基于卷积神经网络的全球海洋叶绿素a浓度反演方法

doi:10.3964/j.issn.1000-0593(2023)02-0608-06

Abstract
References
Related Citation (15)

Download: PDF (14482 KB)
Export: BibTeX | EndNote (RIS)

Abstract Chlorophyll-a, the main pigment in phytoplankton, is an indicator of the degree of eutrophication in the water, so accurately obtaining and predicting the concentration of chlorophyll-a can provide a theoretical basis for protecting the marine environment. In the experiment, remote sensing images obtained by the moderate resolution imaging spectrometer were used as data sources, and images of chlorophyll-a concentration in the same water area were taken as the true relative value. The convolution neural network was used to establish the relationship model between remote sensing reflectance and chlorophyll-a concentration, and then the inversion of marine chlorophyll-a concentration was realized. The experimental procedure starts with pre-processing the global ocean reflectance data (band combinations of 412, 469, 488, 547, 667 nm) and chlorophyll-a concentration data in 2020 by multiplicative amplification and logarithmic transformation. Then, a convolutional neural network inversion model for the concentration of marine chlorophyll-a was constructed based on the data set of the water area at the boundary of the Pacific Ocean and the Indian Ocean in January 2020, which was divided into the training set and validation set. The coefficient of determination (R²), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were used as evaluation indicators to optimise the inverse model. Finally, the data of chlorophyll-a concentration from January to December 2020 were used as the test set to verify the inversion accuracy of the model. The results show that the proposed inversion model has an accuracy of R²=0.930, RMSE=0.130, MAE=0.102 and demonstrate that the inversion results of chlorophyll-a concentrations given by the model are in high agreement with ground truth values. It can be applied in inversion studies of global marine chlorophyll-a concentration based on remote sensing imagery.

Key words：Convolutional neural networks; Remote sensing image; Chlorophyll-a; Inversion

Received: 2021-09-07 Accepted: 2022-04-30

ZTFLH:

TP751.1

Corresponding Authors: FU Yun E-mail: linda_fy@cust.edu.cn

Cite this article:

SUN Xi-tong,FU Yun,HAN Chun-xiao, et al. An Inversion Method for Chlorophyll-a Concentration in Global Ocean Through Convolutional Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 608-613.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)02-0608-06 OR https://www.gpxygpfx.com/EN/Y2023/V43/I02/608

[1] LIANG Xing-fei, GUO Zong-lou（梁兴飞, 郭宗楼）. Acta Hydrobiologica Sinica（水生生物学报）, 2010, 34(4): 856.
[2] Emanuele Organelli, Annick Bricaud, Bernard Gentili, et al. Remote Sensing of Environment, 2016, 186: 297.
[3] Nguyen Ha, Katsuaki Koike, Mai Nhuan. Remote Sensing, 2013, 6(1): 421.
[4] Smith M E, Lain L Robertson, Bernard S. Remote Sensing of Environment, 2018, 215: 217.
[5] Miao S, Lyu H, Wang Q, et al. Ecological Indicators, 2019, 101: 399.
[6] Li X, Sha J, Wang Z L. Environmental Science and Pollution Research, 2018, 25(20): 19488.
[7] Kong Xianyu, Che Xiaowei, Su Rongguo, et al. Journal of Oceanology and Limnology, 2017, 36(2): 249.
[8] LIU Wei-yang, PENG Jie, DOU Zhong-jiang, et al（柳维扬, 彭杰, 窦中江, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2017, 37(1): 156.
[9] Tan K Z, Wang S W, Song Y Z, et al. Chemometrics and Intelligent Laboratory Systems, 2018, 172: 68.
[10] Yu B W, Xu L L, Peng J H, et al. Journal of Applied Remote Sensing, 2020, 14(3): 034520.
[11] Gu J X, Wang Z H, Kuen J, et al. Pattern Recognition, 2018, 77(5): 354.
[12] Makantasis K, Karantzalos K, Doulamis A, et al. Deep Supervised Learning for Hyperspectral Data Classification Through Convolutional Neural Networks. IEEE International Geoscience and Remote Sensing Symposium，2015. 4959.
[13] XIE Ting-ting, CHEN Yun-zhi, LU Wen-fang, et al（谢婷婷, 陈芸芝, 卢文芳, 等）. Acta Scientiae Circumstantiae（环境科学学报）, 2019, 39(12): 4276.
[14] WANG Hao-yun, CAO Xue-lian, SUN Yun-xiao, et al（王浩云, 曹雪莲, 孙云晓, 等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2021, 52(3): 202.
[15] LU Dong-yu, CUI Xin-tu, HUANG Jing-rong, et al（卢东昱, 崔新图, 黄镜荣）. College Physics（大学物理）, 2006，(1): 50.
[16] Igor Sevo, Aleksej Avramovic. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 740.
[17] ZHANG Ting-lu, SHI Ying-ni（张亭禄, 施英妮）. Periodical of Ocean University of China（中国海洋大学学报）, 2005，(5): 849.