Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network

A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 20...
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Autor*in:	Guiying Yang [verfasserIn] Xiaomin Ye [verfasserIn] Qing Xu [verfasserIn] Xiaobin Yin [verfasserIn] Siyang Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 14, p 3696
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:14, p 3696

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15143696

Katalog-ID:	DOAJ09383232X

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520			\|a A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.
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allfields	10.3390/rs15143696 doi (DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30 DE-627 ger DE-627 rakwb eng Guiying Yang verfasserin aut Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Xiaomin Ye verfasserin aut Qing Xu verfasserin aut Xiaobin Yin verfasserin aut Siyang Xu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 14, p 3696 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:14, p 3696 https://doi.org/10.3390/rs15143696 kostenfrei https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 kostenfrei https://www.mdpi.com/2072-4292/15/14/3696 kostenfrei https://doaj.org/toc/2072-4292 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 14, p 3696
spelling	10.3390/rs15143696 doi (DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30 DE-627 ger DE-627 rakwb eng Guiying Yang verfasserin aut Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Xiaomin Ye verfasserin aut Qing Xu verfasserin aut Xiaobin Yin verfasserin aut Siyang Xu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 14, p 3696 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:14, p 3696 https://doi.org/10.3390/rs15143696 kostenfrei https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 kostenfrei https://www.mdpi.com/2072-4292/15/14/3696 kostenfrei https://doaj.org/toc/2072-4292 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 14, p 3696
allfields_unstemmed	10.3390/rs15143696 doi (DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30 DE-627 ger DE-627 rakwb eng Guiying Yang verfasserin aut Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Xiaomin Ye verfasserin aut Qing Xu verfasserin aut Xiaobin Yin verfasserin aut Siyang Xu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 14, p 3696 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:14, p 3696 https://doi.org/10.3390/rs15143696 kostenfrei https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 kostenfrei https://www.mdpi.com/2072-4292/15/14/3696 kostenfrei https://doaj.org/toc/2072-4292 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 14, p 3696
allfieldsGer	10.3390/rs15143696 doi (DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30 DE-627 ger DE-627 rakwb eng Guiying Yang verfasserin aut Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Xiaomin Ye verfasserin aut Qing Xu verfasserin aut Xiaobin Yin verfasserin aut Siyang Xu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 14, p 3696 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:14, p 3696 https://doi.org/10.3390/rs15143696 kostenfrei https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 kostenfrei https://www.mdpi.com/2072-4292/15/14/3696 kostenfrei https://doaj.org/toc/2072-4292 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 14, p 3696
allfieldsSound	10.3390/rs15143696 doi (DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30 DE-627 ger DE-627 rakwb eng Guiying Yang verfasserin aut Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Xiaomin Ye verfasserin aut Qing Xu verfasserin aut Xiaobin Yin verfasserin aut Siyang Xu verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 14, p 3696 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:14, p 3696 https://doi.org/10.3390/rs15143696 kostenfrei https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 kostenfrei https://www.mdpi.com/2072-4292/15/14/3696 kostenfrei https://doaj.org/toc/2072-4292 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 14, p 3696
language	English
source	In Remote Sensing 15(2023), 14, p 3696 volume:15 year:2023 number:14, p 3696
sourceStr	In Remote Sensing 15(2023), 14, p 3696 volume:15 year:2023 number:14, p 3696
format_phy_str_mv	Article
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topic_facet	sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q
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authorswithroles_txt_mv	Guiying Yang @@aut@@ Xiaomin Ye @@aut@@ Qing Xu @@aut@@ Xiaobin Yin @@aut@@ Siyang Xu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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author	Guiying Yang
spellingShingle	Guiying Yang misc sea surface chlorophyll-a concentration (Chl-a) misc Chinese ocean color and temperature scanner (COCTS) misc HY-1C misc residual neural network misc Science misc Q Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
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topic_title	Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network
topic	misc sea surface chlorophyll-a concentration (Chl-a) misc Chinese ocean color and temperature scanner (COCTS) misc HY-1C misc residual neural network misc Science misc Q
topic_unstemmed	misc sea surface chlorophyll-a concentration (Chl-a) misc Chinese ocean color and temperature scanner (COCTS) misc HY-1C misc residual neural network misc Science misc Q
topic_browse	misc sea surface chlorophyll-a concentration (Chl-a) misc Chinese ocean color and temperature scanner (COCTS) misc HY-1C misc residual neural network misc Science misc Q
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title	Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
ctrlnum	(DE-627)DOAJ09383232X (DE-599)DOAJ3937ef4779e94c5bb013a19e8f821b30
title_full	Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
author_sort	Guiying Yang
journal	Remote Sensing
journalStr	Remote Sensing
lang_code	eng
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author_browse	Guiying Yang Xiaomin Ye Qing Xu Xiaobin Yin Siyang Xu
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format_se	Elektronische Aufsätze
author-letter	Guiying Yang
doi_str_mv	10.3390/rs15143696
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title_sort	sea surface chlorophyll-a concentration retrieval from hy-1c satellite data based on residual network
title_auth	Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
abstract	A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.
abstractGer	A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.
abstract_unstemmed	A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R<sub<rs</sub<) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R<sub<rs</sub<565 and R<sub<rs</sub<520/Rrs443, R<sub<rs</sub<565/R<sub<rs</sub<490, R<sub<rs</sub<520/R<sub<rs</sub<490, R<sub<rs</sub<490/R<sub<rs</sub<443, and R<sub<rs</sub<670/R<sub<rs</sub<565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m<sup<3</sup<, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.
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title_short	Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
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up_date	2024-07-03T19:38:55.743Z
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