Process-Oriented Estimation of Chlorophyll-a Vertical Profile in the Mediterranean Sea Using MODIS and Oceanographic Float Products - Li, Xiaojuan; Mao, Zhihua; Zheng, Hongrui; Zhang, Wei; Yuan, Dapeng; Li, Youzhi; Wang, Zheng; Liu, Yunxin (Frontiers in Marine Science, 2022)
Chlorophyll a (Chl-a) vertical profile reconstruction provides us a promising way to investigate the internal structure of marine ecosystem. Given the drawbacks of the profile classification methods are either too subjective or too complex in current process-oriented profile inversion methods, a novel Chl-a profile reconstruction method incorporating a decision tree profile classification model and a profile inversion model in the Mediterranean Sea is proposed by introducing a profile classification model (PCM), an advanced unsupervised machine learning clustering method. The profile classification model is built based on the a priori knowledge provided by clustering results of BGC-Argo Chl-a profiles implemented by the PCM, and is expected to classify per-pixel profile type of the MODIS Chl-a product combined with the products of mixed layer depth and euphotic layer depth. The profile inversion model contains the relationships between the shape-dependent parameters of the non-uniform Chl-a profile and the corresponding Chl-a surface concentration. Quantitative evaluation indicated that the overall accuracy of the proposed profile classification model reached 89%, and the mean absolute percent deviation (MAPD) of the proposed profile inversion model were ranged from 12%–37% under different shape-dependent parameters. By generating monthly 3D Chl-a data from 2011 to 2018, the proposed process-oriented method exhibits great application potential in investigating the spatial and temporal characteristics of Chl-a profile and even the water column total biomass in the Mediterranean Sea.