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<p>In satellite remote sensing of land surface Essential Climate Variables (ECVs) using optical sensors, an atmospheric correction step is typically required to convert top-of-atmosphere (TOA) bi-directional reflectances into top-of-canopy (TOC) bi-directional reflectances. We analyse the error covariance structure of TOC reflectances that arises specifically from uncertainties in atmospheric correction. Using SMAC as the atmospheric correction model and Automatic Differentiation (AD) for efficient Jacobian computation, we quantify these error covariances across different scenarios. Our results show that uncertainties in the atmospheric state introduce non-negligible error correlations of both signs between different satellite bands. Since these error correlations are often overlooked, explicitly accounting for them could improve the accuracy of land surface ECV retrievals.</p>

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Error Covariance Structure Caused by Uncertainties in Atmospheric Correction for Optical Sensors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月12日

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  • Simon Blessing,
  • Ralf Giering

摘要

In satellite remote sensing of land surface Essential Climate Variables (ECVs) using optical sensors, an atmospheric correction step is typically required to convert top-of-atmosphere (TOA) bi-directional reflectances into top-of-canopy (TOC) bi-directional reflectances. We analyse the error covariance structure of TOC reflectances that arises specifically from uncertainties in atmospheric correction. Using SMAC as the atmospheric correction model and Automatic Differentiation (AD) for efficient Jacobian computation, we quantify these error covariances across different scenarios. Our results show that uncertainties in the atmospheric state introduce non-negligible error correlations of both signs between different satellite bands. Since these error correlations are often overlooked, explicitly accounting for them could improve the accuracy of land surface ECV retrievals.