The Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C (HY-1C) satellite was launched in September 2018. Accurate and stable calibration is one of the important factors when deriving geophysical parameters with high quality. The first assessment of HY-1C COCTS thermal infrared calibration is conducted in this research. We choose the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-B satellite as the reference instrument, mainly due to its hyper-spectral characteristic and accurate calibration superiority. The brightness temperatures (BTs) from the two HY-1C COCTS thermal infrared bands centered near 11 and 12 µm are collocated with the IASI in the spatial window of 0.12° × 0.12° and temporal window of half an hour. The homogeneity filtering of matchups is also carried out by setting the relative standard deviation (RSD) thresholds on each collocated grid and its neighboring grids. Based on the filtered matchups, the HY-1C COCTS BTs from the 11 and 12 µm channels are compared with IASI. The mean differences of COCTS minus IASI are 2.68 and 3.18 K for the 11 and 12 μm channels, respectively. The corresponding standard deviations (SDs) are also 0.29 and 0.28 K, respectively. In addition, the BT differences show latitude-dependence and BT-dependence. In order to correct the HY-1C COCTS thermal infrared BTs, the latitude-dependent coefficients are obtained to express the relationship between the BT differences and IASI BTs using the linear robust regression. After the BT correction, the biases and BT-dependence of the COCTS original BT minus IASI differences are removed. Further, the SDs decrease to 0.21 K for the 11 and 12 μm channels. Overall, the calibration of the HY-1C COCTS thermal infrared channels remains stable and the accuracy is around 0.2 K after inter-calibration.

The Haiyang-1C (HY-1C) satellite was launched on September 7, 2018. The Chinese Ocean Color and Temperature Scanner (COCTS) onboard HY-1C has the capacity for ocean color and sea surface temperature observations. The sun glint reflected on sea surface can interfere with the radiance reaching the field of view of satellite sensor, leading to the higher reflectance that is misclassified as cloud and restrictions of satellite data availability. Due to the lack of ability to avoid sun glint, the Bidirectional Reflectance Distribution Function (BRDF) model is implemented in the sun glint correction of HY-1C/COCTS visible images. The atmospheric direct transmittance is calculated by aerosol optical depth and Rayleigh optical depth. The comparisons before and after sun glint correction of visible images at different locations and different time are discussed and analyzed. The spectra are compared before and after the sun glint correction, and it has an obvious decrease in both radiance and reflectance. The performance of the BRDF model in low, moderate and severe glint contaminations are compared. The results indicate that the BRDF model is useful in the sun glint correction of visible bands and leading to more HY-1C/COCTS clear-sky coverage, as consequence can be applied as the pre-processing in the cloud detection and SST retrieval to improve the clear-sky coverage.

Fengyun-3C (FY-3C) is a second-generation meteorological satellite of China that was launched on 23 September 2013. The on board Visible and Infrared Radiometer (VIRR) can be used to observe global sea surface temperature (SST). In this paper, the VIRR SST products are compared with MODIS SST products and buoy measurements from 2015 to 2019. The collocations of VIRR, MODIS, and buoy SST are generated separately during the day and night with the spatial window of 0.05° × 0.05°. The comparison results show that the biases of VIRR SST minus buoy SST during the day and night are −0.21 and −0.13 °C with a corresponding robust standard deviation (RSD) of 0.58 and 0.59 °C, respectively. The mean differences between VIRR and MODIS are −0.10 and 0.08 °C with RSDs of 0.53 and 0.58 °C for the daytime and nighttime, respectively. The consistency of VIRR SST accuracy from 2015 to 2019 and the dependence of VIRR SST error on SST and latitude are also investigated.

A generalized probability density function (PDF) is introduced to enhance sea surface slope modeling for remote sensing applications. This new PDF, which incorporates the anisotropy index to better capture the direction and tilt of surface waves relative to the classical Cox & Munk model proposed 70 years ago, is then applied to sun glint correction in satellite imagery. Sixteen different mean square slope (MSS) models are reviewed to establish both the strengths and limitations of the classical model and the stability and adaptability of the anisotropy index. The new sea surface model applies to a wider range of sea surface states, including those in coastal environments, and provides a stable quantitative description of sea surface topography. Application of the generalized PDF to sun glint correction in satellite imagery demonstrates its overall accuracy and improved efficacy compared to the Cox & Munk model, particularly in maintaining the integrity of sea surface and cloud features in complex weather environments. This initial study provides a promising approach to improve the accuracy and reliability of sun glint correction in remote sensing of water surfaces, with applications to improving both historical and future satellite-based climate data records.

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 study 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 (HY-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 (spatio-temporal coverage) of SST retrievals.

Sun glint presents a considerable challenge in remote sensing imagery, as it interferes with cloud detection and restricts the ability to observe target parameters such as ocean color, temperature, and bathymetry. Sun glint particularly affects the retrieval of water-leaving radiance or reflectance in the direction of the satellite's field of view. Current sun glint correction methods for low- and medium-resolution imagery are limited, especially when winds are weak. This study presents a novel method that uses an anisotropy index to capture the direction and tilt of surface waves based on a bidirectional reflectance distribution function (BRDF). We apply this sun glint correction method to Visible Infrared Imaging Radiometer Suite (VIIRS) observations of the Great Lakes and evaluate its performance compared to the Multispectral Imager (MSI) observations of Sentinel-2 glint removal (GRS) algorithm and in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) dataset. Reflectance and normalized water-leaving radiance after atmospheric correction based on our method agree well with GRS and in-situ measurements, respectively, significantly increasing correlations from 0.694 to 0.888 between VIIRS retrievals and in-situ data, while root mean-square error decreases to 0.615. This method can be applied to increase the availability of long-term climate data records for research on lacustrine ecosystems.