{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:41:20Z","timestamp":1768776080879,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sanya Yazhou Bay Science and Technology City","award":["SKJC-2022-01-001"],"award-info":[{"award-number":["SKJC-2022-01-001"]}]},{"name":"Sanya Yazhou Bay Science and Technology City","award":["T2261149752"],"award-info":[{"award-number":["T2261149752"]}]},{"name":"Sanya Yazhou Bay Science and Technology City","award":["41976163"],"award-info":[{"award-number":["41976163"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SKJC-2022-01-001"],"award-info":[{"award-number":["SKJC-2022-01-001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2261149752"],"award-info":[{"award-number":["T2261149752"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41976163"],"award-info":[{"award-number":["41976163"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) 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 Rrs565 and Rrs520\/Rrs443, Rrs565\/Rrs490, Rrs520\/Rrs490, Rrs490\/Rrs443, and Rrs670\/Rrs565 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\/m3, 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.<\/jats:p>","DOI":"10.3390\/rs15143696","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T01:27:32Z","timestamp":1690248452000},"page":"3696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Guiying","family":"Yang","sequence":"first","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Xiaomin","family":"Ye","sequence":"additional","affiliation":[{"name":"The National Satellite Ocean Application Service, Ministry of Natural Resources of the People\u2019s Republic of China, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0214-744X","authenticated-orcid":false,"given":"Qing","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Xiaobin","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}]},{"given":"Siyang","family":"Xu","sequence":"additional","affiliation":[{"name":"China Shipbuilding Industry Corporation No. 722 Institute, Wuhan 430200, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Characteristics of an Open Complex Giant System-Carbon Cycling System in the Ocean","volume":"1","author":"Yang","year":"2004","journal-title":"Complex Syst. Complex. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Silveira Kupssinsku, L., Thomassim Guimaraes, T., Menezes de Souza, E., Zanotta, D.C., Roberto Veronez, M., Gonzaga, L., and Mauad, F.F. (2020). A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning. Sensors, 20.","DOI":"10.3390\/s20072125"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.3390\/rs4082373","article-title":"Comparative Analysis of Four Models to Estimate Chlorophyll-a Concentration in Case-2 Waters Using MODerate Resolution Imaging Spectroradiometer (MODIS) Imagery","volume":"4","author":"Chokmani","year":"2012","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Clay, S., Pena, A., DeTracey, B., and Devred, E. (2019). Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans. Remote Sens., 11.","DOI":"10.3390\/rs11222609"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20920","DOI":"10.1364\/OE.20.020920","article-title":"Under the hood of satellite empirical chlorophyll a algorithms: Revealing the dependencies of maximum band ratio algorithms on inherent optical properties","volume":"20","author":"Sauer","year":"2012","journal-title":"Opt. Express."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1029\/2002JC001638","article-title":"Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters","volume":"108","author":"Zhang","year":"2003","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Syariz, M.A., Lin, C.-H., Nguyen, M.V., Jaelani, L.M., and Blanco, A.C. (2020). WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval. Remote Sens., 12.","DOI":"10.3390\/rs12121966"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ali, K.A., and Moses, W.J. (2022). Application of a PLS-Augmented ANN Model for Retrieving Chlorophyll-a from Hyperspectral Data in Case 2 Waters of the Western Basin of Lake Erie. Remote Sens., 14.","DOI":"10.3390\/rs14153729"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"034520","DOI":"10.1117\/1.JRS.14.034520","article-title":"Global chlorophyll-a concentration estimation from moderate resolution imaging spectroradiometer using convolutional neural networks","volume":"14","author":"Yu","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ye, H., Tang, S., and Yang, C. (2021). Deep Learning for Chlorophyll-a Concentration Retrieval: A Case Study for the Pearl River Estuary. Remote Sens., 13.","DOI":"10.3390\/rs13183717"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9914","DOI":"10.1109\/TGRS.2020.3036963","article-title":"Global Ocean Chlorophyll-a Concentrations Derived From COCTS Onboard the HY-1C Satellite and Their Preliminary Evaluation","volume":"59","author":"Ye","year":"2021","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"A1615","DOI":"10.1364\/OE.27.0A1615","article-title":"Vicarious calibration of COCTS-HY1C at visible and near-infrared bands for ocean color application","volume":"27","author":"Song","year":"2019","journal-title":"Opt. Express."},{"key":"ref_13","unstructured":"Sathyendranath, S., Jackson, T., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Clements, O., Cipollini, P., Danne, O., and Dingle, J. (2023, March 15). ESA Ocean Colour Climate Change Initiative (Ocean_Colour_CCI): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 2021. Available online: http:\/\/climate.esa.int\/en\/projects\/ocean-colour\/key-documents\/."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gordon, H., and Morel, A. (1983). Remote assessment of ocean color for interpretation of satellite visible imagery: A review. Phys. Earth Planet. Int., 37.","DOI":"10.1029\/LN004"},{"key":"ref_15","first-page":"937","article-title":"Ocean color chlorophyll algorithms for SeaWiFS","volume":"103","author":"Maritorena","year":"1998","journal-title":"J. Geophys. Res. C."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"C01011","DOI":"10.1029\/2011JC007395","article-title":"Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference","volume":"117","author":"Hu","year":"2012","journal-title":"J. Geophys. Res. C Oceans"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ferreira, A., Brotas, V., Palma, C., Borges, C., and Brito, A.C. (2021). Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13040675"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1038\/s41561-022-01057-3","article-title":"Annual variations in phytoplankton biomass driven by small-scale physical processes","volume":"15","author":"Keerthi","year":"2022","journal-title":"Nat. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"481","DOI":"10.5194\/essd-13-481-2021","article-title":"Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data","volume":"13","author":"Pitarch","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_20","first-page":"9","article-title":"Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4","volume":"11","year":"2000","journal-title":"SeaWiFS Postlaunch Calibration Valid. Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1016\/j.rse.2009.07.013","article-title":"Validation of satellite ocean color primary products at optically complex coastal sites: Northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland","volume":"113","author":"Zibordi","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"275","DOI":"10.5194\/os-11-275-2015","article-title":"In situ autonomous optical radiometry measurements for satellite ocean color validation in the Western Black Sea","volume":"11","author":"Zibordi","year":"2015","journal-title":"Ocean Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2006.01.015","article-title":"A multi-sensor approach for the on-orbit validation of ocean color satellite data products","volume":"102","author":"Bailey","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 10). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","unstructured":"Brownlee, J. (2023, June 26). Bagging and Random Forest Ensemble Algorithms for Machine Learning. 2016, pp. 4\u201322. Available online: https:\/\/machinelearningmastery.com\/bagging-and-random-forest-ensemble-algorithms-for-machine-learning\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TNN.2009.2032543","article-title":"Feature Selection for MLP Neural Network: The Use of Random Permutation of Probabilistic Outputs","volume":"20","author":"Yang","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_27","first-page":"1","article-title":"All Models are Wrong, but Many are Useful: Learning a Variable\u2019s Importance by Studying an Entire Class of Prediction Models Simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J. Mach. Learn. Res. JMLR"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.rse.2010.10.014","article-title":"Remote estimation of chlorophyll a in optically complex waters based on optical classification","volume":"115","author":"Le","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.rse.2019.04.027","article-title":"A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types","volume":"229","author":"Neil","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"138","article-title":"A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake","volume":"74","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2019.04.021","article-title":"Chlorophyll Algorithms for Ocean Color Sensors\u2014Oc4, Oc5 & Oc6","volume":"229","author":"Werdell","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Hooker, S., Firestone, E., Mcclain, C., Kwiatkowska, E., Barnes, R., Eplee, R., Elaine, R., Patt, F., Robinson, W., and Wang, M. (2022, August 25). SeaWiFS Postlaunch Calibration and Validation Analyses Part 1. 2000; pp. 4\u201312. Available online: https:\/\/www.researchgate.net\/publication\/24293669_SeaWiFS_Postlaunch_Calibration_and_Validation_Analyses."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2011.11.013","article-title":"Review of constituent retrieval in optically deep and complex waters from satellite imagery","volume":"118","author":"Odermatt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Su, H., Lu, X., Chen, Z., Zhang, H., Lu, W., and Wu, W. (2021). Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040576"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:18:02Z","timestamp":1760127482000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,24]]},"references-count":34,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143696"],"URL":"https:\/\/doi.org\/10.3390\/rs15143696","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,24]]}}}