Remote sensing algorithm for dissolved organic carbon in the Laptev Sea: Correction of photobleaching effect using spectral slope - Ruiwu Zhang; Ruru Deng; Jun Ying; Cong Lei; Jiayi Li; Yu Guo; Tongtong Zhao (Ecological Informatics)
The absorption coefficient of colored dissolved organic matter (αCDOM) is a critical optical parameter for quantifying dissolved organic carbon (DOC). However, photobleaching significantly reduces αCDOM, leading to uncertainties in DOC concentration estimation, an issue that has not received widespread attention. Drawing on in situ measurements from the Laptev Sea, this study proposes a method to correct for photobleaching using the spectral slope (S275–295). Setting a threshold for S275–295 identifies areas that are significantly affected by photobleaching. To assess the applicability of this method, a stratified estimation model analyses the relationship between αCDOM and DOC concentration before and after correction at different water depths. A remote sensing inversion algorithm for DOC was also developed based on αCDOM and remote sensing reflectance data. Results indicate that αCDOM443 effectively characterises DOC concentration across different water depths. After correction, the photobleaching-induced error decreases by approximately 8.04 %, significantly improving the non-linear fitting accuracy of αCDOM443 with DOC concentration in the surface water layer (0-20 m). Results for depths greater than 20 m remain essentially unchanged, although incorporating temperature and salinity improves the linear correlation with DOC, with some uncertainties persisting. The correction method is therefore most applicable to surface waters. Remote sensing results show that this method reduces DOC overestimation in coastal areas by 12 %, improving fitting accuracy and minimising error distribution. This study highlights the impact of photobleaching on DOC estimation and introduces a correction model that enhances the accuracy of remote sensing-based DOC retrieval, thereby supporting marine carbon cycle monitoring