HY-1D COCTS 海表温度反演与印证

刘铭坤; 管磊; 刘凡莉; 刘建强

doi:10.11834/jrs.20221689

中国卫星海洋遥感研究进展 | 浏览量 : 0 下载量: 1684 CSCD: 1

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HY-1D COCTS 海表温度反演与印证
Retrieval and validation of sea surface temperature from HY-1D COCTS
2023年27卷第4期页码：953-964
收稿：2021-11-02，

纸质出版：2023-04-07
DOI： 10.11834/jrs.20221689
稿件说明：

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刘铭坤，管磊，刘凡莉，刘建强.2023.HY-1D COCTS 海表温度反演与印证.遥感学报，27（4）： 953-964 DOI： 10.11834/jrs.20221689.

Liu M K，Guan L，Liu F L and Liu J Q. 2023. Retrieval and validation of sea surface temperature from HY-1D COCTS. National Remote Sensing Bulletin， 27（4）：953-964 DOI： 10.11834/jrs.20221689.

摘要

海洋一号D（Haiyang-1D，HY-1D）卫星于2020年6月成功发射，与海洋一号C（Haiyang-1C，HY-1C）卫星形成上、下午卫星组网，是中国HY-1系列卫星在轨业务化运行的海洋遥感卫星。HY-1D卫星上搭载的海洋水色扫描仪COCTS（Chinese Ocean Color and Temperature Scanner）有两个热红外通道，波段范围分别为10.30—11.40 µm和11.40—12.50 µm，可用于海表温度SST（Sea Surface Temperature）观测。本文采用HY-1D COCTS热红外通道的观测亮温，基于大气辐射传输模拟计算，开展云检测和SST反演算法研究，对2021年1月西北太平洋海域的COCTS观测数据进行SST反演，并采用现场实测和同步卫星数据对反演得到的COCTS SST进行印证。COCTS与现场实测SST的比较结果表明，二者的平均偏差为

0.04 ℃，标准偏差为0.45 ℃。且COCTS与iQuam的SST偏差与SST以及大气水汽总量之间没有明显的依赖关系。COCTS与搭载在Suomi NPP卫星上的可见光红外成像辐射计VIIRS（Visible Infrared Imaging Radiometer Suite）SST产品的比较结果表明，二者的平均偏差为

0.05 ℃，标准偏差为0.49 ℃。基于以上算法得到的HY-1D COCTS西北太平洋海域的SST具有较高的精度，与国际海表温度产品达到了相当的水平。

Abstract

HY-1D was launched in June 2020 as the first operational generation of Chinese marine satellite constellation with the launched HY-1C satellite for networking in the morning and afternoon. The Chinese Ocean Color and Temperature Scanner (COCTS) has two thermal infrared channels (10.30—11.40 and 11.40—12.50 µm) for observing Sea Surface Temperature (SST). In this work

the Bayesian cloud detection and optimal estimation algorithm are utilized for HY-1D COCTS SST retrieval in the Northwest Pacific based on the atmospheric radiative transfer model.

COCTS is a whiskbroom scanner with eight parallel detectors along-track. The different spectral responses of these eight parallel detectors caused the sharp striped noise across the scan lines. The de-striping is carried out based on the unidirectional variational model. De-striping can be viewed as an optimization problem based on the minimization of a unidirectional variational model because striping can be assumed to be unidirectional noise because it does not affect the image horizontal gradient. The solution of the Euler-Lagrange equation is obtained based on a Gauss-Seidel fixed-point iterative scheme. The de-striped analysis show that the de-striping algorithm is successfully utilized in the HY-1D COCTS radiance data.

Based on the simulated brightness temperature using the moderate resolution atmospheric transmission model

a Bayesian approach is utilized for the cloud detection of COCTS infrared brightness temperatures. Bayesian cloud detection is based on Bayes’ theorem

which determines a clear-sky probability given the satellite observations and prior background information. The COCTS brightness temperature images and the retrieved SST validation distributions show that the cloud detection is effective for SST retrieval.

The optimal estimation algorithm is used for COCTS SST retrieval

based on the COCTS simulated brightness temperature and ERA5 SST as the prior SST. The HY-1D COCTS-retrieved SSTs are compared with the in situ SST and Visible Infrared Imaging Radiometer Suite (VIIRS) SST. The bias of the comparisons between the COCTS-retrieved SST and the in situ measurement is

0.04 ℃

and the standard deviation is 0.45 ℃. The bias and standard deviation of the COCTS newly retrieved SST minus SST from VIIRS are

0.05 ℃ and 0.49 ℃

respectively. Validation result shows that the accuracy of HY-1D COCTS SST in the Northwest Pacific reaches the equivalent level with international operational SST.

关键词

Keywords

references

Bentamy A , Piollé J F , Grouazel A , Danielson R , Gulev S , Paul F , Azelmat H , Mathieu P P , Von Schuckmann K , Sathyendranath S , Evers-King H , Esau I , Johannessen J A , Clayson C A , Pinker R T , Grodsky S A , Bourassa M , Smith S R , Haines K , Valdivieso M , Merchant C J , Chapron B , Anderson A , Hollmann R and Josey S A . 2017 . Review and assessment of latent and sensible heat flux accuracy over the global oceans . Remote Sensing of Environment , 201 : 196 - 218 [ DOI: 10.1016/j.rse.2017.08.016 http://dx.doi.org/10.1016/j.rse.2017.08.016 ]

Bouali M and Ignatov A . 2013 . Estimation of detector biases in MODIS thermal emissive bands . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 7 ): 4339 - 4348 [ DOI: 10.1109/TGRS.2012.2230183 http://dx.doi.org/10.1109/TGRS.2012.2230183 ]

Bulgin C . 2019 . SST CCI auxiliary datasets [Data set] . Zenodo . [ DOI: 10.5281/zenodo.2586714 http://dx.doi.org/10.5281/zenodo.2586714 ]

Hersbach H , Bell B , Berrisford P , Hirahara S , Horányi A , Muñoz-Sabater J , Nicolas J , Peubey C , Radu R , Schepers D , Simmons A , Soci C , Abdalla S , Abellan X , Balsamo G , Bechtold P , Biavati G , Bidlot J , Bonavita M , De Chiara G , Dahlgren P , Dee D , Diamantakis M , Dragani R , Flemming J , Forbes R , Fuentes M , Geer A , Haimberger L , Healy S , Hogan R J , Hólm E , Janisková M , Keeley S , Laloyaux P , Lopez P , Lupu C , Radnoti G , De Rosnay P , Rozum I , Vamborg F , Villaume S and Thépaut J N . 2020 . The ERA5 global reanalysis . Quarterly Journal of the Royal Meteorological Society , 146 ( 730 ): 1999 - 2049 [ DOI: 10.1002/qj.3803 http://dx.doi.org/10.1002/qj.3803 ]

Kent E C , Kennedy J J , Smith T M , Hirahara S , Huang B Y , Kaplan A , Parker D E , Atkinson C P , Berry D I , Carella G , Fukuda Y , Ishii M , Jones P D , Lindgren F , Merchant C J , Morak-Bozzo S , Rayner N A , Venema V , Yasui S and Zhang H M . 2017 . A call for new approaches to quantifying biases in observations of sea surface temperature . Bulletin of the American Meteorological Society , 98 ( 8 ): 1601 - 1616 [ DOI: 10.1175/BAMS-D-15-00251.1 http://dx.doi.org/10.1175/BAMS-D-15-00251.1 ]

Kilpatrick K A , Podestá G , Walsh S , Williams E , Halliwell V , Szczodrak M , Brown O B , Minnett P J and Evans R . 2015 . A decade of sea surface temperature from MODIS . Remote Sensing of Environment , 165 : 27 - 41 [ DOI: 10.1016/j.rse.2015.04.023 http://dx.doi.org/10.1016/j.rse.2015.04.023 ]

Li N H , Wang S J , Guan L and Liu M K . 2021 . Assessment of global FY-3C/VIRR sea surface temperature . Remote Sensing , 13 ( 16 ): 3249 [ DOI: 10.3390/rs13163249 http://dx.doi.org/10.3390/rs13163249 ]

Liu M K , Merchant C J , Guan L and Mittaz J P D . 2018 . Inter-calibration of HY-1B/COCTS thermal infrared channels with MetOp-A/IASI . Remote Sensing , 10 ( 8 ): 1173 [ DOI: 10.3390/rs10081173 http://dx.doi.org/10.3390/rs10081173 ]

Liu M K , Guan L , Liu J Q , Song Q J , Ma C F and Li N H . 2021 . First assessment of HY-1C COCTS thermal infrared calibration using MetOp-B IASI . Remote Sensing , 13 ( 4 ): 635 [ DOI: 10.3390/rs13040635 http://dx.doi.org/10.3390/rs13040635 ]

Luo B K , Minnett P J , Szczodrak M , Kilpatrick K and Izaguirre M . 2020 . Validation of Sentinel-3A SLSTR derived Sea-Surface Skin Temperatures with those of the shipborne M-AERI . Remote Sensing of Environment , 244 : 111826 [ DOI: 10.1016/j.rse.2020.111826 http://dx.doi.org/10.1016/j.rse.2020.111826 ]

Merchant C J , Le Borgne P , Marsouin A and Roquet H . 2008 . Optimal estimation of sea surface temperature from split-window observations . Remote Sensing of Environment , 112 ( 5 ): 2469 - 2484 [ DOI: 10.1016/j.rse.2007.11.011 http://dx.doi.org/10.1016/j.rse.2007.11.011 ]

Merchant C J , Le Borgne P , Roquet H and Marsouin A . 2009 . Sea surface temperature from a geostationary satellite by optimal estimation . Remote Sensing of Environment , 113 ( 2 ): 445 - 457 [ DOI: 10.1016/j.rse.2008.10.012 http://dx.doi.org/10.1016/j.rse.2008.10.012 ]

Merchant C J , Le Borgne P , Roquet H and Legendre G . 2013 . Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) . Remote Sensing of Environment , 131 : 287 - 297 [ DOI: 10.1016/j.rse.2012.12.019 http://dx.doi.org/10.1016/j.rse.2012.12.019 ]

Merchant C J , Embury O , Bulgin C E , Block T , Corlett G K , Fiedler E , Good S A , Mittaz J , Rayner N A , Berry D , Eastwood S , Taylor M , Tsushima Y , Waterfall A , Wilson R and Donlon C . 2019 . Satellite-based time-series of sea-surface temperature since 1981 for climate applications . Scientific Data , 6 ( 1 ): 223 [ DOI: 10.1038/s41597-019-0236-x http://dx.doi.org/10.1038/s41597-019-0236-x ]

Minnett P J , Alvera-Azcárate A , Chin T M , Corlett G K , Gentemann C L , Karagali I , Li X , Marsouin A , Marullo S , Maturi E , Santoleri R , Picart S S , Steele M and Vazquez-Cuervo J . 2019 . Half a century of satellite remote sensing of sea-surface temperature . Remote Sensing of Environment , 233 : 111366 [ DOI: 10.1016/j.rse.2019.111366 http://dx.doi.org/10.1016/j.rse.2019.111366 ]

Petrenko B , Ignatov A , Kihai Y , Stroup J and Dash P . 2014 . Evaluation and selection of SST regression algorithms for JPSS VIIRS . Journal of Geophysical Research: Atmospheres , 119 ( 8 ): 4580 - 4599 [ DOI: 10.1002/2013JD020637 http://dx.doi.org/10.1002/2013JD020637 ]

Rodgers C D . 2000 . Inverse methods for atmospheric sounding: theory and practice . Singapore : World Scientific , 2000. [ DOI: 10.1142/3171 http://dx.doi.org/10.1142/3171 ]

Saha K , Dash P , Zhao X P and Zhang H M . 2020 . Error estimation of pathfinder version 5.3 level-3C SST using extended triple collocation analysis . Remote Sensing , 12 ( 4 ): 590 [ DOI: 10.3390/rs12040590 http://dx.doi.org/10.3390/rs12040590 ]

Wanninkhof R , Asher W E , Ho D T , Sweeney C and McGillis W R . 2009 . Advances in quantifying Air-Sea gas exchange and environmental forcing . Annual Review of Marine Science , 1 : 213 - 244 [ DOI: 10.1146/annurev.marine.010908.163742 http://dx.doi.org/10.1146/annurev.marine.010908.163742 ]

Xu F and Ignatov A . 2014 . In situ SST quality monitor ( i Quam) . Journal of Atmospheric and Oceanic Technology , 31 ( 1 ): 164 - 180 [ DOI: 10.1175/JTECH-D-13-00121.1 http://dx.doi.org/10.1175/JTECH-D-13-00121.1 ]

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