The HY-2B satellite was successfully launched on 25 October 2018. One of the main payloads of the HY-2B was a radar altimeter. In the present study, the quality of the HY-2B along-track sea surface heights (SSH), significant wave heights (SWH), and sea surface wind speeds (SSWS) were assessed, including their precision and accuracy. In order to achieve this goal, the mono-mission metrics of the HY-2B were analyzed and compared with those of the Jason-2 and Jason-3 over the same periods of time. The results of both direct comparison and cross comparison methods were presented in this study. The comparison results indicated that the quality of the HY-2B satellite’s geophysical data records (GDRs) data was excellent, with 95% of the sea surfaces effectively observed between 82 degrees north and south latitudes. In addition, the standard deviation of the sea level anomalies (SLA) at the single mission crossovers was 4.6 cm to 5.8 cm, and at the dual-crossovers with Jason-3, the standard deviation was determined to be 5.1 cm to 5.8 cm. The accuracy levels of the significant wave heights and products of the HY-2B satellite radar altimeter were observed to be greater than 0.3 m and 1.4 m/s (STD), respectively. Therefore, it was concluded in this study that the data quality and system performance of the HY-2B satellite were excellent and stable, and could be widely utilized in such fields as global sea-level change monitoring, wave numerical assimilation predictions etc.

HY2B is now the latest altimetry mission that provides global nadir significant wave height (SWH) and sea surface wind speed. The validation and calibration of HY2B are carried out against National Data Buoy Center (NDBC) buoy observations from April 2019 to April 2020. In general, the HY2B altimeter measurements agree well with buoy observation, with scatter index of 9.4% for SWH, and 15.1% for wind speed. However, we observed a significant bias of 0.14 m for SWH and −0.42 m/s for wind speed. A deep learning technique is novelly applied for the calibration of HY2B SWH and wind speed. Deep neural network (DNN) is built and trained to correct SWH and wind speed by using input from parameters provided by the altimeter such as sigma0, sigma0 standard deviation (STD). The results based on DNN show a significant reduction of the bias, root mean square error (RMSE), and scatter index (SI) for both SWH and wind speed. Several DNN schemes based on different combination of input parameters have been examined in order to obtain the best model for the calibration. The analysis reveals that sigma0 STD is a key parameter for the calibration of HY2B SWH and wind speed.

In this study, China’s first altimeter satellite Haiyang-2A (HY-2A) data combined observations from CryoSat-2, SARAL/AltiKa, and Jason-1&2 are used to calculate the global (60°S–60°N) marine deflections of the vertical and gravity anomalies named Global Marine Gravity Anomaly Version 1(GMGA1), with grid resolution of 1′ × 1′. The deflections of the vertical from each satellite observations are first derived from the gradients of the geoid height through the least squares method. The deflections of the vertical are then merged by assigning different weights to each satellite product based on their accuracy. Finally, gravity anomalies are obtained by the remove-restore method. The results reveal that the fused deflections of the vertical have an accuracy of 0.4 arcsec in the north component and 0.8 arcsec in the east component. HY-2A’s contribution to the north component of the integrated deflections of the vertical is second only to Cryosat-2. Jason-1/2 accounts for a large proportion of the integrated east components. Compared to worldwide products such as DTU17, Sandwell & Smith V31.1, as well as values from EGM2008, EIGEN-6C4 and XGM2019e_2159, GMGA1 has an accuracy of around 3.3 mGal. By not using HY-2A data, the precision of GMGA1 is reduced by about 0.1 mGal. To further improve the accuracy, seafloor topography information is used to provide short wavelength gravity anomaly. It is verified in the South China Sea (112°E–119E°, 12°N–20°N) using the Parker formula. By combining shipborne depth generated data and GMGA1 through a filtering technique, a new version of gravity anomaly grid with an accuracy improvement of 0.4 mGal in the South China Sea is obtained. Graphical Abstract

Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry’s overall development and research front.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult, and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this paper, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves a SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

Global sea level rise is both a major indicator and consequence of global warming. At present, global warming is causing sea level rise in two main ways: one is the thermal expansion of sea water, and the other is the injection of large amounts of fresh water into the ocean after glaciers and ice sheets melt. In this paper, satellite altimeter data are used to study the total changes of global sea level from 2002 to 2020. Different from most previous studies, this study proposes a calculation method of sea level anomaly using only the along track altimetry data, which is similar to considering the satellite points as tide gauges, in order to avoid the error caused by interpolation in the map data. In addition, GRACE satellite data are used to calculate the changes of global sea level caused by water increase; temperature and salinity data are used to calculate the changes from ocean thermal expansion. Next, using satellite altimetry data, the calculation results show that the global sea level rise rate in the period of 2002–2020 is 3.3 mm/a. During this period, the sea level change caused by the increase of sea water calculated with GRACE satellite data is 2.07 mm/a, and that caused by the thermal expansion of seawater is 0.62 mm/a. The sea level rise caused by the increase of water volume accounts for 62.7% of the total sea level rise.