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,是我国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(Chinese Ocean Color and Temperature Scanner,COCTS)有两个热红外通道,波段范围分别为10.30–11.40 微米和 11.40–12.50 微米,可用于海表温度(Sea Surface Temperature,SST)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 °C,标准偏差为0.45 °C。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(Visible Infrared Imaging Radiometer Suite,VIIRS)SST产品的比较结果表明,二者的平均偏差为-0.05°C,标准偏差为0.49°C。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。

Haiyang-1D (HY-1D) was launched in June 2020, that is the first operational generation of Chinese marine satellite constellation with the launched Haiyang-1C (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 μm and 11.40–12.50 μm) for observing sea surface temperature (SST). In this paper, based on the atmospheric radiative transfer model, the Bayesian cloud detection and optimal estimation algorithm are utilized for HY-1D COCTS SST retrieval in the Northwest Pacific. 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. Because the striping can be assumed to be unidirectional noise as it does not affect the image horizontal gradient, the de-striping can be viewed as an optimization problem based on the minimization of a unidirectional variational model. 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 HY-1D COCTS radiance data. Based on the simulated brightness temperature using the MODerate resolution atmospheric TRANsmission (MODTRAN) model, a Bayesian approach is utilized for cloud detection of COCTS infrared brightness temperatures. Bayesian cloud detection is based on Bayes’ theorem, determining a clear-sky probability given the satellite observations and prior background information. Both 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 COCTS simulated brightness temperature and ERA5 SST as the prior SST. The HY-1D COCTS retrieved SSTs are compared with in-situ SST and Visible Infrared Imaging Radiometer Suite (VIIRS) SST. The bias of the comparisons between COCTS retrieved SST and in-situ measurement is -0.04°C and the standard deviation is 0.45°C. And the bias and standard deviation of COCTS newly retrieved SST minus SST from VIIRS are -0.05°C and 0.49°C, respectively. The validation result shows that the accuracy of HY-1D COCTS SST in the Northwest Pacific reaches the equivalent level with international operational SST.