Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks
Total phosphorus (TP) and total nitrogen (TN) represent the primary water quality parameters indicative of the eutrophication status in the mainstream of the Yangtze River. Nowadays, satellite remote sensing offers an economical and efficient method for monitoring the water environment with a broad...
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MDPI AG
2023-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/21/3802 |
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| author | Yuanyuan Yang Shuanggen Jin |
| author_facet | Yuanyuan Yang Shuanggen Jin |
| author_sort | Yuanyuan Yang |
| collection | DOAJ |
| description | Total phosphorus (TP) and total nitrogen (TN) represent the primary water quality parameters indicative of the eutrophication status in the mainstream of the Yangtze River. Nowadays, satellite remote sensing offers an economical and efficient method for monitoring the water environment with a broad geographical scope, while single satellite and traditional methods are still limited. In this paper, inversion models of TN and TP are constructed and evaluated based on the neural networks (NNs) algorithm and random forest (RF) algorithm in the upper, middle, and lower reaches of the Yangtze River, respectively. Subsequently, the monthly variations of TN and TP concentrations are estimated and analyzed in the mainstream of the Yangtze River using Landsat-8 and Sentinel-2 satellites images from January 2016 to December 2022. The results show that the NNs model exhibits better estimation performance than the RF model within the study area. The accuracy of the TN model varies across different sections, with R<sup>2</sup> values of 0.70 in the upstream, 0.67 in the midstream, and 0.74 in the downstream, accompanied by respective RMSE values of 0.21 mg/L, 0.21 mg/L, and 0.23 mg/L. Similarly, the TP model exhibits varying accuracy in different sections, with R<sup>2</sup> values of 0.71 in the upstream, 0.69 in the midstream, and 0.78 in the downstream, along with corresponding RMSE values of 0.008 mg/L, 0.012 mg/L, and 0.008 mg/L. From 2016 to 2022, the concentrations of TN and TP in the mainstream of the Yangtze River exhibited an overall downward trend, with TN decreasing by 13.7% and TP decreasing by 46.2%. Furthermore, this study also gives the possible causes of water quality changes in the mainstream of the Yangtze River with a specific focus on hydrometeorological factors. |
| first_indexed | 2024-03-11T11:18:05Z |
| format | Article |
| id | doaj.art-092df5232d7f4a068f1bea49cc506cd7 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T11:18:05Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
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| series | Water |
| spelling | doaj.art-092df5232d7f4a068f1bea49cc506cd72023-11-10T15:15:23ZengMDPI AGWater2073-44412023-10-011521380210.3390/w15213802Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural NetworksYuanyuan Yang0Shuanggen Jin1School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaTotal phosphorus (TP) and total nitrogen (TN) represent the primary water quality parameters indicative of the eutrophication status in the mainstream of the Yangtze River. Nowadays, satellite remote sensing offers an economical and efficient method for monitoring the water environment with a broad geographical scope, while single satellite and traditional methods are still limited. In this paper, inversion models of TN and TP are constructed and evaluated based on the neural networks (NNs) algorithm and random forest (RF) algorithm in the upper, middle, and lower reaches of the Yangtze River, respectively. Subsequently, the monthly variations of TN and TP concentrations are estimated and analyzed in the mainstream of the Yangtze River using Landsat-8 and Sentinel-2 satellites images from January 2016 to December 2022. The results show that the NNs model exhibits better estimation performance than the RF model within the study area. The accuracy of the TN model varies across different sections, with R<sup>2</sup> values of 0.70 in the upstream, 0.67 in the midstream, and 0.74 in the downstream, accompanied by respective RMSE values of 0.21 mg/L, 0.21 mg/L, and 0.23 mg/L. Similarly, the TP model exhibits varying accuracy in different sections, with R<sup>2</sup> values of 0.71 in the upstream, 0.69 in the midstream, and 0.78 in the downstream, along with corresponding RMSE values of 0.008 mg/L, 0.012 mg/L, and 0.008 mg/L. From 2016 to 2022, the concentrations of TN and TP in the mainstream of the Yangtze River exhibited an overall downward trend, with TN decreasing by 13.7% and TP decreasing by 46.2%. Furthermore, this study also gives the possible causes of water quality changes in the mainstream of the Yangtze River with a specific focus on hydrometeorological factors.https://www.mdpi.com/2073-4441/15/21/3802water qualityYangtze Riverneural networkLandsat-8 OLISentinel-2 MSI |
| spellingShingle | Yuanyuan Yang Shuanggen Jin Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks Water water quality Yangtze River neural network Landsat-8 OLI Sentinel-2 MSI |
| title | Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks |
| title_full | Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks |
| title_fullStr | Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks |
| title_full_unstemmed | Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks |
| title_short | Long-Time Water Quality Variations in the Yangtze River from Landsat-8 and Sentinel-2 Images Based on Neural Networks |
| title_sort | long time water quality variations in the yangtze river from landsat 8 and sentinel 2 images based on neural networks |
| topic | water quality Yangtze River neural network Landsat-8 OLI Sentinel-2 MSI |
| url | https://www.mdpi.com/2073-4441/15/21/3802 |
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