Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion
Remote sensing technology for water environments can invert water quality parameters based on the response relationships between water quality and remote sensing bands, enabling spatiotemporal dynamic monitoring of large-scale water bodies. This study focused on the Pearl River in Guangzhou and anal...
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Editorial Office of Pearl River
2025-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails?columnId=79677288&Fpath=home&index=0 |
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| author | LIU Mengyao FENG Dewang |
| author_facet | LIU Mengyao FENG Dewang |
| author_sort | LIU Mengyao |
| collection | DOAJ |
| description | Remote sensing technology for water environments can invert water quality parameters based on the response relationships between water quality and remote sensing bands, enabling spatiotemporal dynamic monitoring of large-scale water bodies. This study focused on the Pearl River in Guangzhou and analyzed Sentinel-2 remote sensing data and measured data from state-controlled sections. By identifying the optimal band factors, water quality parameter inversion models were constructed based on a variety of statistical regression models, and their inversion accuracies were compared. The inversion model with the highest accuracy was used, and the concentrations of dissolved oxygen (DO), permanganate (COD<sub>Mn</sub>), total phosphorus (TP), total nitrogen (TN), and turbidity (NTU) of the Pearl River in Guangzhou were inverted to analyze the overall water quality. The results indicate that the correlation between the normalized band factor in a single band and the subtracted band factor in a multi-band combination is the strongest, followed by the ratio band factor in a multi-band combination. Among the five inversion models of water quality parameters, TN achieves the highest inversion accuracy with an <italic>R</italic><sup>2</sup> of 0.565, followed by COD<sub>Mn</sub>, NTU, and TP, with <italic>R</italic><sup>2</sup> values of 0.546, 0.529, and 0.446, respectively. DO exhibits the lowest inversion accuracy with an <italic>R</italic><sup>2</sup> of 0.329. Spatial distribution analysis of water quality parameters reveals significant water quality issues in the front, west, and back channels of the Pearl River, as well as in the northern mainstream of the Dongjiang River and near the Shiziyang Waterway. |
| format | Article |
| id | doaj-art-ef1f678963144df29e2bf237bfce9aba |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-ef1f678963144df29e2bf237bfce9aba2025-01-15T03:08:41ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352025-01-0111179677288Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image InversionLIU MengyaoFENG DewangRemote sensing technology for water environments can invert water quality parameters based on the response relationships between water quality and remote sensing bands, enabling spatiotemporal dynamic monitoring of large-scale water bodies. This study focused on the Pearl River in Guangzhou and analyzed Sentinel-2 remote sensing data and measured data from state-controlled sections. By identifying the optimal band factors, water quality parameter inversion models were constructed based on a variety of statistical regression models, and their inversion accuracies were compared. The inversion model with the highest accuracy was used, and the concentrations of dissolved oxygen (DO), permanganate (COD<sub>Mn</sub>), total phosphorus (TP), total nitrogen (TN), and turbidity (NTU) of the Pearl River in Guangzhou were inverted to analyze the overall water quality. The results indicate that the correlation between the normalized band factor in a single band and the subtracted band factor in a multi-band combination is the strongest, followed by the ratio band factor in a multi-band combination. Among the five inversion models of water quality parameters, TN achieves the highest inversion accuracy with an <italic>R</italic><sup>2</sup> of 0.565, followed by COD<sub>Mn</sub>, NTU, and TP, with <italic>R</italic><sup>2</sup> values of 0.546, 0.529, and 0.446, respectively. DO exhibits the lowest inversion accuracy with an <italic>R</italic><sup>2</sup> of 0.329. Spatial distribution analysis of water quality parameters reveals significant water quality issues in the front, west, and back channels of the Pearl River, as well as in the northern mainstream of the Dongjiang River and near the Shiziyang Waterway.http://www.renminzhujiang.cn/thesisDetails?columnId=79677288&Fpath=home&index=0remote sensing imageinversionwater quality parameter |
| spellingShingle | LIU Mengyao FENG Dewang Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion Renmin Zhujiang remote sensing image inversion water quality parameter |
| title | Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion |
| title_full | Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion |
| title_fullStr | Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion |
| title_full_unstemmed | Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion |
| title_short | Spatiotemporal Characteristics of Pearl River Water Environment in Guangzhou Based on Remote Sensing Image Inversion |
| title_sort | spatiotemporal characteristics of pearl river water environment in guangzhou based on remote sensing image inversion |
| topic | remote sensing image inversion water quality parameter |
| url | http://www.renminzhujiang.cn/thesisDetails?columnId=79677288&Fpath=home&index=0 |
| work_keys_str_mv | AT liumengyao spatiotemporalcharacteristicsofpearlriverwaterenvironmentinguangzhoubasedonremotesensingimageinversion AT fengdewang spatiotemporalcharacteristicsofpearlriverwaterenvironmentinguangzhoubasedonremotesensingimageinversion |