Abstract/Summary

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 inter-calibration, cloud detection, and SSTskin retrieval. First, we conduct the inter-calibration of COCTS infrared channels utilizing 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 the superior performance across diverse conditions and geographical areas, especially reducing false flagging of ocean fronts. Lastly, 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 SST uncertainty appropriate to local spatial variability. A second focus is to employ atmospheric correction smoothing algorithm of OE. Both these measures combine to suppress noise and enhance 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 °C and the robust standard deviation of 0.27 °C between COCTS and insitu 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.