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

Gespeichert in:

Autor*in:	Qi Cao [verfasserIn] Gongliang Yu [verfasserIn] Shengjie Sun [verfasserIn] Yong Dou [verfasserIn] Hua Li [verfasserIn] Zhiyi Qiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	ground-based remote sensing hyperspectral water quality BP neural network Haihe River

Übergeordnetes Werk:	In: Water - MDPI AG, 2010, 14(2021), 1, p 22
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:1, p 22

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/w14010022

Katalog-ID:	DOAJ084856076

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ084856076
003	DE-627
005	20240414215555.0
007	cr uuu---uuuuu
008	230311s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/w14010022 \|2 doi
035			\|a (DE-627)DOAJ084856076
035			\|a (DE-599)DOAJ535a3fad444544379295bac903869714
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TC1-978
050		0	\|a TD201-500
100	0		\|a Qi Cao \|e verfasserin \|4 aut
245	1	0	\|a Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
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.
650		4	\|a ground-based remote sensing
650		4	\|a hyperspectral
650		4	\|a water quality
650		4	\|a BP neural network
650		4	\|a Haihe River
653		0	\|a Hydraulic engineering
653		0	\|a Water supply for domestic and industrial purposes
700	0		\|a Gongliang Yu \|e verfasserin \|4 aut
700	0		\|a Shengjie Sun \|e verfasserin \|4 aut
700	0		\|a Yong Dou \|e verfasserin \|4 aut
700	0		\|a Hua Li \|e verfasserin \|4 aut
700	0		\|a Zhiyi Qiao \|e verfasserin \|4 aut
773	0	8	\|i In \|t Water \|d MDPI AG, 2010 \|g 14(2021), 1, p 22 \|w (DE-627)611729008 \|w (DE-600)2521238-2 \|x 20734441 \|7 nnns
773	1	8	\|g volume:14 \|g year:2021 \|g number:1, p 22
856	4	0	\|u https://doi.org/10.3390/w14010022 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/535a3fad444544379295bac903869714 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2073-4441/14/1/22 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2073-4441 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 14 \|j 2021 \|e 1, p 22

Indexfelder

author_variant	q c qc g y gy s s ss y d yd h l hl z q zq
matchkey_str	article:20734441:2021----::oioigaeqaiyfhhieiebsdnrudaehpr
hierarchy_sort_str	2021
callnumber-subject-code	TC
publishDate	2021
allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	In Water 14(2021), 1, p 22 volume:14 year:2021 number:1, p 22
sourceStr	In Water 14(2021), 1, p 22 volume:14 year:2021 number:1, p 22
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	ground-based remote sensing hyperspectral water quality BP neural network Haihe River Hydraulic engineering Water supply for domestic and industrial purposes
isfreeaccess_bool	true
container_title	Water
authorswithroles_txt_mv	Qi Cao @@aut@@ Gongliang Yu @@aut@@ Shengjie Sun @@aut@@ Yong Dou @@aut@@ Hua Li @@aut@@ Zhiyi Qiao @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	611729008
id	DOAJ084856076
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ084856076</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414215555.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/w14010022</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ084856076</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ535a3fad444544379295bac903869714</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1-978</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD201-500</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qi Cao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ground-based remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hyperspectral</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">water quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Haihe River</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Hydraulic engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Water supply for domestic and industrial purposes</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gongliang Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shengjie Sun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yong Dou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hua Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhiyi Qiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Water</subfield><subfield code="d">MDPI AG, 2010</subfield><subfield code="g">14(2021), 1, p 22</subfield><subfield code="w">(DE-627)611729008</subfield><subfield code="w">(DE-600)2521238-2</subfield><subfield code="x">20734441</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1, p 22</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/w14010022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/535a3fad444544379295bac903869714</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2073-4441/14/1/22</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4441</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2021</subfield><subfield code="e">1, p 22</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Qi Cao
spellingShingle	Qi Cao misc TC1-978 misc TD201-500 misc ground-based remote sensing misc hyperspectral misc water quality misc BP neural network misc Haihe River misc Hydraulic engineering misc Water supply for domestic and industrial purposes Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
authorStr	Qi Cao
ppnlink_with_tag_str_mv	@@773@@(DE-627)611729008
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TC1-978
illustrated	Not Illustrated
issn	20734441
topic_title	TC1-978 TD201-500 Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing ground-based remote sensing hyperspectral water quality BP neural network Haihe River
topic	misc TC1-978 misc TD201-500 misc ground-based remote sensing misc hyperspectral misc water quality misc BP neural network misc Haihe River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_unstemmed	misc TC1-978 misc TD201-500 misc ground-based remote sensing misc hyperspectral misc water quality misc BP neural network misc Haihe River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
topic_browse	misc TC1-978 misc TD201-500 misc ground-based remote sensing misc hyperspectral misc water quality misc BP neural network misc Haihe River misc Hydraulic engineering misc Water supply for domestic and industrial purposes
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Water
hierarchy_parent_id	611729008
hierarchy_top_title	Water
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)611729008 (DE-600)2521238-2
title	Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
ctrlnum	(DE-627)DOAJ084856076 (DE-599)DOAJ535a3fad444544379295bac903869714
title_full	Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
author_sort	Qi Cao
journal	Water
journalStr	Water
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
author_browse	Qi Cao Gongliang Yu Shengjie Sun Yong Dou Hua Li Zhiyi Qiao
container_volume	14
class	TC1-978 TD201-500
format_se	Elektronische Aufsätze
author-letter	Qi Cao
doi_str_mv	10.3390/w14010022
author2-role	verfasserin
title_sort	monitoring water quality of the haihe river based on ground-based hyperspectral remote sensing
callnumber	TC1-978
title_auth	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.
collection_details	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
container_issue	1, p 22
title_short	Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing
url	https://doi.org/10.3390/w14010022 https://doaj.org/article/535a3fad444544379295bac903869714 https://www.mdpi.com/2073-4441/14/1/22 https://doaj.org/toc/2073-4441
remote_bool	true
author2	Gongliang Yu Shengjie Sun Yong Dou Hua Li Zhiyi Qiao
author2Str	Gongliang Yu Shengjie Sun Yong Dou Hua Li Zhiyi Qiao
ppnlink	611729008
callnumber-subject	TC - Hydraulic and Ocean Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/w14010022
callnumber-a	TC1-978
up_date	2024-07-04T00:50:49.068Z
_version_	1803607604278067200
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ084856076</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414215555.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230311s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/w14010022</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ084856076</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ535a3fad444544379295bac903869714</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TC1-978</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TD201-500</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Qi Cao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ground-based remote sensing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hyperspectral</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">water quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BP neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Haihe River</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Hydraulic engineering</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Water supply for domestic and industrial purposes</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gongliang Yu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shengjie Sun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yong Dou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hua Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhiyi Qiao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Water</subfield><subfield code="d">MDPI AG, 2010</subfield><subfield code="g">14(2021), 1, p 22</subfield><subfield code="w">(DE-627)611729008</subfield><subfield code="w">(DE-600)2521238-2</subfield><subfield code="x">20734441</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:1, p 22</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/w14010022</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/535a3fad444544379295bac903869714</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2073-4441/14/1/22</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2073-4441</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2021</subfield><subfield code="e">1, p 22</subfield></datafield></record></collection>
score	7.400717

Nicht das Richtige dabei?

Schreiben Sie uns!

Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?