{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:51:21Z","timestamp":1780462281566,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFC3104900\/2022YFC31 04905"],"award-info":[{"award-number":["2022YFC3104900\/2022YFC31 04905"]}]},{"name":"National Key R&amp;D Program of China","award":["122CXTD519"],"award-info":[{"award-number":["122CXTD519"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["2022YFC3104900\/2022YFC31 04905"],"award-info":[{"award-number":["2022YFC3104900\/2022YFC31 04905"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["122CXTD519"],"award-info":[{"award-number":["122CXTD519"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Fengyun-4A (FY-4A) satellite is a new-generation geostationary meteorological satellite developed by China. The advanced geosynchronous radiation imager (AGRI), one of the key payloads onboard FY-4A, can monitor sea surface temperature (SST). This paper compares FY-4A\/AGRI SST with in situ and Himawari-8\/advanced Himawari imager (AHI) SST. The study area spans 30\u00b0E\u2013180\u00b0E, 60\u00b0S\u201360\u00b0N, and the study period is from January 2019 to December 2021. The matching time window of the three data is 30 min, and the space window is 0.1\u00b0. The quality control criterion is to select all clear sky and well-distributed matchups within the study period, removing the influence of SST fronts. The results of the difference between FY-4A\/AGRI and in situ SST show a bias of \u22120.12 \u00b0C, median of \u22120.05 \u00b0C, standard deviation (STD) of 0.76 \u00b0C, robust standard deviation (RSD) of 0.68 \u00b0C, and root mean square error (RMSE) of 0.77 \u00b0C for daytime and a bias of 0.00 \u00b0C, median of 0.05 \u00b0C, STD of 0.78 \u00b0C, RSD of 0.72 \u00b0C, and RMSE of 0.78 \u00b0C for nighttime. The results of the difference between FY-4A\/AGRI SST and Himawari-8\/AHI SST show a bias of 0.04 \u00b0C, median of 0.10 \u00b0C, STD of 0.78 \u00b0C, RSD of 0.70 \u00b0C, and RMSE of 0.78 \u00b0C for daytime and the bias of 0.30 \u00b0C, median of 0.34 \u00b0C, STD of 0.81 \u00b0C, RSD of 0.76 \u00b0C, and RMSE of 0.86 \u00b0C for nighttime. The three-way error analysis also indicates a relatively larger error of AGRI SST. Regarding timescale, the bias and STD of FY-4A\/AGRI SST show no seasonal correlation, but FY-4A\/AGRI SST has a noticeable bias jump in the study period. Regarding spatial scale, FY-4A\/AGRI SST shows negative bias at the edge of the AGRI SST coverage in the Pacific region near 160\u00b0E longitude and positive bias in high latitudes of the southern hemisphere. The accuracy of FY-4A\/AGRI SST depends on the satellite zenith angle and water vapor. Further research on the FY-4A\/AGRI SST retrieval algorithm accounting for the variability of water vapor will be conducted.<\/jats:p>","DOI":"10.3390\/rs15174139","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"4139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Comparison of FY-4A\/AGRI SST with Himawari-8\/AHI and In Situ SST"],"prefix":"10.3390","volume":"15","author":[{"given":"Chang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9902-8035","authenticated-orcid":false,"given":"Lei","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"},{"name":"Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China"},{"name":"Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya 572024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"},{"name":"Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya 572024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1146\/annurev.marine.010908.163742","article-title":"Advances in quantifying air-sea gas exchange and environmental forcing","volume":"1","author":"Wanninkhof","year":"2009","journal-title":"Ann. Rev. Mar. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1109\/TGRS.2013.2280494","article-title":"Segmentation of mesoscale ocean surface dynamics using satellite SST and SSH observations","volume":"52","author":"Tandeo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Martin, S. (2014). An Introduction to Ocean Remote Sensing, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9781139094368"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8007","DOI":"10.1029\/JC086iC09p08007","article-title":"Application of GOES visible-infrared data to quantifying mesoscale ocean surface temperatures","volume":"86","author":"Maul","year":"1981","journal-title":"J. Geophys. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11609","DOI":"10.1029\/JC090iC06p11609","article-title":"Sea surface temperature: Observations from geostationary satellites","volume":"90","author":"Bates","year":"1985","journal-title":"J. Geophys. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111366","DOI":"10.1016\/j.rse.2019.111366","article-title":"Half a century of satellite remote sensing of sea-surface temperature","volume":"233","author":"Minnett","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1175\/2008BAMS2528.1","article-title":"NOAA\u2019s sea surface temperature products from operational geostationary satellites","volume":"89","author":"Maturi","year":"2008","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1175\/2008JTECHO596.1","article-title":"Sea Surface Temperature Estimation from the Geostationary Operational Environmental Satellite-12 (GOES-12)","volume":"26","author":"Merchant","year":"2009","journal-title":"J. Atmos. Oceanic Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Azevedo, M.H., Rudorff, N., and Arav\u00e9quia, J.A. (2021). Evaluation of the ABI\/GOES-16 SST Product in the Tropical and Southwestern Atlantic Ocean. Remote Sens., 13.","DOI":"10.3390\/rs13020192"},{"key":"ref_10","unstructured":"Ignatov, A. (2023, August 20). GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Sea Surface Temperature. NOAA NESDIS Center for Satellite Applications and Research, Available online: https:\/\/www.star.nesdis.noaa.gov\/goesr\/documents\/ATBDs\/Baseline\/ATBD_GOES-R_SST-v2.0_Aug2010.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1175\/JTECH-D-15-0166.1","article-title":"Sensor-specific error statistics for SST in the advanced clear-sky processor for oceans","volume":"33","author":"Petrenko","year":"2016","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9902","DOI":"10.1109\/TGRS.2021.3054895","article-title":"Skin sea surface temperatures from the GOES-16 ABI validated with those of the shipborne M-AERI","volume":"59","author":"Luo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1023\/A:1011111902601","article-title":"Improved estimates of wide-ranging sea surface temperature from GMS S-VISSR data","volume":"56","author":"Tanahashi","year":"2000","journal-title":"J. Oceanogr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10872-010-0005-0","article-title":"Hourly sea surface temperature retrieval using the Japanese geostationary satellite, multi-functional transport satellite (MTSAT)","volume":"66","author":"Kawamura","year":"2010","journal-title":"J. Oceanogr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ditri, A., Minnett, P., Liu, Y., Kilpatrick, K., and Kumar, A. (2018). The accuracies of Himawari-8 and MTSAT-2 sea-surface temperatures in the tropical Western Pacific Ocean. Remote Sens., 10.","DOI":"10.3390\/rs10020212"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"151","DOI":"10.2151\/jmsj.2016-009","article-title":"An introduction to Himawari-8\/9\u2014Japan\u2019s new-generation geostationary meteorological satellites","volume":"94","author":"Bessho","year":"2016","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1002\/2015GL067159","article-title":"Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite","volume":"43","author":"Kurihara","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kramar, M., Ignatov, A., Petrenko, B., Kihai, Y., and Dash, P. (2016, January 17\u201321). Near Real Time SST Retrievals from Himawari-8 at NOAA Using ACSPO System. Proceedings of the Ocean Sensing and Monitoring VIII SPIE, Baltimore, MD, USA. Available online: https:\/\/www.star.nesdis.noaa.gov\/pub\/sod\/osb\/mkramar\/paper_SPIE16\/07\/SPIE_Proc_9827-21_Kramar.pdf.","DOI":"10.1117\/12.2229771"},{"key":"ref_19","first-page":"245","article-title":"Ch 14: Temperature","volume":"Volume 3B","author":"Harrison","year":"2020","journal-title":"Earth Observation: Data Processing and Applications."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"012038","DOI":"10.1088\/1755-1315\/98\/1\/012038","article-title":"Sea surface temperature dynamics in Indonesia","volume":"98","author":"Kusuma","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_21","first-page":"25","article-title":"Three-way error analysis of sea surface temperature (SST) between Himawari-8, buoy, and MUR SST in Savu Sea","volume":"15","author":"Sukresno","year":"2018","journal-title":"Int. J. Remote Sens. Earth Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, M.L., Guan, L., Beggs, H., Morgan, N., Kurihara, Y., and Kachi, M. (2020). Comparison of Himawari-8 AHI SST with shipboard skin SST measurements in the Australian region. Remote Sens., 12.","DOI":"10.3390\/rs12081237"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/JSTARS.2019.2963773","article-title":"Validation of sea surface temperature derived from Himawari-8 by JAXA","volume":"13","author":"Tu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","first-page":"249","article-title":"Retrieval of sea surface temperature in all sky conditions based on AHI observations","volume":"40","author":"Wang","year":"2020","journal-title":"J. Meteorol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hsu, P.C. (2022). Evaluation of wind and solar insolation influence on ocean near-surface temperature from in situ observations and the geostationary Himawari-8 satellite. Remote Sens., 14.","DOI":"10.3390\/rs14194975"},{"key":"ref_26","first-page":"15","article-title":"MSG\u2019s SEVIRI Instrument","volume":"111","author":"Aminou","year":"2002","journal-title":"ESA Bull."},{"key":"ref_27","unstructured":"Schmid, J. (June, January 29). The SEVIRI Instrument. Proceedings of the 2000 EUMETSAT Meteorological Satellite Data User\u2019s Conference, Bologna, Italy. Available online: https:\/\/www-cdn.eumetsat.int\/files\/2020-04\/pdf_ten_msg_seviri_instrument.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.rse.2012.06.015","article-title":"Comparison of MSG\/SEVIRI and drifting buoy derived diurnal warming estimates","volume":"124","author":"Legendre","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.rse.2013.08.042","article-title":"Comparison of diurnal warming estimates from unpumped Argo data and SEVIRI satellite observations","volume":"140","author":"Castro","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1080\/01431161.2017.1407051","article-title":"INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent","volume":"39","author":"Tyagi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, H.J., Park, K.A., Li, X., and Lee, E.Y. (2018). Sea surface temperature retrieval from the first Korean geostationary satellite COMS data: Validation and error assessment. Remote Sens., 10.","DOI":"10.3390\/rs10121916"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6132","DOI":"10.1109\/TGRS.2013.2295260","article-title":"Retrieval of sea and land surface temperature from SVISSR\/FY-2C\/D\/E measurements","volume":"52","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4","volume":"98","author":"Yang","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_34","first-page":"1","article-title":"The Chinese next-generation geostationary meteorological satellite FY-4 compared with the Japanese himawari-8\/9 satellites","volume":"6","author":"Zhang","year":"2015","journal-title":"Adv. Meteorol. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1007\/s13351-017-6161-z","article-title":"Developing the scienceproduct algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series","volume":"31","author":"Min","year":"2017","journal-title":"J. Meteor. Res."},{"key":"ref_36","first-page":"257","article-title":"FY-4A\/AGRI sea surface temperatures product and quality inspection","volume":"34","author":"Cui","year":"2023","journal-title":"J. Appl. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, Q., He, Q., and Chen, C. (2021). Retrieval of daily mean VIIRS SST products in China seas. Remote Sens., 13.","DOI":"10.3390\/rs13245158"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/JSTARS.2022.3225729","article-title":"Evaluation and improvement of FY-4A\/AGRI sea surface temperature data","volume":"16","author":"He","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105502","DOI":"10.1016\/j.atmosres.2021.105502","article-title":"Validation of FY-4A AGRI layer precipitable water products using radiosonde data","volume":"253","author":"Wang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1175\/JTECH-D-13-00121.1","article-title":"In situ SST quality monitor (iQuam)","volume":"31","author":"Xu","year":"2014","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_41","first-page":"1","article-title":"Assessing Radiometric Calibration of FY-4A\/AGRI Thermal Infrared Channels Using CrIS and IASI","volume":"60","author":"He","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1175\/2007JTECHO542.1","article-title":"Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations","volume":"25","author":"Eyre","year":"2008","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1002\/2013JC009716","article-title":"Three way validation of MODIS and AMSR-E sea surface temperatures","volume":"119","author":"Gentemann","year":"2014","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2012.08.006","article-title":"The accuracy of SST retrievals from Metop-A IASI and A VHRR using the EUMETSAT OSI-SAF matchup dataset","volume":"126","author":"August","year":"2012","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:41:05Z","timestamp":1760128865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,23]]},"references-count":44,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174139"],"URL":"https:\/\/doi.org\/10.3390\/rs15174139","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,23]]}}}