What question did this study set out to answer?

To evaluate how different aerosol-type assumptions affect the accuracy of aerosol optical depth estimation and surface reflectance retrieval in Landsat 8 atmospheric correction.

August 14, 2026Atmospheric measurement techniques0 citationsOpen Access

Impact comparison of different aerosol types on atmospheric correction of Landsat 8 over land

SZShuning Zhang HZHao Zhang BZBing Zhang

Key Points

To evaluate how different aerosol-type assumptions affect the accuracy of aerosol optical depth estimation and surface reflectance retrieval in Landsat 8 atmospheric correction.
Evaluated over 600 Landsat 8 scenes acquired across 100 global Aerosol Robotic Network (AERONET) validation sites.
Compared atmospheric correction performance among three aerosol models: Urban Clean (used in LaSRC), MOD04-based, and MOD09-based aerosol assumptions across visible, near-infrared, and shortwave infrared bands.
MOD04-based aerosol retrieval achieved the highest aerosol optical depth accuracy with an R² of 0.762, an RMSE of 0.0437, and a bias of 0.0876, while delivering the highest surface reflectance accuracy in visible and near-infrared bands.
Surface reflectance metrics across the three aerosol types ranged between 0.00036–0.00043 for accuracy, 0.02382–0.02468 for precision, and 0.02337–0.02425 for uncertainty.
MOD09-based models showed superior performance over bright surfaces, whereas the standard Urban Clean model achieved higher stability and accuracy primarily in shortwave infrared bands.

Abstract

Abstract. The official Landsat 8 surface reflectance (SR) product, generated by the Land Surface Reflectance (LaSRC) algorithm, is the most extensively utilized medium-resolution dataset and serves as a benchmark to cross-validate the accuracy of other SR products. However, the accuracy of the Landsat 8 SR products did not meet the expectations of the previous studies under specific conditions. Consequently, it is necessary to analyze the Urban Clean aerosol-type assumption implemented in the LaSRC algorithm and comprehensively re-evaluate the accuracy of the Landsat 8 SR. Therefore, this study leverages Landsat 8 data over 600 scenes acquired at 100 Aerosol Robotic Network (AERONET) sites globally and conducts a comprehensive analysis of how different aerosol types—MOD04-based (used in the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level‐2 Aerosol Optical Depth Product), MOD09-based (used in MODIS Terra Atmospherically Corrected Surface Reflectance Product), and Urban Clean (used in LaSRC)—affect the accuracy of atmospheric correction (AC) for the first time. The results indicated that, in terms of aerosol optical depth (AOD), the MOD04 aerosol type exhibited the highest accuracy, with an R² of 0.762, Root Mean Square Error (RMSE) of 0.0437, and bias of 0.0876. The accuracy, precision, and uncertainty of the SR products corresponding to the three aerosol types were within the ranges of 0.00036–0.00043, 0.02382–0.02468, and 0.02337–0.02425, respectively. The MOD04-based aerosol type demonstrated the highest overall accuracy in the visible and near-infrared (VNIR) bands. The MOD09-based aerosol type outperformed the others in the bright surface regions. The Urban Clean aerosol type showed a comparable but slightly inferior performance to that of the MOD09-based aerosol type, with limited advantages in specific reflectance ranges. Moreover, LaSRC-derived SR demonstrated higher stability and accuracy in the shortwave infrared (SWIR) bands compared to its inferior performance in the VNIR. These findings emphasize the critical importance of aerosol-type assumptions in AC workflow. A mixed strategic implementation framework is proposed as follows: (1) adopt MOD04-based aerosol types for AOD retrieval and VNIR SR retrieval, (2) utilize MOD09-based aerosol types to process data acquired over bright surface processing, and (3) leverage SWIR SR products derived by LaSRC. Our findings provide actionable guidelines for dynamic aerosol-type selection to enhance the AC performance across diverse environments.

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