Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water...
Ausführliche Beschreibung
Autor*in: |
Qi Cao [verfasserIn] Gongliang Yu [verfasserIn] Shengjie Sun [verfasserIn] Yong Dou [verfasserIn] Hua Li [verfasserIn] Zhiyi Qiao [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Übergeordnetes Werk: |
In: Water - MDPI AG, 2010, 14(2021), 1, p 22 |
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Übergeordnetes Werk: |
volume:14 ; year:2021 ; number:1, p 22 |
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DOI / URN: |
10.3390/w14010022 |
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Katalog-ID: |
DOAJ084856076 |
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520 | |a The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. | ||
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10.3390/w14010022 doi (DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Qi Cao verfasserin aut Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes Gongliang Yu verfasserin aut Shengjie Sun verfasserin aut Yong Dou verfasserin aut Hua Li verfasserin aut Zhiyi Qiao verfasserin aut In Water MDPI AG, 2010 14(2021), 1, p 22 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2021 number:1, p 22 https://doi.org/10.3390/w14010022 kostenfrei https://doaj.org/article/535a3fad444544379295bac903869714 kostenfrei https://www.mdpi.com/2073-4441/14/1/22 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 1, p 22 |
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10.3390/w14010022 doi (DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Qi Cao verfasserin aut Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes Gongliang Yu verfasserin aut Shengjie Sun verfasserin aut Yong Dou verfasserin aut Hua Li verfasserin aut Zhiyi Qiao verfasserin aut In Water MDPI AG, 2010 14(2021), 1, p 22 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2021 number:1, p 22 https://doi.org/10.3390/w14010022 kostenfrei https://doaj.org/article/535a3fad444544379295bac903869714 kostenfrei https://www.mdpi.com/2073-4441/14/1/22 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 1, p 22 |
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10.3390/w14010022 doi (DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Qi Cao verfasserin aut Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes Gongliang Yu verfasserin aut Shengjie Sun verfasserin aut Yong Dou verfasserin aut Hua Li verfasserin aut Zhiyi Qiao verfasserin aut In Water MDPI AG, 2010 14(2021), 1, p 22 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2021 number:1, p 22 https://doi.org/10.3390/w14010022 kostenfrei https://doaj.org/article/535a3fad444544379295bac903869714 kostenfrei https://www.mdpi.com/2073-4441/14/1/22 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 1, p 22 |
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10.3390/w14010022 doi (DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Qi Cao verfasserin aut Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes Gongliang Yu verfasserin aut Shengjie Sun verfasserin aut Yong Dou verfasserin aut Hua Li verfasserin aut Zhiyi Qiao verfasserin aut In Water MDPI AG, 2010 14(2021), 1, p 22 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2021 number:1, p 22 https://doi.org/10.3390/w14010022 kostenfrei https://doaj.org/article/535a3fad444544379295bac903869714 kostenfrei https://www.mdpi.com/2073-4441/14/1/22 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 1, p 22 |
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10.3390/w14010022 doi (DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Qi Cao verfasserin aut Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes Gongliang Yu verfasserin aut Shengjie Sun verfasserin aut Yong Dou verfasserin aut Hua Li verfasserin aut Zhiyi Qiao verfasserin aut In Water MDPI AG, 2010 14(2021), 1, p 22 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2021 number:1, p 22 https://doi.org/10.3390/w14010022 kostenfrei https://doaj.org/article/535a3fad444544379295bac903869714 kostenfrei https://www.mdpi.com/2073-4441/14/1/22 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2021 1, p 22 |
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Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing |
abstract |
The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. |
abstractGer |
The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. |
abstract_unstemmed |
The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters. |
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Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing |
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The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH<sub<4</sub<-N), nitrate-nitrogen (NO<sub<3</sub<-N), and pH) were modeled and verified. The results show that the performance R<sup<2</sup< of the training model is above 80%, and the performance R<sup<2</sup< of the verification model is above 70%. In the training model, the highest fitting degree is TN (R<sup<2</sup< = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R<sup<2</sup< = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. 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