An Enhanced Atmospheric Pre-Corrected Differential Absorption (APDA) Algorithm by Extending LUTs Applied to Analyze ZY1-02D Hyperspectral Images

Abstract

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the past 40 years, numerous CWV inversion algorithms have been developed, with refinements to enhance retrieval accuracy and reliability. In this study, we proposed an enhanced atmospheric pre-corrected differential absorption (APDA) algorithm. This enhancement was achieved by thoroughly analyzing water vapor absorption in relation to elevation and aerosol optical depth and extending look up tables (LUTs). The enhanced method utilizes a pre-built MODTRAN lookup table and is applied to ZY1-02D hyperspectral data from a satellite launched in 2020. We compared the inversion results of 10 ZY1-02D scenes obtained using the improved method with AERONET measurements and inversion results from commonly used atmospheric correction software, namely, FLAASH and ATCOR. The updated algorithm demonstrated a lower average error (0.0568 g·cm−2) and relative average error (10.49%) compared to the ATCOR software (0.17 g·cm−2 and 40.78%, respectively) and the FLAASH module (0.13 g·cm−2 and 30.82%, respectively). Consequently, the enhanced method outperforms traditional CWV inversion algorithms, especially at high altitudes.

Keywords

• columnar water vapor (CWV)
• ZY1-02D
• atmospheric pre-corrected differential absorption (APDA) technique