Journal of Marine Sciences ›› 2022, Vol. 40 ›› Issue (2): 10-18.DOI: 10.3969-j.issn.1001-909X.2022.02.002

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Reconstruction of sea surface temperature from DINEOF-based FY polar-orbiting meteorological satellite


  1. 1.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China;
    2.State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
    3.Second Institute of Oceanography, MNR, Hangzhou 310012, China;
    4.Innovation Center for FengYun Meteorological Satellite (FYSIC), China Meteorological Administration, Beijing 100081, China
  • Online:2022-06-15 Published:2022-06-15

Abstract: Sea surface temperature (SST) is the critical factor for depicting the marine thermal distribution. Daily global SST data sets support the typhoon elaborated monitoring and other marine disasters forecast. SST products retrieved by the visible infrared radiometers and mediumresolution imagers have high spatial resolution, while the SST products retrieved by infrared remote sensing are affected by clouds, fog and haze, and therefore a large areas under the clouds are lack of value. SST products retrieved by the microwave radiometer have low spatial resolution, while the microwave could penetrate the cloud layer to achieve all-weather sea surface observation. The data interpolation empirical orthogonal function method (DINEOF) was used to reconstructed the global SST products, and FY-3 (Fengyun 3) SST data sets were applied in this study, which included the SST data sets from the FY-3B/FY-3C Visible and Infra-Red Radiometer, FY-3D Medium Resolution Spectral Imager and FY-3D Micro-Wave Radiation Imager. Accuracy of the reconstructed data sets was verified using OISST measurements to demonstrate the validity and reliability of the DINEOF method. The results show that DINEOF reconstructed sea surface temperature (DSST) data are validated reliable. Root mean square error of the original data is ranging from 0.59 ℃ to 0.70 ℃, while the reconstructed data is relatively stable, ranging from 0.10 ℃ to 0.34 ℃. Correlation coefficient obvious raises from 0.33-0.48 to 0.78-0.98. Multi-sensors reconstructed SST products is continuous and credible in spatial distribution and monitor the variation of warm pool from spring to winter. Addition of FY-3D microwave SST products has significantly improved the spatial continuous distribution and temporal resolution of reconstructed SST.

Key words: SST reconstruction, DINEOF, FY-3 satellites

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