the Nadir-view channel of the Directional Multispectral Camera (DMC-NAD) onboard the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). ESAC leverages spatiotemporally registered, high-spatial resolution atmospheric parameters from the Directional Polarimetric Camera (DPC) on the same platform to perform fine-scale correction of atmospheric and adjacency effects, leading to more accurate surface reflectance retrieval. Experimental results demonstrate that the proposed method effectively reduces the impact of atmospheric heterogeneity on HSR optical imagery quality, resulting in improved radiometric uniformity across the scene. The corrected surface reflectance closely aligns with Sentinel-2 Multispectral Imager (MSI) L2A surface reflectance data in the visible bands, achieving a mean absolute error below 0.013 and a root mean square error below 0.020 in reflectance, while maintaining strong temporal consistency across different acquisition dates. Furthermore, multiple no-reference image quality indicators and the National Imagery Interpretability Rating Scale (NIIRS) show substantial gains: clarity and contrast increase by at least 54.48% and 86.12%, respectively, and NIIRS ratings rise by no less than 0.22, confirming the effectiveness of ESAC in enhancing surface retrieval accuracy, image quality, and interpretability.
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Sui et al. (2026) studied this question.
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