Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images
Total phosphorus (TP) and total nitrogen (TN) are critical water quality indicators in the Yangtze River and remote sensing techniques can inverse these parameters. However, current models suffer from shortcomings such as lower accuracy due to the fewer spectral bands available from a single satelli...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10829670/ |
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| author | Wentao Hu Shuanggen Jin Yuanyuan Zhang |
| author_facet | Wentao Hu Shuanggen Jin Yuanyuan Zhang |
| author_sort | Wentao Hu |
| collection | DOAJ |
| description | Total phosphorus (TP) and total nitrogen (TN) are critical water quality indicators in the Yangtze River and remote sensing techniques can inverse these parameters. However, current models suffer from shortcomings such as lower accuracy due to the fewer spectral bands available from a single satellite. In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. These models are validated and applied to derive long-term time series of TP and TN in the lower Yangtze River from 2018 to 2023. The results indicate that the three-satellite GA-RF joint model shows the best estimation performance from the in-situ measurements: TP with MAE 0.0108 and RMSE 0.0132, and TN with MAE 0.32 and RMSE 0.40. From 2018 to 2023, the water quality shows an improved trend with TP decreasing by 8.91% and TN decreasing by 11.34% . The annual average TP shows a decreasing trend with 0.0017 mg/L per year, while TN shows a decreasing trend with 0.0557 mg/L per year. In terms of seasonal distribution, the highest values of TP and TN are mostly distributed in summer, and the lowest values are mostly distributed in winter. Spatially, both TP and TN increase from west to east. Furthermore, the effects of hydrometeorological factors on water quality are discussed as well as water environmental factors such as pH and NH3-N. |
| format | Article |
| id | doaj-art-39f44ba02e1b4159a1dd0523c788c3e4 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-39f44ba02e1b4159a1dd0523c788c3e42025-02-12T00:00:18ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184992500410.1109/JSTARS.2025.352620710829670Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 ImagesWentao Hu0https://orcid.org/0009-0001-4740-5596Shuanggen Jin1https://orcid.org/0000-0002-5108-4828Yuanyuan Zhang2https://orcid.org/0000-0001-9429-6305School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, ChinaTotal phosphorus (TP) and total nitrogen (TN) are critical water quality indicators in the Yangtze River and remote sensing techniques can inverse these parameters. However, current models suffer from shortcomings such as lower accuracy due to the fewer spectral bands available from a single satellite. In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. These models are validated and applied to derive long-term time series of TP and TN in the lower Yangtze River from 2018 to 2023. The results indicate that the three-satellite GA-RF joint model shows the best estimation performance from the in-situ measurements: TP with MAE 0.0108 and RMSE 0.0132, and TN with MAE 0.32 and RMSE 0.40. From 2018 to 2023, the water quality shows an improved trend with TP decreasing by 8.91% and TN decreasing by 11.34% . The annual average TP shows a decreasing trend with 0.0017 mg/L per year, while TN shows a decreasing trend with 0.0557 mg/L per year. In terms of seasonal distribution, the highest values of TP and TN are mostly distributed in summer, and the lowest values are mostly distributed in winter. Spatially, both TP and TN increase from west to east. Furthermore, the effects of hydrometeorological factors on water quality are discussed as well as water environmental factors such as pH and NH3-N.https://ieeexplore.ieee.org/document/10829670/Genetic algorithm (GA)machine learningtotal nitrogen (TN)total phosphorus (TP)Yangtze River |
| spellingShingle | Wentao Hu Shuanggen Jin Yuanyuan Zhang Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Genetic algorithm (GA) machine learning total nitrogen (TN) total phosphorus (TP) Yangtze River |
| title | Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images |
| title_full | Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images |
| title_fullStr | Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images |
| title_full_unstemmed | Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images |
| title_short | Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images |
| title_sort | water quality variations in the lower yangtze river based on ga rf model from gf 1 landsat 8 and sentinel 2 images |
| topic | Genetic algorithm (GA) machine learning total nitrogen (TN) total phosphorus (TP) Yangtze River |
| url | https://ieeexplore.ieee.org/document/10829670/ |
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