Inversion diffuse attenuation coefficient of photosynthetically active radiation based on deep learning

Accurate estimation of the diffuse attenuation coefficient of photosynthetically active radiation, K_d(PAR), is critical for understanding and modeling key physical, chemical, and biological processes in waters. In this study, a deep learning model (DLKPAR) was developed for remotely estimating K_d(PAR). Compared to the traditional empirical algorithms and semi-analytical algorithm, DLKPAR demonstrated an improvement in the model's stability and accuracy. By using in situ NOMAD data to evaluate the model's performance, DLKPAR had lower root mean square difference (RMSD; 0.028 vs. 0.030-0.048 m^-1) and mean absolute relative difference (MARD; 0.14 vs. 0.17-0.25) and higher R² (0.94 vs. 0.82-0.94). The statistical results of the matchup NOMAD and Argo data to the MODIS also indicated DLKPAR improves the inversion accuracy of K_d(PAR) and could be applied to remotely estimate K_d(PAR) in the global oceans. Therefore, we anticipate that DLKPAR could yield reliable K_d(PAR) values from ocean color remote sensing, providing an accurate estimation of visible light attenuation in the upper ocean and facilitating biogeochemical cycle research.