81 / 2023-07-28 17:58:34

Global distribution of the spectral power coefficient of particulate backscattering coefficient obtained by a neural network scheme

spectral power coefficient, particulate backscattering, ocean color, neural network

The spectral power coefficient (η) of the particulate backscattering coefficient (b_bp(λ)) is directly estimated from the remote sensing reflectance (R_rs(λ)) with a neural network-based scheme (NN_η) in this study. Evaluations with both synthetic dataset and in-situ measurements show that NN_η could significantly improve the accuracy of estimated η compared to several conventional schemes reported in the literature that are based on chlorophyll-a concentration (Chl), band ratios of R_rs(λ), or remotely sensed b_bp(λ). Demonstrations with measurements from MODerate resolution Imaging Spectroradiometer (MODIS) Aqua further confirm the robustness of NN_η, where reasonable spatial distribution and seasonality of η in the global oceans can be acquired by NN_η. High and low η values are observed in the oligotrophic gyres and the coastal zones, respectively, which are consistent with the current understanding of η distribution concluded from theoretic analysis and repeated field measurements in the global ocean. Implementation of NN_η to 19-year MODIS monthly composite measurements from 2003 to 2021 reveals strong seasonal variations of η in most of the global ocean, but the decadal changes of η are insignificant in the majority (~82.2%) of the global ocean. Similar to any empirical algorithms, the performance of NN_η is dependent on the training dataset, particularly its range, here a proper upper limit of η for natural waters is provided.