Retrieval of sea surface temperature from HY-1B COCTS

Liu, M., Merchant, C. J. ORCID: https://orcid.org/0000-0003-4687-9850, Embury, O. ORCID: https://orcid.org/0000-0002-1661-7828, Liu, J., Song, Q. and Guan, L. (2022) Retrieval of sea surface temperature from HY-1B COCTS. IEEE Transactions on Geoscience and Remote Sensing, 60. 5002913. ISSN 0196-2892 doi: 10.1109/TGRS.2022.3190444

Abstract/Summary

The Chinese Ocean Color and Temperature Scanner (COCTS) onboard Haiyang-1 (HY-1) series satellites has two thermal infrared channels with the spectrum range of 10.30–11.40 and 11.40– 12.50 μm for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in this study. The Bayesian cloud detection algorithm that has been developed is based on Bayes’ theorem and uses a simulation of COCTS observations. The MODerate resolution atmospheric TRANsmission (MODTRAN) model was used for the simulation of COCTS brightness temperatures. SSTs were retrieved from COCTS by OE from 2009 to 2011 in the northwest Pacific. A comparison of COCTS OE SST with in situ SST showed that the COCTS SSTs are cooler than buoy measurements by −0.23 °C on average, and the standard deviation (SD) of differences was 0.51 °C. A large component of the mean difference is attributable to the cool skin effect at the ocean surface (typically −0.15 °C to −0.2 °C), with the remainder being attributable to simulation and calibration biases. The mean difference of COCTS OE SST with matched skin temperatures from the advanced along-track scanning radiometer (AATSR) is closer to zero, being −0.09 °C, with an SD of 0.49 °C. These validation results of COCTS OE SST demonstrate that Bayesian cloud detection and OE SST retrieval algorithm work well for improving COCTS SST accuracy and show the potential of these methods to help develop SST products for operational HY-1 satellites: HY-1C and HY-1D.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/106451
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IEEE Geoscience and Remote Sensing Society
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