Cloud Detection and Sea Surface Temperature Retrieval by HY-1C COCTS Observations

Abstract

Sea surface temperature (SST) is a vital oceanic parameter that significantly influences air–sea heat flux and momentum exchange. SST datasets are crucial for identifying and describing both short-term and long-term climate perturbations in the ocean. This article focuses on cloud detection and SST retrievals in the Western Pacific Ocean, using observations obtained by the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C satellite. To distinguish between clear-sky and overcast regions, reflectance after sun glint correction and brightness temperature are used as inputs for an alternative decision tree (ADTree). The accuracy of cloud detection is 93.85% for daytime and 91.98% for nighttime, respectively. Application of the cloud detection algorithm improves the accuracy and data availability (spatiotemporal coverage) of SST retrievals. We implement a nonlinear algorithm to retrieve the SST and validate these retrieved values against buoy measurements of SST. Comparisons are conducted for measurements within ±1 h and 0.01° × 0.01° of the retrieval. During the day, the bias and standard deviation (SD) are −0.01 °C and 0.63 °C, respectively, while at night, they stand at −0.08 °C and 0.71 °C, respectively. Furthermore, the intercomparison between the SST products derived from the moderate-resolution imaging spectroradiometer (MODIS) onboard Terra and the results are conducted. During the day, the bias and SD are 0.03 °C and 0.42 °C, respectively, whereas at night, they are 0.25 °C and 0.76 °C, respectively. This article improves the accuracy and applicability of the SST retrieved from the COCTS thermal infrared channels.

Keywords

• Alternative decision tree (ADTree)
• cloud detection
• Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS)
• retrieval
• sea surface temperature (SST)