Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model
This paper establishes a multi-parameter quantitative inversion model of water qualityin multispectral remote sensing images by domestic GF-1 WFV image and neural network model toachieve high-efficiency, large-scale, continuous-space and multi-parameter change monitoring ofreservoir water quality, e...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2020-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.07.009 |
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| author | ZHENG Yanhui ZHANG Yuanbo HE Yanhu |
| author_facet | ZHENG Yanhui ZHANG Yuanbo HE Yanhu |
| author_sort | ZHENG Yanhui |
| collection | DOAJ |
| description | This paper establishes a multi-parameter quantitative inversion model of water qualityin multispectral remote sensing images by domestic GF-1 WFV image and neural network model toachieve high-efficiency, large-scale, continuous-space and multi-parameter change monitoring ofreservoir water quality, explores the application feasibility of domestic satellite image inremotesensing inversion of water quality, and provide technical support for lake chief system and lakeeutrophication assessment. Taking a medium-sized reservoir in Foshan City, Guangdong Province asan example, based on the GF-1 WFV image, a quantitative inversion model between 5 water qualityparameters of Chl-a, SD, TP, TN and COD<sub>Mn</sub>) and image data of Dongfeng Reservoiris established withneural network models, the determination coefficient (R<sup>2</sup>) between the predicted and measuredvalues of the 5 water quality parameters all reached above 0.8, with the average relative errorsof less than 40%. The results confirm the feasibility of domestic satellite image for remotesensing inversion of water quality,which can provide a reference for the water quality andeutrophication monitoring of the reservoir. |
| format | Article |
| id | doaj-art-fe57086922a24904a9ef4e6a02a35dfb |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-fe57086922a24904a9ef4e6a02a35dfb2025-01-15T02:30:58ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352020-01-014147651357Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network ModelZHENG YanhuiZHANG YuanboHE YanhuThis paper establishes a multi-parameter quantitative inversion model of water qualityin multispectral remote sensing images by domestic GF-1 WFV image and neural network model toachieve high-efficiency, large-scale, continuous-space and multi-parameter change monitoring ofreservoir water quality, explores the application feasibility of domestic satellite image inremotesensing inversion of water quality, and provide technical support for lake chief system and lakeeutrophication assessment. Taking a medium-sized reservoir in Foshan City, Guangdong Province asan example, based on the GF-1 WFV image, a quantitative inversion model between 5 water qualityparameters of Chl-a, SD, TP, TN and COD<sub>Mn</sub>) and image data of Dongfeng Reservoiris established withneural network models, the determination coefficient (R<sup>2</sup>) between the predicted and measuredvalues of the 5 water quality parameters all reached above 0.8, with the average relative errorsof less than 40%. The results confirm the feasibility of domestic satellite image for remotesensing inversion of water quality,which can provide a reference for the water quality andeutrophication monitoring of the reservoir.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.07.009multispectralremote-sensing imageneural networkwater quality inversionGF-1WFV |
| spellingShingle | ZHENG Yanhui ZHANG Yuanbo HE Yanhu Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model Renmin Zhujiang multispectral remote-sensing image neural network water quality inversion GF-1WFV |
| title | Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model |
| title_full | Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model |
| title_fullStr | Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model |
| title_full_unstemmed | Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model |
| title_short | Study on the Multi-parameter Inversion of Reservoir Water Quality Based on GF-1 WFV Image and Neural Network Model |
| title_sort | study on the multi parameter inversion of reservoir water quality based on gf 1 wfv image and neural network model |
| topic | multispectral remote-sensing image neural network water quality inversion GF-1WFV |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2020.07.009 |
| work_keys_str_mv | AT zhengyanhui studyonthemultiparameterinversionofreservoirwaterqualitybasedongf1wfvimageandneuralnetworkmodel AT zhangyuanbo studyonthemultiparameterinversionofreservoirwaterqualitybasedongf1wfvimageandneuralnetworkmodel AT heyanhu studyonthemultiparameterinversionofreservoirwaterqualitybasedongf1wfvimageandneuralnetworkmodel |