Search Results (10)

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article

Chlorophyll-a (Chla) and suspended particulate matter (SPM) are key indicators of water quality, playing critical roles in understanding marine biogeochemical processes and ecosystem health. Although satellite data from the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the Haiyang-1C/D satellites is freely available, there has been limited validation of its standard Chla and SPM products. This study is a first step to address this gap by evaluating COCTS-derived Chla and SPM products against in situ measurements in French coastal waters. The matchup analysis showed robust performance for the Chla product, with a median symmetric accuracy (MSA) of 50.46% over a dynamic range of 0.13–4.31 mg·m⁻³ (n = 24, Bias = 41.11%, Slope = 0.93). In contrast, the SPM product showed significant limitations, particularly in turbid waters, despite a reasonable performance in the matchup exercise, with an MSA of 45.86% within a range of 0.18–10.52 g·m⁻³ (n = 23, Bias = −14.59%, Slope = 2.29). A comparison with another SPM model and Moderate Resolution Imaging Spectroradiometer (MODIS) products showed that the COCTS standard algorithm tends to overestimate SPM and suggests that the issue does not originate from the input radiometric data. This study provides the first regional assessment of COCTS Chla and SPM products in European coastal waters. The findings highlight the need for algorithm refinement to improve the reliability of COCTS SPM products, while the Chla product demonstrates suitability for water quality monitoring in low to moderate Chla concentrations. Future studies should focus on the validation of COCTS ocean color products in more diverse waters. Full article

China’s marine satellite infrared radiometer SST remote sensing observations began relatively late. Thus, it is essential to evaluate and correct the SST observation data of the Ocean Color and Temperature Scanner (COCTS) onboard the China HY-1C satellite in the Southeast Asia seas. We conducted a quality assessment and correction work on the SST of the China COCTS/HY-1C in Southeast Asian seas based on multisource satellite SST data and temperature data measured by Argo buoys. The accuracy evaluation results of the COCTS SST indicated that the bias, Std, and RMSE of the daytime SST data for HY-1C were −0.73 °C, 1.38 °C, and 1.56 °C, respectively, while the bias, Std, and RMSE of the nighttime SST data were −0.95 °C, 1.57 °C, and 1.83 °C, respectively. The COCTS SST accuracy was significantly lower than that of other infrared radiometers. The effect of the COCTS SST zonal correction was most significant, with the Std and RMSE approaching 1 °C. After correction, the RMSE of the daytime SST and nighttime SST data decreased by 32.52% and 42.04%, respectively. Full article

A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (R_rs) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of R_rs565 and R_rs520/Rrs443, R_rs565/R_rs490, R_rs520/R_rs490, R_rs490/R_rs443, and R_rs670/R_rs565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m³, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. Full article

The data quality of the remote sensing reflectance (R_rs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has been applied to process the data of the Chinese Ocean Color and Temperature Scanner (COCTS) on HY-1C/1D. The accuracy of the R_rs products was evaluated by the in situ dataset from the Marine Optical BuoY (MOBY) with a mean relative error (MRE) of −1.56% and a mean absolute relative error (MAE) of 17.31% for HY-1C. The MRE and MAE of HY-1D are 1.05% and 15.68%, respectively. The comparisons of the global daily R_rs imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra show an MRE of 10.94% and an MAE of 21.38%. The comparisons between HY-1D and Aqua exhibit similar results, with an MRE of 13.31% and an MAE of 21.46%. The percentages of valid pixels of the global daily images of HY-1C and HY-1D are 32.3% and 32.6%, much higher than that of Terra (11.9%) and Aqua (11.9%). The gaps in the 8-day composite images have been significantly reduced, with 83.9% of valid pixels for HY-1C and 85.4% for HY-1D, which are also much higher than that of Terra (52.9%) and Aqua (50.9%). The gaps due to the contamination of sun glint have been almost removed from the 3-day composite imagery, with valid pixels of 63.5% for HY-1C and 65.6% for HY-1D, which are higher than that of the 8-day imagery of Terra and Aqua. The patterns of HY-1C imagery exhibit a similarity with those of HY-1D, but they are different on a pixel scale, mainly due to the changes in the ocean dynamic features within 3 h. The evaluations of the COCTS indicate that the imagery of HY-1C/1D can be used as a kind of standard product. Full article

China’s first operational ocean color satellite sensor, named the Chinese Ocean Color and Temperature Scanner (HY-1C-COCTS), was launched in September 2018 and began to provide operational data in June 2019. However, as a polar orbiting ocean color satellite sensor, HY-1C-COCTS would inevitably encounter regions impacted by large solar zenith angles when observing the high-latitude seas, especially during the winter. The current atmospheric correction algorithm used by ocean color satellite data processing software cannot effectively process observation data with solar zenith angles greater than 70°. This results in a serious lack of effective ocean color product data from high-latitude seas in winter. To solve this problem, this study developed an atmospheric correction algorithm based on a neural network model for use with HY-1C-COCTS data. The new algorithm used HY-1C-COCTS satellite data collected from latitudes greater than 50°N and between April 2020 and April 2021 to establish a direct relationship between the total radiance received by the satellite and the remote sensing reflectance products. The evaluation using the test dataset shows that the inversion accuracy of the new algorithm is relatively high under different solar zenith angles and different HY-1C-COCTS bands (the relative deviation is 3.37%, 7.05%, 5.10%, 5.29%, and 10.06% at 412 nm, 443 nm, 490 nm, 520 nm, and 565 nm, respectively; the relative deviation is 1.07% when the solar zenith angle is large (70~90°)). Cross comparison with MODIS Aqua satellite products shows that the inversion results are consistent. After verifying the accuracy and stability of the algorithm, we reconstructed the remote sensing reflectance dataset from the Arctic Ocean and surrounding high-latitude seas (latitude greater than 50°N) and successfully retrieved chlorophyll-a data and information on other marine ecological parameters. Full article

In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regularization iteration, are proposed to improve the accuracy of the COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics of these two schemes are discussed. The standard deviation of residual between retrieved SST and measured SST for these two data improvement algorithms, which are considered as the main indexes for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the generalization ability of the SST models under two data improvement methods is quantitatively compared, and the factors affecting the model accuracy are also carefully evaluated, including the in situ data acquisition method and measurement time (day/night). Finally, future works about SST retrieval with COCTS/HY-1C satellite data are summarized. Full article

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components. Full article

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. Full article

We analyzed the impacts of islands on suspended sediment concentration (SSC) in Zhoushan Coastal waters based on data from HY-1C, which was launched in September 2018 in China, carrying Coastal Zone Imager (CZI) and Chinese Ocean Color and Temperature Scanner (COCTS) on it for offshore observation. A new SSC retrieved model was established based on the relationship between in situ SSC and the reflectance in red and near infrared bands of CZI image. Fifteen CZI images obtained from October to December 2019 were applied to retrieve SSC in Zhoushan coastal waters. The results show that SSC in study area is 100–1600 mg·L⁻¹. The SSC near islands changes obviously. Upstream of the islands, SSC is lower than downstream. During the flood and ebb, when the current passes through the islands, circumfluence will appear, under certain geophysical factors, generating Karman vortex streets downstream of the islands. The sediments were stirred by the fast speed current at the outer side of vortex street to the sea surface inducing higher SSC at the outer side of the vortex street, while the central sediments of the vortex street were lower. In the direction of ocean currents, the SSC of the vortex street downstream of islands is changing regularly, i.e., increasing, then decreasing and increasing again and then decreasing in a snaking vortex street whose length downstream is between 1000 and 8000 m long. Full article