A universal physically-based topographic correction framework for high-resolution optical satellite data

Surface reflectance, retrieved via atmospheric correction from top-of-atmosphere (TOA) observations, characterizes the intrinsic properties of the Earth’s surface. Despite its importance, topographic effects are often neglected in surface reflectance retrievals, introducing significant uncertainties, particularly over mountainous regions. Existing topographic correction methods, while numerous, commonly face challenges: physically-based approaches require accurate atmospheric parameters (often unavailable in complex terrains) and are computationally intensive, whereas semi-empirical methods depend on empirical parameters that can lead to overcorrection or inconsistent performance across diverse conditions. Additionally, prior studies have predominantly utilized data from a single satellite platform, limiting the applicability and transferability of topographic correction algorithms across diverse datasets. To overcome these limitations, we introduce a Universal Topographic Correction (UTC) framework, a physically-based approach designed for seamless integration with multiple high-resolution satellite and airborne datasets. The UTC integrates spectral information from extensive radiative transfer simulations with image-derived spatial information to optimize spectral direct irradiance ratios, a key component of physically-based correction, while accounting for shadow effects and digital elevation model (DEM)-induced errors through targeted processing along shadow boundaries. We evaluated UTC’s performance against established methods, including C-correction, SCS + C, and Statistical-Empirical (SE), using a 3D radiative transfer model as a reference across varied topographic and illumination conditions. Results show that UTC consistently outperforms these methods, particularly in shadowed areas, with mean absolute deviations in the near-infrared band of 0.0103 for UTC compared to 0.0179 (C), 0.0362 (SCS + C), and 0.0311 (SE). Testing across Landsat 9 (30 m), Sentinel-2 (20 m), SPOT 4/5 (10–20 m), PlanetScope (3 m), and AVIRIS-3 (∼2.9 m) datasets further demonstrates UTC’s robustness, effectively reducing overcorrection in complex terrains and improving reflectance accuracy in shadowed regions. UTC’s advantages lie in (i) requiring no external atmospheric inputs, and (ii) its physically-informed design based on spectral and spatial information for broad applicability. This study underscores critical limitations in existing topographic correction methods and proposes a robust solution for addressing them. Future research could enhance the UTC framework by integrating atmospheric effects, thereby achieving combined atmospheric and topographic correction from top-of-atmosphere observations.

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Publication Details

Journal
ISPRS Journal of Photogrammetry and Remote Sensing
Published
2025-06-27
DOI
https://doi.org/10.1016/j.isprsjprs.2025.05.027
Citations
7
Primary Topic
Satellite Image Processing and Photogrammetry
Type
article
Field-Weighted Citation Impact
8.02
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A universal physically-based topographic correction framework for high-resolution optical satellite data

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article

A universal physically-based topographic correction framework for high-resolution optical satellite data

Jianglei Xu, Han Ma, Wanshan Peng, Yongzhe Chen, Wenyuan Li, Shunlin Liang, Shikang Guan, Yichuan Ma, Tao He
article en
7 citations

Abstract

Surface reflectance, retrieved via atmospheric correction from top-of-atmosphere (TOA) observations, characterizes the intrinsic properties of the Earth’s surface. Despite its importance, topographic effects are often neglected in surface reflectance retrievals, introducing significant uncertainties, particularly over mountainous regions. Existing topographic correction methods, while numerous, commonly face challenges: physically-based approaches require accurate atmospheric parameters (often unavailable in complex terrains) and are computationally intensive, whereas semi-empirical methods depend on empirical parameters that can lead to overcorrection or inconsistent performance across diverse conditions. Additionally, prior studies have predominantly utilized data from a single satellite platform, limiting the applicability and transferability of topographic correction algorithms across diverse datasets. To overcome these limitations, we introduce a Universal Topographic Correction (UTC) framework, a physically-based approach designed for seamless integration with multiple high-resolution satellite and airborne datasets. The UTC integrates spectral information from extensive radiative transfer simulations with image-derived spatial information to optimize spectral direct irradiance ratios, a key component of physically-based correction, while accounting for shadow effects and digital elevation model (DEM)-induced errors through targeted processing along shadow boundaries. We evaluated UTC’s performance against established methods, including C-correction, SCS + C, and Statistical-Empirical (SE), using a 3D radiative transfer model as a reference across varied topographic and illumination conditions. Results show that UTC consistently outperforms these methods, particularly in shadowed areas, with mean absolute deviations in the near-infrared band of 0.0103 for UTC compared to 0.0179 (C), 0.0362 (SCS + C), and 0.0311 (SE). Testing across Landsat 9 (30 m), Sentinel-2 (20 m), SPOT 4/5 (10–20 m), PlanetScope (3 m), and AVIRIS-3 (∼2.9 m) datasets further demonstrates UTC’s robustness, effectively reducing overcorrection in complex terrains and improving reflectance accuracy in shadowed regions. UTC’s advantages lie in (i) requiring no external atmospheric inputs, and (ii) its physically-informed design based on spectral and spatial information for broad applicability. This study underscores critical limitations in existing topographic correction methods and proposes a robust solution for addressing them. Future research could enhance the UTC framework by integrating atmospheric effects, thereby achieving combined atmospheric and topographic correction from top-of-atmosphere observations.

ISPRS Journal of Photogrammetry and Remote SensingVol. 227
Wuhan University (CN), Hong Kong Jockey Club (HK), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), University of Hong Kong (HK)
Climate action
Openalex Percentile: Top 2%
Satellite Image Processing and Photogrammetry
8.02
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