CNN-based inversion of chlorophyll-a in the Bohai Sea with spatio-temporal variation characterization of driving forces

Zhaoyang Li; Yan Xue

doi:10.1117/12.3010248

15 November 2023 CNN-based inversion of chlorophyll-a in the Bohai Sea with spatio-temporal variation characterization of driving forces

Zhaoyang Li, Yan Xue

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150W (2023) https://doi.org/10.1117/12.3010248
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

With the strategy of Marine potestatem gradually promoting, satellite remote sensing technology has been widely used in chlorophyll-a concentration inversion and evaluation because of its advantages of large range and long time series. In this paper, using the data of GOCI with high temporal and spatial resolution and taking the fluorescence line height (FLH) as a sensitive factor, the chlorophyll-a concentration in Bohai Sea is inversed by CNN convolutional neural network, analyzing its temporal and spatial distribution characteristics and influencing factors. The results show that the spatial distribution of chlorophyll-a decreases from the nearshore to the interior of Bohai Sea, presenting annular distribution. The standard deviation of the overall change of chlorophyll-a concentration is less than 0.4mg/m3 during the five years from 2016 to 2020. However, the daily variation is large. In addition to the positive correlation between chlorophyll-a concentration and total suspended sediments concentration, sea surface temperature, tidal level, pollution discharge and ocean vortex are also factors that cannot be ignored.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Zhaoyang Li and Yan Xue "CNN-based inversion of chlorophyll-a in the Bohai Sea with spatio-temporal variation characterization of driving forces", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150W (15 November 2023); https://doi.org/10.1117/12.3010248

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KEYWORDS

Fluorescence

Data modeling

Machine learning

Ocean optics

Remote sensing

Water

Convolutional neural networks

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