Estimation of sea surface temperature in the Arctic based on Fengyun-3D/MERSI II data

Abstract

Sea surface temperature (SST) is a critical parameter in understanding Arctic amplification of climate change. In this study, SST in the Arctic was estimated based on data from the Medium Resolution Spectral Imager (MERSI) II on board the Fengyun-3D (FY-3D) satellite and in-situ measurements. To improve the quality of the MERSI thermal data, an optimization model for stripe noise removal based on the alternating direction multiplier method was employed. Clear-sky SST was estimated based on the nonlinear SST (NLSST) algorithm and tripe NLSST algorithm. When compared with the SST product retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) in September 2019, the mean difference between VIIRS SST and MERSI II SST is −0.21℃ with a standard deviation of 0.29℃ in the daytime, while the mean difference is −0.15℃ with a standard deviation of 0.34℃ at nighttime. Results indicate that the accuracy of MERSI II SST meets the requirements for high-accuracy SST retrieval. Furthermore, these algorithms demonstrate the potential for long-term SST estimation in the Arctic using the FY-3D/MERSI II data.

Keywords

Sea surface temperature / FY-3D/MERSI / Noise removal / Information and Computing Sciences / Artificial Intelligence and Image Processing

References

[1] BoydS, ParikhN, ChuE, PeleatoB, EcksteinJ. Distributed optimization and statistical learning via the alternating direction method of multiplier. Found Trends Mach Learn, 2010, 311-122.

[2] Dosapati A (2019) Algorithm for estimating the sea surface temperature (SST) through Sentinel-3’s SLSTR sensor. Sapienza University of Roma, Roma, pp 1–10. https://doi.org/10.13140/RG.2.2.22118.45120

[3] Frey R, Ackerman S, Liu Y, Strabala K, Zhang H, Key J et al (2008) Cloud detection with MODIS. Part I: improvements in the MODIS cloud mask for collection 5. J Atmos Ocean Technol 25(7):1057–1072. https://doi.org/10.1175/2008JTECHA1052.1

[4] GaoZH, MaSY, LiJC, SunP, LiuY, XingQW, et al. . Climate-induced long-term variations of the Arctic ecosystems. Prog Oceanogr, 2023, 213: 103006.

[5] HøyerJL, Le BorgneP, EastwoodS. A bias correction method for Arctic satellite sea surface temperature observations. Remote Sens Environ, 2014, 146: 201-213.

[6] JiaC, MinnettPJ. High latitude sea surface temperatures derived from MODIS infrared measurements. Remote Sens Environ, 2020, 251: 112094.

[7] KilpatrickKA, PodestáGP, EvansR. Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J Geophys Res- Oceans, 2001, 106C59179-9197.

[8] KilpatrickKA, PodestáG, WalshS, WilliamsE, HalliwellV, SzczodrakM, et al. . A decade of sea surface temperature from MODIS. Remote Sens Environ, 2015, 165: 27-41.

[9] KimN, HanSS, JeongCS. ADOM: ADMM-based optimization model for stripe noise removal in remote sensing image. IEEE Access, 2023, 11: 106587-106606.

[10] LongZX, PerrieW. Air-sea interactions during an Arctic storm. J Geophys Res-Atmos, 2012, 117D15D15103.

[11] MatsumuraS, KosakaY. Arctic-Eurasian climate linkage induced by tropical ocean variability. Nat Commun, 2019, 1013441.

[12] McMillinLM, CrosbyDS. Theory and validation of the multiple window sea surface temperature technique. J Geophys Res-Oceans, 1984, 89C33655-3661.

[13] MinnettPJ, Alvera-AzcárateA, ChinTM, CorlettGK, GentemannCL, KaragaliI, et al. . Half a century of satellite remote sensing of sea-surface temperature. Remote Sens Environ, 2019, 233: 111366.

[14] MinnettPJ, KilpatrickKA, PodestáGP, EvansRH, SzczodrakMD, IzaguirreMA, et al. . Skin sea-surface temperature from VIIRS on Suomi-NPP–NASA continuity retrievals. Remote Sens, 2020, 12203369.

[15] PithanF, MauritsenT. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat Geosci, 2014, 73181-184.

[16] SunX, GuanL. Arctic sea ice albedo estimation from Fengyun-3C/Visible and infra-red radiometer. Remote Sens, 2024, 16101719.

[17] Timmermans ML, Labe Z (2023) NOAA Arctic report card 2023: sea surface temperature. Available at https://doi.org/10.25923/e8jc-f342

[18] WaltonCC. A review of differential absorption algorithms utilized at NOAA for measuring sea surface temperature with satellite radiometers. Remote Sens Environ, 2016, 187: 434-446.

[19] WaltonCC, PichelWG, SapperJF, MayDA. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. J Geophys Res-Oceans, 1998, 103C1227999-28012.

[20] WangH, GuanL, ChenG. Evaluation of sea surface temperature from FY-3C VIRR Data in the Arctic. IEEE Geosci Remote Sens Lett, 2016, 132292-296.

[21] Wang SJ, Cui P, Zhang P, Ran MN, Lu F, Wang WH (2014) FY-3C/VIRR SST algorithm and cal/val activities at NSMC/CMA. In: Conference on Ocean Remote Sensing and Monitoring from Space. SPIE, 9261. https://doi.org/10.1117/12.2068773

[22] Wang X, Key J (2023) Climate algorithm theoretical basis document for the extended AVHRR Polar Pathfinder (APP-x). NOAA/NESDIS, National Climatic Data Center, Washington, DC https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/AVHRR_Extended_Polar_Pathfinder/AlgorithmDescription_01B-24b.pdf. Accessed 22 Feb 2025

[23] XuF, IgnatovA. In situ SST quality monitor (iQuam). J Atmos Ocean Technol, 2014, 311164-180.

[24] ZhangL, HuiFM, ChengX, LiG, ZhengXP, ChiZH, et al. . Enhancing the quality of FY-3D MERSI-II TIR images: an application to improve sea ice lead detection. IEEE Trans Geosci Remote Sens, 2024, 62: 4301816.

[25] ZhengXP, HuiFM, HuangZH, WangTX, HuangHB, WangQM. Ice/snow surface temperature retrieval from Chinese FY-3D MERSI-II data: algorithm and preliminary validation. IEEE Trans Geosci Remote Sens, 2022, 60: 4512715.

Funding

the National Key R&D Program of China(2022YFC3104900/2022YFC3104905)

Sanya Yazhou Bay Science and Technology City(HSPHDSRF-2023-02-004)

RIGHTS & PERMISSIONS

The Author(s)