{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T06:11:34Z","timestamp":1769235094992,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41906025"],"award-info":[{"award-number":["41906025"]}],"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":["420QN289"],"award-info":[{"award-number":["420QN289"]}],"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":["41876031"],"award-info":[{"award-number":["41876031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["41906025"],"award-info":[{"award-number":["41906025"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["420QN289"],"award-info":[{"award-number":["420QN289"]}]},{"name":"Hainan Provincial Natural Science Foundation of China","award":["41876031"],"award-info":[{"award-number":["41876031"]}]},{"name":"National Natural Science Foundation of China","award":["41906025"],"award-info":[{"award-number":["41906025"]}]},{"name":"National Natural Science Foundation of China","award":["420QN289"],"award-info":[{"award-number":["420QN289"]}]},{"name":"National Natural Science Foundation of China","award":["41876031"],"award-info":[{"award-number":["41876031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015\u2013June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.<\/jats:p>","DOI":"10.3390\/rs15102493","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T01:57:51Z","timestamp":1683683871000},"page":"2493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea"],"prefix":"10.3390","volume":"15","author":[{"given":"Changlong","family":"Feng","sequence":"first","affiliation":[{"name":"College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8931-4975","authenticated-orcid":false,"given":"Wenbin","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China"}]},{"given":"Shuangyan","family":"He","sequence":"additional","affiliation":[{"name":"Ocean College, Zhejiang University, Zhoushan 316021, China"},{"name":"Hainan Institute, Zhejiang University, Sanya 572025, China"},{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Mingjun","family":"He","sequence":"additional","affiliation":[{"name":"Ocean College, Zhejiang University, Zhoushan 316021, China"},{"name":"Hainan Institute, Zhejiang University, Sanya 572025, China"}]},{"given":"Xiaoxia","family":"Li","sequence":"additional","affiliation":[{"name":"Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, Y.-C., Lu, C.-Y., Huang, S.-J., Yang, T.-Z., Chang, Y.-C., and Ho, C.-R. (2022). On the Reconstruction of Missing Sea Surface Temperature Data from Himawari-8 in Adjacent Waters of Taiwan Using DINEOF Conducted with 25-h Data. Remote Sens., 14.","DOI":"10.3390\/rs14122818"},{"key":"ref_2","first-page":"125","article-title":"The characteristics of the seasonal variability of the sea surface temperature field in the Bohai Sea, the Huanghai Sea and the East China Sea from AVHRR data","volume":"24","author":"Bao","year":"2002","journal-title":"Acta Oceanol. Sin."},{"key":"ref_3","first-page":"102086","article-title":"Surface Temperature trends in the Mediterranean Sea from MODIS data during years 2003\u20132019","volume":"49","author":"Sobrino","year":"2022","journal-title":"Reg. Stud. Mar. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mohamed, B., Nilsen, F., and Skogseth, R. (2022). Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea. Remote Sens., 14.","DOI":"10.3390\/rs14174413"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s00343-009-9238-4","article-title":"Warming trend in northern East China Sea in recent four decades","volume":"27","author":"Tang","year":"2009","journal-title":"Chin. J. Oceanol. Limnol."},{"key":"ref_6","first-page":"C08031","article-title":"Modes and mechanisms of sea surface temperature low-frequency variations over the coastal China seas","volume":"115","author":"Zhang","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Ignatov, A. (2021). A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System. Remote Sens., 13.","DOI":"10.3390\/rs13183741"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111485","DOI":"10.1016\/j.rse.2019.111485","article-title":"Construction of a climate data record of sea surface temperature from passive microwave measurements","volume":"236","author":"Alerskans","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.5194\/essd-13-2111-2021","article-title":"A new global gridded sea surface temperature data product based on multisource data","volume":"13","author":"Cao","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Koutantou, K., Brunner, P., and Vazquez-Cuervo, J. (2023). Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data. Remote Sens., 15.","DOI":"10.3390\/rs15092277"},{"key":"ref_11","first-page":"213","article-title":"Spatio-temporal analysis of sea surface temperature in the East China Sea using TERRA\/MODIS products data","volume":"13","author":"Gong","year":"2018","journal-title":"Sea Level Rise Coast. Infrastruct."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/JSTARS.2017.2651951","article-title":"Validation of MODIS Sea Surface Temperature Product in the Coastal Waters of the Yellow Sea","volume":"10","author":"Hao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Saleh, A.K., and Al-Anzi, B.S. (2021). Statistical Validation of MODIS-Based Sea Surface Temperature in Shallow Semi-Enclosed Marginal Sea: A Comparison between Direct Matchup and Triple Collocation. Water, 13.","DOI":"10.3390\/w13081078"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmarsys.2012.05.011","article-title":"MODIS-based sea surface temperature of the Baltic Sea Curonian Lagoon","volume":"129","author":"Kozlov","year":"2014","journal-title":"J. Mar. Syst."},{"key":"ref_15","first-page":"697","article-title":"Daytime and nighttime sea surface temperature (SST) along with diurnal variability (D-SST) in the northern bay of bengal: A remote sensing approach","volume":"38","author":"Shuva","year":"2022","journal-title":"Thalass. Int. J. Mar. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1007\/s11802-021-4714-3","article-title":"Evaluation of NOAA\/AVHRR Sea Surface Temperature at Full HRPT Resolution in the Northwest Pacific Ocean","volume":"20","author":"Chen","year":"2021","journal-title":"J. Ocean Univ. China"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1016\/j.csr.2009.10.009","article-title":"Validation of AVHRR and TMI-derived sea surface temperature in the northern South China Sea","volume":"29","author":"Qiu","year":"2009","journal-title":"Cont. Shelf Res."},{"key":"ref_18","first-page":"436","article-title":"Comparison in multi-infrared products of sea surface temperature in northwest pacific","volume":"48","author":"Meng","year":"2017","journal-title":"Oceanol. Limnol. Sin"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yin, W., Ma, Y., Wang, D., He, S., and Huang, D. (2022). Surface Upwelling off the Zhoushan Islands, East China Sea, from Himawari-8 AHI Data. Remote Sens., 14.","DOI":"10.3390\/rs14143261"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e2019JC015647","DOI":"10.1029\/2019JC015647","article-title":"Quantitative Mapping of the East Australian Current Encroachment Using Time Series Himawari-8 Sea Surface Temperature Data","volume":"125","author":"Xie","year":"2020","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, Z., Xie, G., Zhao, J., Lei, Y., Xie, J., and Pang, W. (2021). Mapping Diurnal Variability of the Wintertime Pearl River Plume Front from Himawari-8 Geostationary Satellite Observations. Water, 14.","DOI":"10.3390\/w14010043"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, C., Liu, Y., Luo, Y., Wang, Y., Liu, X., Zhang, Y., Zhuang, Y., and Tian, Y. (2022). Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP. Remote Sens., 14.","DOI":"10.3390\/rs14153610"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112742","DOI":"10.1016\/j.rse.2021.112742","article-title":"High-resolution marine heatwave mapping in Australasian waters using Himawari-8 SST and SSTAARS data","volume":"267","author":"Huang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","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_25","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_26","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_27","unstructured":"Saha, K., Zhao, X., Zhang, H.-M., Casey, K., Zhang, D., Baker-Yeboah, S., Kilpatrick, K., Evans, R., Ryan, T., and Relph, J. (2018). AVHRR Pathfinder Version 5.3 Level 3 Collated (L3C) Global 4km Sea Surface Temperature for 1981-Present, NOAA National Centers for Environmental Information."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9179","DOI":"10.1029\/1999JC000065","article-title":"Overview of the NOAA\/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database","volume":"106","author":"Kilpatrick","year":"2001","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saha, K., Dash, P., Zhao, X., and Zhang, H.-m. (2020). Error estimation of pathfinder version 5.3 level-3C SST using extended triple collocation analysis. Remote Sens., 12.","DOI":"10.3390\/rs12040590"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/36.701076","article-title":"An overview of MODIS capabilities for ocean science observations","volume":"36","author":"Esaias","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"10826","DOI":"10.1002\/2016GL070287","article-title":"Error characterization in iQuam SSTs using triple collocations with satellite measurements","volume":"43","author":"Xu","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, H., Lin, M., Ma, C., Yin, X., and Guan, L. (October, January 26). Evaluation of Sea Surface Temperature from HY-1C Data. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324171"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"L\u00f3pez Garc\u00eda, M.J. (2020). SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea. Remote Sens., 12.","DOI":"10.3390\/rs12142241"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1175\/JTECH-D-17-0116.1","article-title":"Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model","volume":"35","author":"Zhu","year":"2018","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"C04002","DOI":"10.1029\/2005JC002934","article-title":"Validation of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sea surface temperature in the Southern Ocean","volume":"111","author":"Dong","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s13143-019-00148-3","article-title":"Development of Sea Surface Temperature Retrieval Algorithms for Geostationary Satellite Data (Himawari-8\/AHI)","volume":"56","author":"Park","year":"2019","journal-title":"Asia Pac. J. Atmos. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.rse.2016.02.021","article-title":"Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data","volume":"177","author":"Bulgin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"122","article-title":"Sea surface temperature consistency analysis based on AVHRR and MODIS","volume":"40","author":"Xu","year":"2021","journal-title":"Mar. Environ. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:22Z","timestamp":1760124742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102493"],"URL":"https:\/\/doi.org\/10.3390\/rs15102493","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,9]]}}}