High-resolution optical satellite sensors, such as Multispectral Instrument (MSI) and the Operational Land Imager (OLI), show excellent performance in capturing fine-scale sea surface wave motion and spatial patterns. Their inter-band time lag offers the potential to resolve wave directional ambiguity and parameter estimation. Here, we investigate the spatial resolution and channel time lag of optical sensor, based on cross-spectral analysis of multi-channel imagery, to clarify their requirement in ocean wave monitoring. The analysis, validated by matched buoy data and optical imagery, demonstrates that the minimum detectable time lags for 180° directional ambiguity removal are 0.47s, 0.52s, and 0.74s for MSI 10 m, 20 m, and OLI 30 m resolution data, respectively. All three resolutions effectively detect wave system wavelengths ranging from 60 m to 300 m, with minimum detection limits of 20 m, 40 m, and 60 m, respectively. Additionally, the optimal statistics window size for consistent wave detection is about 8 km. These findings not only highlight the strengths of current satellite sensors but also provide references for future high-resolution optical sensor design in ocean wave monitoring.

The Chinese new-generation ocean color observation satellite (HY-3A), launched on November 16, 2023, carries the 2nd Chinese Ocean Color and Temperature Scanner (COCTS2) as its primary sensor. COCTS2 offers a swath width of over 3000 km, a spatial resolution of 500 m, and 15 reflective solar bands (RSBs) spanning 360 to 1640 nm. This study reviews COCTS2 intrinsic characterization and radiometric performance during its first year on-orbit and assesses the accuracy of global ocean color products. It demonstrates a high signal-to-noise ratio (SNR) under typical ocean radiance, outperforming specification values by a significant margin of 50%. Meanwhile, the image non-uniformity is better than 1%, and the linear response remains close to 1 within the radiance dynamic range under oceanic ocean color conditions. Notably, COCTS2 is the first Chinese sensor equipped with an onboard calibration system, achieving solar calibration accuracy within 2%. Through solar calibration, we established a radiometric degradation model that maintains stable performance throughout nine months, with radiometric degradation across RSBs below 1%. Subsequently, we derived absolute calibration gain factors using cross calibration approaches, achieving consistency with the solar calibration gain factors. On this basis, we established a relative calibration gain factors by vicarious calibration, and after application, the radiometric accuracy was better than 95%. Ultimately, in validating global ocean color products, we compare COCTS2-derived remote sensing reflectance (Rrs) with Sentinel-3A OLCI and AERONET-OC data, confirming the high quality of the ocean color products. And compared with PACE OCI, although there is a certain deviation in ultraviolet band, it still has a high correlation.

The central pattern generator (CPG) is widely applied in biped gait generation, and the particle swarm optimization (PSO) algorithm is commonly used to solve optimization problems for CPG network controllers. However, the canonical PSO algorithms fail to balance exploration and exploitation, resulting in reduced optimization accuracy and stability, decreasing the control effectiveness of CPG controllers. In order to address this issue, a balanced PSO (BPSO) algorithm is proposed, which achieves better performance by balancing the algorithm’s exploration and exploitation capabilities. The BPSO algorithm’s solving process consists of two phases: the free exploration phase (FEP), which emphasizes exploration, and the attention exploration phase (AEP), which emphasizes exploitation. The proportion of each phase during optimization is controlled by an adjustable parameter. The BPSO algorithm is subjected to qualitative, numerical, convergence, and statistical analyses based on 13 benchmark functions. The experimental results from the benchmark functions demonstrate that the BPSO algorithm outperforms other comparison algorithms. Finally, a linear walking optimization method for humanoid robots based on the BPSO algorithm is established and tested in the Webots simulator. Comparative results with two other optimization methods show that the BPSO‐based optimization method enables the robot to achieve greater walking distance and smaller lateral deviation within a fixed number of iterations. Compared to the other two methods, walking distance increases by at least 60.98% and lateral deviation decreases by at least 1.96%. This research contributes to enhancing the locomotion capabilities of CPG‐controlled humanoid robots, enriching biped gait optimization theory and promoting the application of CPG gait control methods in humanoid robots.

The construction of a radiometric degradation model is vital for elucidating the performance alterations of ocean color satellite sensors following in-orbit operations. The Chinese ocean color satellites HaiYang-1C (HY1C) and HaiYang-1D (HY1D), operating as afternoon and morning satellites, respectively, conduct networked observations of the global ocean. However, there has been limited analysis of the radiometric performance of each ocean color sensor on HY1C/D. Therefore, to understand the performance change sensor, this study relies on the Satellite Calibration Spectrometer (SCS), a hyperspectral sensor mounted on HY1C/D. The radiometric degradation model of each sensor is constructed, to realize the tracking of the long-term radiometric performance trend of the sensors. First, the SCS ensures the accuracy of its observations through a high-precision solar calibration method. Using the abundant simultaneous earth observation data gathered by the SCS, this study realizes the radiometric calibration of each sensor, and the radiometric degradation model is further constructed to adjust the radiometric level and track and correct the sensor performance trend. The Chinese Ocean Color and Temperature Scanner (COCTS) is taken as an example, and the results show that the annual average radiometric degradation rate of COCTS-HY1C after launch ranges from 0.8% yr−1to 2.4% yr−1, while that of COCTS-HY1D ranges from 0.5% yr−1to 3.9% yr−1. These degradations were achived by the SCS that allowed the sensors onboard HY1C/D to perform in-orbit radiometric calibration independently of external data sources and continuously generate high-quality Climate Data Records (CDRs).

Satellite Calibration Spectrometer (SCS) onboard HY-1C/HY-1D could support a direct calibration using the Simultaneous Nadir Overpass (SNO) approach for imaging spectroradiometers with various band configurations on the same or different satellite platforms by providing precise hyperspectral radiance after onboard calibration employing a solar diffuser (SD). However, a unique phenomenon was discovered in the analysis of annual variations of onboard calibration coefficients of the SCS. Although the solar calibration scheme employing an SD was used, a season-dependent oscillation still existed, and the oscillation trend of calibration coefficients is highly correlated with the variation trend of solar beta angle. In this article, we analyzed the entire solar calibration results of SCS from HY-1C using more than three years of data and discovered that season-dependent oscillation is primarily due to the variations in the transmittance of the solar attenuation screen affected by satellite platform attitude. Furthermore, an accurate variation model of the incident angle on the solar attenuation screen was developed, and the findings demonstrate that the season-dependent oscillation of onboard calibration coefficients of SCS could be effectively removed. Finally, the model established in this article is validated by optical simulation, which shows its consistency and reliability.

The Chinese Ocean Color and Temperature Scanner (COCTS) on board HY-1 series satellites has two thermal infrared channels with the spectrum range of 10.30-11.40 μm and 11.40-12.50 μm for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in this study. The Bayesian cloud detection algorithm that has been developed is based on the Bayes’ theorem and uses simulation of COCTS observations. The MODerate resolution atmospheric TRANsmission (MODTRAN) model was used for simulation of COCTS brightness temperatures. SSTs were retrieved from COCTS by OE from 2009 to 2011 in the northwest Pacific. Comparison of COCTS OE SST with in situ SST showed that the COCTS SSTs are cooler than buoy measurements by –0.23 °C on average, and the standard deviation (SD) of differences was 0.51 °C. A large component of the mean difference is attributable to the cool skin effect at the ocean surface (typically –0.15 to –0.2 °C), the remainder being attributable to simulation and calibration biases. The mean difference of COCTS OE SST with matched skin temperatures from the Advanced Along Track Scanning Radiometer (AATSR) is closer to zero, being –0.09 °C, with a SD of 0.49 °C. These validation results of COCTS OE SST demonstrate that Bayesian cloud detection and OE SST retrieval algorithm work well for improving COCTS SST accuracy, and show the potential of these methods to help develop SST products for operational HY-1 satellites, HY-1C and HY-1D.

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.

The Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite was launched on September 7, 2018, and has been providing global multispectral Earth observation data as a new spaceborne sensor for ocean color detection since September 10, 2018. In this study, an atmospheric correction algorithm using a composite of the algorithms developed by Wang and Gordon (1994) and Heet al.(2004) and a chlorophyll-a concentration retrieval method that is a composite of the OC4 and color index (CI) algorithms are used to derive the global chlorophyll-a concentration from COCTS. The retrieval products are validated againstin situdata measured in the East China Sea and the South China Sea during the in-orbit testing activity for the HY-1C satellite with an unbiased percentage difference (UPD) of 39%, and the data are also validated against the Aerosol Robotic Network-Ocean Color (AERONET-OC) data with a UPD of 39%. The daily global chlorophyll-a concentration from COCTS with a gridded resolution of 9.2 km and a time period from September 10, 2018 to February 29, 2020 is compared with the same products from MODIS and VIIRS. The UPDs of COCTS against MODIS onboard Terra are approximately 20%, and the mean biases (in logarithm form) are approximately zero. Examples of chlorophyll-a concentration retrieval results in specific cases along the eastern coast of China and the Kuroshio Current are also presented to show the performance of the algorithms used in this study. The retrieval and evaluation results show that COCTS onboard HY-1C demonstrates satisfactory performance for global chlorophyll-a concentration observations.

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient α. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured α and MC-simulated α at different stations (r = 0.95) and at various FOVs (r = 0.96). The results indicate the reliability of the developed lidar system.

A forward Monte Carlo 3-D (FMC3D) model is developed for simulating light fields in a volume of water where the boundary conditions for radiance values can be expressed by mathematical formulas, which cannot be done using the radiative transfer models currently available such as HydroLight. Water is assumed parallel homogenous for these models, which are incapable of investigating the sidewall reflectance effect on the light fields. These ones are called quasi-3-D radiative transfer models. The FMC3D model perfects the assumption and the incapability and is validated using the in situ data measured in the tank experiment. The FMC3D model is first applied to investigate the sidewall reflectance effect on the remote sensing reflectanceRrsfor waters in a fabricated tank with infinite depth and different radii. The investigation shows that the effect is decreasing with the increase in the tank radius and that the minimum radius that the effect is negligible for highly scattering water is bigger than that for highly absorbing water. Taking the tank used in the experiment carried out in a previous work by Han and Rundquist as an example, the FMC3D model is second applied to investigate the combining effects onRrsfrom bottom and sidewall reflectances. Compared withRrsfor open water, theRrsfor tank water having the same inherent optical properties is underestimated. The underestimation is increasing with the increase in the single scattering albedo ω and can be up to 32% for water with ω = 0.88, showing that the effects cannot be removed by the black inside wall, which is a method commonly used in tank experiments. The potential applications of the FMC3D model are discussed, taking the examples of the correction for the wall reflectance effect on apparent spectra measured in tank experiments and of the scattering error correction for the reflective tube absorption coefficient measured using a WET Labs AC-9 or AC-S device.