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 rs ) 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...
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ftdoajarticles:oai:doaj.org/article:3937ef4779e94c5bb013a19e8f821b30 2023-08-20T03:59:11+02:00 Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network Guiying Yang Xiaomin Ye Qing Xu Xiaobin Yin Siyang Xu 2023-07-01T00:00:00Z https://doi.org/10.3390/rs15143696 https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 EN eng MDPI AG https://www.mdpi.com/2072-4292/15/14/3696 https://doaj.org/toc/2072-4292 doi:10.3390/rs15143696 2072-4292 https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 Remote Sensing, Vol 15, Iss 3696, p 3696 (2023) sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15143696 2023-07-30T00:34:47Z A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R rs ) 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 rs 565 and R rs 520/Rrs443, R rs 565/R rs 490, R rs 520/R rs 490, R rs 490/R rs 443, and R rs 670/R rs 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 3 , 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. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 15 14 3696 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q |
spellingShingle |
sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q Guiying Yang Xiaomin Ye Qing Xu Xiaobin Yin Siyang Xu Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
topic_facet |
sea surface chlorophyll-a concentration (Chl-a) Chinese ocean color and temperature scanner (COCTS) HY-1C residual neural network Science Q |
description |
A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R rs ) 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 rs 565 and R rs 520/Rrs443, R rs 565/R rs 490, R rs 520/R rs 490, R rs 490/R rs 443, and R rs 670/R rs 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 3 , 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. |
format |
Article in Journal/Newspaper |
author |
Guiying Yang Xiaomin Ye Qing Xu Xiaobin Yin Siyang Xu |
author_facet |
Guiying Yang Xiaomin Ye Qing Xu Xiaobin Yin Siyang Xu |
author_sort |
Guiying Yang |
title |
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
title_short |
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
title_full |
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
title_fullStr |
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
title_full_unstemmed |
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network |
title_sort |
sea surface chlorophyll-a concentration retrieval from hy-1c satellite data based on residual network |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15143696 https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 15, Iss 3696, p 3696 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/14/3696 https://doaj.org/toc/2072-4292 doi:10.3390/rs15143696 2072-4292 https://doaj.org/article/3937ef4779e94c5bb013a19e8f821b30 |
op_doi |
https://doi.org/10.3390/rs15143696 |
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Remote Sensing |
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15 |
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14 |
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3696 |
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