Bin Zou

Sea ice concentration (SIC) and thickness (SIT) are critical variables for polar research. In this study, the potential of Tianmu-1 GNSS-R observations for retrieving Arctic SIC and SIT is explored using machine learning algorithms. XGBoost demonstrated superior accuracy and efficiency in the comparison of the three methods. For SIC retrieval, 14 parameters from Tianmu-1 were employed directly, whereas SIT retrieval incorporated additional auxiliary parameters, including SIC, sea ice salinity (S), and temperature (T). Among the different GNSS systems, GLO achieved the lowest RMSE for SIC, at 7.750%, whereas GAL performed comparatively poorly, with an RMSE of 10.475%. In SIT retrieval, the GPS and BDS yielded the smallest RMSE values of 0.276 m and 0.278 m, respectively, while GLO resulted in a slightly higher RMSE of 0.309 m. Daily retrievals of both the SIC and SIT were conducted from 18 October 2023 to 12 April 2024, with consistently stable evaluation metrics throughout the freezing season. In high-concentration regions, the retrieved SIC and SIT closely matched the reference data, whereas larger errors occurred in marginal ice zones and coastal areas. This study reveals the potential of Tianmu-1 to complement existing satellite missions in Arctic sea ice monitoring during the freezing period.

The increasingly significant global warming has severely affected the Arctic region. The melting speed of sea ice is accelerating, and its spatial coverage is gradually shrinking, causing a series of ecological problems that cannot be ignored. Therefore, conducting in-depth research on the sea ice conditions in the Arctic region is crucial. This study developed a Sea Ice Concentration Inversion model suitable for the Arctic using remote sensing data from HY-1C/D Chinese Ocean Color and Temperature Scanner (COCTS). The model's accuracy was tested using a confusion matrix and measured data. The results indicated that the model achieved an accuracy rate of 89% in sea ice extraction. Its mean absolute percentage error is lower and consistent with the other similar products. The model was applied to COCTS image data from Apr. to Sep. 2020 and 2021, revealing the monthly distribution characteristics of sea ice concentration in the Arctic region. The results indicated that sea ice concentration in the central Arctic region consistently remains high, exceeding 90% throughout the year. However, further analysis of various subregions revealed a declining trend in sea ice concentration in the Chukchi Sea, Baffin Bay, and the Laptev Sea. In contrast, the fluctuation of sea ice concentration in Greenland, Barents, and Kara was not noticeable. The findings show that the sea ice extraction model developed effectively assesses sea ice conditions in the Arctic region. It successfully demonstrates the spatial and temporal distribution of Arctic sea ice concentration and provides valuable support for ongoing research and development in sea ice inversion.

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.

Ship detection in synthetic aperture radar (SAR) images using a convolutional neural network (CNN) has become a hotspot. However, dense multi-size ship targets in inshore scenes, proximity of ship targets to man-made targets, and datasets containing only one class limit the performance of ship detection methods. In pursuit of attaining heightened performance, most existing methods endeavor to augment the number of parameters and enhance network complexity. To address these problems, this letter proposes an anchor-free ghost feature extraction and cross-scale interaction network (GFECSI-Net), which improves detection performance through an efficient implementation, thereby avoiding the increase in the number of parameters or network complexity. First, to enhance the capability of feature extraction for ship targets, a multi-scale adaptive feature pyramid network (MSAFPN) is proposed to realize intensive information interaction and cross-scale feature fusion between different feature maps. Meanwhile, a selective efficient channel attention module (SECAM) is designed to enable the network to prioritize channels that better characterize ship targets. Besides, a GPU-efficient backbone for generating ghost feature maps and a task alignment detection head are integrated into GFECSI-Net. Comparison results with nine state-of-the-art CNN methods on a high-resolution SAR image dataset (HRSID) and a large-scale multi-class SAR target dataset (MSAR-1.0) indicate that GFECSI-Net achieves superior detection performance in F1-Score and mAP with a small number of parameters.

Accurate Noctiluca scintillans bloom (NSB) recognition from space is of great significance for marine ecological monitoring and underwater target detection. However, most existing NSB recognition models require expert visual interpretation or manual adjustment of model thresholds, which limits model application in operational NSB monitoring. To address these problems, we developed a Noctiluca scintillans Bloom Recognition Network (NSBRNet) incorporating an Inception Conv Block (ICB) and a Swin Attention Block (SAB) based on the latest deep learning technology, where ICB uses convolution to extract channel and local detail features, and SAB uses self-attention to extract global spatial features. The model was applied to Coastal Zone Imager (CZI) data onboard Chinese ocean color satellites (HY1C/D). The results show that NSBRNet can automatically identify NSB using CZI data. Compared with other common semantic segmentation models, NSBRNet showed better performance with a precision of 92.22%, recall of 88.20%, F1-score of 90.10%, and IOU of 82.18%.

Polar sea ice profoundly affects atmospheric and oceanic circulation and plays a significant role in climate change. Sea ice concentration (SIC) is a key geophysical parameter used to quantify these changes. In this study, we determined SIC products for the Arctic and Antarctic from 2019 to 2021 using data from the Chinese marine satellite Haiyang 2B (HY-2B) with an improved bootstrap algorithm. Then the results were compared with similar operational SIC products and ship-based data. Our findings demonstrate the effectiveness of the improved algorithm for accurately determining SIC in polar regions. Additionally, the results of the study demonstrate that the SIC product obtained through the improved bootstrap algorithm has a high correlation with other similar SIC products. The daily average SIC of the different products showed similar inter-annual trends for both the Arctic and Antarctic regions. Comparison of the different SIC products showed that the Arctic BT-SMR SIC was slightly lower than the BT-SSMIS and BT-AMSR2 SIC products, while the difference between Antarctic SIC products was more pronounced. The lowest MAE was between the BT-SSMIS SIC and BT-SMR SIC in both regions, while the largest MAE was between the NT-SMR and BT-SMR in the Arctic, and between the NT-SSMIS and BT-SMR in the Antarctic. The SIE and SIA time series showed consistent trends, with a greater difference in SIA than SIC and a slight difference in SIA between the BT-AMSR2 and BT-SMR in the Arctic. Evaluation of the different SIC products using ship-based observation data showed a high correlation between the BT-SMR SIC and the ship-based SIC of approximately 0.85 in the Arctic and 0.88 in the Antarctic. The time series of dynamic tie-points better reflected the seasonal variation in sea ice radiation characteristics. This study lays the foundation for the release of long-term SIC product series from the Chinese autonomous HY-2B satellite, which will ensure the continuity of polar sea ice records over the past 40 years despite potential interruptions.

More convolutional neural network (CNN) methods are widely utilized for ship detection in synthetic aperture radar (SAR) images. Nevertheless, there are still some problems that need to be addressed. First, detection methods may misclassify islands and onshore objects as ships. Second, due to the large scale and low resolution, a majority of ships are regarded as small targets and difficult to detect. Third, due to the different sizes of ships, the hyperparameters of anchor-based methods are difficult to set. Thus, we propose an anchor-free method called improved fully convolutional one-stage object detector (Improved-FCOS) to solve these problems. First, a multilevel feature attention mechanism is proposed to extract effective features and collect global context information. Second, we propose a feature refinement and reuse module with two stages to refine small ship features. Third, a head improvement module is designed to optimize the methods for classification and localization of ship targets. Finally, a modified varifocal loss is adopted to better train the classification branch. We conduct ablation experiments on LS-SSDD-v1.0 and verify the detection performance of the proposed modules. Additionally, we compare the Improved-FCOS with other state-of-the-art methods by experiments on three datasets, including the SAR-Ship-Dataset, HRSID, and LS-SSDD-v1.0. Detection results show that Improved-FCOS obtains the best detection performance on all datasets and indicate that the Improved-FCOS is more accurate and robust.