Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning
Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2277 |
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| author | Keming Mao Dakang Wang Shirong Cai Tao Zhou Wenxin Zhang Qianqian Yang Zikang Li Xiankun Yang Lorenzo Picco |
| author_facet | Keming Mao Dakang Wang Shirong Cai Tao Zhou Wenxin Zhang Qianqian Yang Zikang Li Xiankun Yang Lorenzo Picco |
| author_sort | Keming Mao |
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| description | Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R<sup>2</sup> = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments. |
| format | Article |
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| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Remote Sensing |
| spelling | doaj-art-50c87a3213f94e2cbb11c10a9a462ee62025-07-11T14:42:34ZengMDPI AGRemote Sensing2072-42922025-07-011713227710.3390/rs17132277Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine LearningKeming Mao0Dakang Wang1Shirong Cai2Tao Zhou3Wenxin Zhang4Qianqian Yang5Zikang Li6Xiankun Yang7Lorenzo Picco8School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaDepartment of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Agripolis, Viale dell’Università 16, 35020 Legnaro, ItalySchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaDepartment of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, ChinaDepartment of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Agripolis, Viale dell’Università 16, 35020 Legnaro, ItalyDissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R<sup>2</sup> = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments.https://www.mdpi.com/2072-4292/17/13/2277fishpondsdissolved oxygensatellite imagesmachine learningGuangdong–Hong Kong–Macao Greater Bay area |
| spellingShingle | Keming Mao Dakang Wang Shirong Cai Tao Zhou Wenxin Zhang Qianqian Yang Zikang Li Xiankun Yang Lorenzo Picco Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning Remote Sensing fishponds dissolved oxygen satellite images machine learning Guangdong–Hong Kong–Macao Greater Bay area |
| title | Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning |
| title_full | Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning |
| title_fullStr | Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning |
| title_full_unstemmed | Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning |
| title_short | Retrieval of Dissolved Oxygen Concentrations in Fishponds in the Guangdong–Hong Kong–Macao Greater Bay Area Using Satellite Imagery and Machine Learning |
| title_sort | retrieval of dissolved oxygen concentrations in fishponds in the guangdong hong kong macao greater bay area using satellite imagery and machine learning |
| topic | fishponds dissolved oxygen satellite images machine learning Guangdong–Hong Kong–Macao Greater Bay area |
| url | https://www.mdpi.com/2072-4292/17/13/2277 |
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