The analytical model based on the quasi-single small-angle scattering approximation can efficiently simulate oceanic lidar signals with multiple scattering; thus, its accuracy is of particular interest to scientists. In this paper, the model is modified to include refraction at oblique incidence and is then compared with Monte Carlo (MC) simulations and experimental results. Under different conditions, the results calculated by the analytical model demonstrate good agreement with the MC simulation and experimental data. The coefficient of determination R² considering the logarithm of signals and the root mean square of the relative difference δ are R² = 0.998 and δ = 10% in comparison with the semi-analytic MC simulation and R² = 0.952 and δ = 46% for the lidar experiment. Thus, the results demonstrate the validity of the analytical model in the simulation of oceanic lidar signals.

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

In this study, sea surface skylight spectral reflectance ρ(λ) was retrieved by means of the non-linear spectral optimization method and a bio-optical model. The spectral variability of ρ(λ) was found to be mainly influenced by the uniformity of the incident skylight, and a model is proposed to predict the ρ(λ) spectral dependency based on skylight reflectance at 750 nm. It is demonstrated that using the spectrally variable ρ(λ), rather than a constant, yields an improved agreement between the above-water remote sensing reflectance R_rs(λ) estimates and concurrent profiling ones. The findings of this study highlight the necessity to re-process the relevant historical above-water data and update ocean color retrieval algorithms accordingly.

Ocean color satellite sensors have become an indispensable component in the Earth Observing System, in which the use of multiple ocean color satellite sensors not only improves the spatiotemporal coverage of the global oceans but also maintains the continuity of the data products for long-term monitoring. In this research, the performance of a new ocean color satellite sensor Chinese Ocean Color and Temperature Scanner (COCTS) from HY1C launched in September 2018 is thoroughly evaluated with two important aspects: the signal-to-noise ratio (SNR) at the top of the atmosphere and the uncertainty in the remote-sensing reflectance Rrs) products. The results showed that the SNR of the COCTS can satisfy the requirements of the ocean color applications, and the uncertainty in the Rrs at the blue bands in the ocean waters meets the demand of less than 5%. A further comparison with other well-known ocean color sensors indicates that not only the COCTS can provide reliable ocean color data but also the processing system is robust and reliable. These results provide a solid base for merging the COCTS products with other ocean color sensors for the studies of ocean biogeochemistry.

As one of the key payloads onboard the HY-1C satellite, the Chinese ocean color and temperature scanner (COCTS) was designed for global sea surface temperature (SST) and ocean color detection. In this study, a nonlinear SST algorithm (NLSST) is applied to derive SSTs from the brightness temperatures (BTs) of the two COCTS thermal infrared bands at 10.8 and 12.0 μm. The SST retrieval results from the multichannel SST algorithm (MCSST) are used as the first guess for the input of the NLSST algorithm. The coefficients of both the MCSST and NLSST algorithms are regressed from the matchup datasets of the COCTS BT observations and the measurements from the in situ Quality Monitor system (iQuam), which were collected from Sep. 10 to Dec. 31, 2018. The retrieval SSTs from Jan. 1, 2019 to Mar. 31, 2020 are evaluated by the in situ measurements from iQuam with root mean square errors (RMSEs) of 0.84 °C for daytime and 0.97 °C for nighttime and robust standard deviations (RSDs) of 0.73 °C for daytime and 0.72 °C for nighttime. Daily gridded retrieval SSTs from Sep. 10, 2018 to Mar. 31, 2020 are compared with SSTs from moderate-resolution imaging spectroradiometer (MODIS) onboard Terra satellite. The cross comparison RSDs are 0.56±0.05 °C for daytime and 0.67±0.05 °C for nighttime.

Ocean color remote sensing using visible spectral bands with or without the combination of near infrared (NIR) bands for the retrieval of suspended sediment (SS) concentration (SSC) has been proven to be successful in low and medium turbid waters, but the visible bands are prone to be saturated and less sensitive to high-turbid waters. This paper proposes an NIR bands SS index (NISSI) for SSC estimation using an experimental ocean-color sensor, the moderate resolution wide-wavelength imager onboard the Chinese Tiangong 2 Space Lab. The NISSI takes advantage of the reflectance peak height around 820 nm, which is highly related with SSC. Validation using in situ dataset at the Poyang and Taihu Lakes of China and comparison with conventional approaches showed higher accuracy with mean relative error values of 21.9% and 18.6%, respectively. Benefits of NISSI algorithm is proven with higher saturation level of 300 mg/L. Sensitivity of the algorithm is affected by the sensor noise, and SSC variations lower than 12 mg/L would be significantly impeded. The results showed a promising solution for ocean-color remote sensing of extremely turbid waters, with alternative or new NIR bands around 820 nm, or short-wave infrared bands for SSC estimation.