Dynamic Optimal Estimation With Atmospheric Correction Smoothing for Sea Surface Skin Temperature Retrieval From Infrared Satellite Imagery

This study offers an in-depth exploration into sea surface skin temperature (SSTskin) from the Haiyang-1D (HY-1D) Chinese Ocean Color and Temperature Scanner (COCTS). The main components include intercalibration, cloud detection, and SSTskin retrieval. First, we conduct the intercalibration of COCTS infrared channels utilizing the Visible Infrared Imaging Radiometer Suite (VIIRS) as the reference instrument. A double-differencing methodology is employed to evaluate and correct the COCTS calibration. Next, we introduce a physically based deep learning algorithm for cloud detection, designed to interpret complex textures in satellite imagery. The algorithm demonstrates superior performance across diverse conditions and geographical areas, especially in reducing false flagging of ocean fronts. Finally, we propose an optimal estimation (OE) methodology for COCTS SSTskin retrieval. One focus is on estimating appropriate covariance matrices within the OE algorithm, including an innovative method for dynamically setting the prior SSTskin uncertainty appropriate to local spatial variability. The second focus is to employ the atmospheric correction smoothing algorithm of OE. Both these measures combine to suppress noise and enhance the sensitivity of SSTskin. We assign quality levels to the retrieved SSTskin data. The high-quality COCTS SSTskin is validated using iQuam in situ data. Our results indicate the bias of $- 0.20~^{\circ }$ C and the robust standard deviation (RSD) of $0.27~^{\circ }$ C between COCTS and in situ SST, with an average sensitivity of 0.87. These findings affirm that the successful implementation of these methodologies significantly enhances the accuracy and reliability of SSTskin data from HY-1D COCTS. This advancement provides substantial benefits to expand the global high-precision SSTskin dataset.