Dissolved oxygen concentration inversion based on Himawari-8 data and deep learning: a case study of lake Taihu

Frontiers in Environmental Science

Environmental Science

Qi Lang¹  Hang Yin²  Qian Zhang²  Peng Wang²  Haozhi Wang²  Wei Li³  Wenhao Yang⁴  Guoxin Chen⁵  Kaifang Shi⁵ 

[1] Chinese Research Academy of Environmental Sciences, Beijing, China;College of Water Conservancy and Civil Engineering, Shandong Agricultural University, Taian, China;Government Service Center of Beijing Municipal Water Bureau, Beijing, China;School of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang, China;State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, China;

关键词: inversion for water quality;    remote sensing model;    multi-modal deep neural network;    synchronous satellite;    dissolved oxygen;   

DOI  :  10.3389/fenvs.2023.1230778

 received in 2023-05-29, accepted in 2023-09-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

Dissolved Oxygen (DO) concentration is an essential water quality parameter widely used in water environments and pollution assessments, which indirectly reflects the pollution level and the occurrence of blue-green algae. With the advancement of satellite technology, the use of remote sensing techniques to estimate DO concentration has become a crucial means of water quality monitoring. In this study, we propose a novel model for DO concentration estimation in water bodies, termed Dissolved Oxygen Multimodal Deep Neural Network (DO-MDNN), which utilizes synchronous satellite remote sensing data for real-time DO concentration inversion. Using Lake Taihu as a case study, we validate the DO-MDNN model using Himawari-8 (H8) satellite imagery as input data and actual DO concentration in Lake Taihu as output data. The research results demonstrate that the DO-MDNN model exhibits high accuracy and stability in DO concentration inversion. For Lake Taihu, the performance metrics including adj_R2, RMSE, Pbias, and SMAPE are 0.77, 0.66 mg/L, −0.44%, and 5.36%, respectively. Compared to the average performance of other machine learning models, the adj_R2 shows an improvement of 6.40%, RMSE is reduced by 8.27%, and SMAPE is decreased by 12.1%. These findings highlight the operational feasibility of real-time DO concentration inversion using synchronous satellite data, providing a more efficient, economical, and accurate approach for real-time DO monitoring. This method holds significant practical value in enhancing the efficiency and precision of water environment monitoring.

【 授权许可】

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Copyright © 2023 Shi, Lang, Wang, Yang, Chen, Yin, Zhang, Li and Wang.