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.

Ocean color satellites are important tools in the field of environmental remote sensing, and their calibration accuracy during in-orbit operation is crucial. This study introduces a radiometric calibration method based on a hyperspectral reference source, aimed at evaluating discrepancies in onboard absolute radiometric calibration among different ocean color satellite sensors and providing more consistent ocean color remote sensing products. Specifically, the method uses a high-precision hyperspectral ocean color sensor, satellite calibration spectrometer (SCS), as a unified reference source to obtain correction coefficients for multiple sensors through radiometric calibration. Leveraging the hyperspectral capabilities of the SCS, the absolute radiometric calibration differences between sensors can be clearly analyzed after removing variations in the relative spectral response (RSR) functions. Simultaneously, the study employs the simultaneous nadir overpass (SNO) method to match the sensors, analyzing the factors that influence the SNO process. The study reveals that despite accounting for RSR differences, significant radiance disparities remain. The largest overall difference among five sensors reached 6.37%. However, applying the derived correction coefficients enhances the consistency of ocean optical and biogeochemical products in the global open ocean. Based on the results of the time series analysis, the average consistency of multisource satellite-derived chlorophyll-a (Chla) concentration products improved from 73.7% to 90.4%. This result demonstrates the effectiveness and necessity of the calibration evaluation method proposed in this study for enhancing the consistency of multisource data. Furthermore, this method can be applied to additional ocean color sensors in the future, as long as the necessary matching conditions between satellites are met.

System Vicarious Calibration (SVC) is essential for achieving high-precision ocean color products from satellite sensors, traditionally relying onin situmeasurements from stable calibration sites. However, data constraints due to environmental factors have prompted the exploration of Marine Pseudo-Invariant Sites (MPIS) as supplementary SVC sources. This study investigates the feasibility of MPIS for SVC by analyzing long-term MODIS-Aqua data to identify oceanic regions with stable optical properties suitable for calibration. Four MPIS were identified, and time-series models of remote sensing reflectance (Rrs) were constructed, demonstrating consistent variability patterns that support their suitability for SVC. These models were then applied to calculate SVC coefficients for VIIRS-SNPP and were validated againstin situmeasurements from the MOBY site. Results indicate that MPIS-derived SVC coefficients show less than 1.68% discrepancy across visible wavelengths compared to MOBY, enhancing SVC stability and reducing calibration timeframes. Additionally, the comparison between VIIRS-derived remote sensing reflectance, before and after applying the MPIS-based SVC coefficients, andin situdata further verifies the accuracy of the calibration coefficients. This study underscores MPIS’s potential to supplement traditional SVC methods, improving calibration coverage and enabling continuity in ocean color remote sensing missions.

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).

Solidago canadensis L. is a typical invasive plant that has become a significant threat worldwide and profoundly impacts local ecosystems. An unmanned aerial vehicle (UAV)-based semantic segmentation (SS) system can help in monitoring the spread and location of Solidago canadensis L. To identify the growth range of this species with greater efficiency, we employ a high-speed multispectral camera, which provides richer color information and features with limited resolution, in conjunction with a high-quality RGB camera to construct a segmentation dataset. We construct a validated UAV multispectral (UAVM) dataset comprising 3260 pairs of calibrated RGB and multispectral images. All the images in the dataset underwent semantic annotation at a fine-grained pixel level, with 12 categories being covered. In addition, other plant categories can be employed in precision agriculture and ecological conservation. Moreover, we propose a benchmark model, UAVM semantic segmentation network (UAVMNet). With the aid of the feature alignment module and the UAVMFuse module, UAVMNet efficiently integrates multispectral and high-quality RGB image information, enhancing its ability to perform semantic segmentation tasks effectively. To the best of our knowledge, this is the first model to colearn semantic representations via high-quality RGB and paired multispectral information on a UAV platform. We conduct comprehensive experiments on the proposed UAVM dataset.

In recent years, there has been a growing demand for the colorization of remote sensing images due to their inherent limitations caused by remote sensors, such as hazy or noisy atmospheric conditions. These factors result in the captured images needing to be clarified. Compared to ordinary images, remote sensing images present unique challenges in color recovery due to their imbalanced spatial distribution of objects. In this article, we propose a novel bidirectional layout-semantic-pixel joint decoupling and embedding network (BDEnet) following the idea of human painting to generate highly saturated color images with strong spatial consistency and object salience. The proposed BDEnet model emulates the process of human painting through a step-by-step approach. It begins by determining the overall tone of a large macroscopic region and progressively refining the local color based on this initial assessment. Specifically, BDEnet incorporates finer-grained semantics and pixel color information into a colored layout that represents a wide range of continuous areas, thereby accomplishing the colorization task. The BDEnet model operates at three scales, namely the layout (macro), semantic (medium), and pixel (micro) scales. It comprises three key modules: the multiscale feature decoupling (MFD) module, the layout-semantic-pixel multigranularity learning (MGL) module, and the semantic-pixel embedding (SPE) module. MFD module effectively reduces redundant noise from the semantic and layout scales by employing scale decoupling. This process ensures the extraction of efficient features essential for MGL. In the MGL module, three branches with different scales are employed to achieve layout division, semantic segmentation, and pixel coloring. To address the issue of insufficient category label guidance in layouts, we propose a novel approach called similar semantic merging (SSM) using a weakly supervised scheme to accomplish layout division. Finally, the SPE module incorporates stable semantic and pixel information into the layout features. This integration results in the generation of color images that exhibit strong spatial consistency, emphasize object salience, and possess high color saturation.

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.

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.

Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient Kd(λ) from remote sensing reflectance Rrs(λ) in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful Kd(λ) retrieval algorithms in UVR, the results showed that KpcaUV (with R2: 0.970 and RMSE: 14.0%) performed similar to SeaUVc (withR2: 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain Kd(380) for the continuous observation of the large area.

Jiaozhou Bay is a semi-enclosed basin in the western part of Yellow Sea in China. Because of eutrophication and pollution in the bay, it is increasingly urgent to monitor the water quality effectively in real-time. In the summer of 2014, we conducted a two-day in-situ optical measurement in Jiaozhou Bay and Qingdao coast to better understand the optical properties of the area. The inherent optical parameters show a typical characteristic of coastal waters. Light absorption is dominated by phytoplankton and CDOM. The scattering property of Jiaozhou Bay is more significant than that of Qingdao coast. Particulate attenuation, scattering and backscatting spectra all have power-law shapes.