The deep learning method based on multi-channel convolutional neural network(MCCNN) can be used for gravity recovery from altimetry and bathymetry data. Compared with the traditional inverse Vening Meinesz (IVM) method, the MCCNN method enhances gravity accuracy but introduces long-wavelength gravity errors in training data-scarce areas. The Enhanced MCCNN method (EMCCNN) is proposed, which treats geo-locations in the input layer as nodes rather than channels. Evaluated by independent ship-borne data, the EMCCNN method demonstrates a gravity accuracy improvement of 0.05-0.24 mGal compared to the IVM method, maintaining consistent gravity accuracy compared to the MCCNN method. Additionally, assessed by a recognized global marine gravity model, the EMCCNN method achieves the highest accuracy. Particularly in regions with limited training data, the EMCCNN method outperforms the MCCNN method in minimizing gravity differences relative to assessment data. In the wavelengths larger than 100 km, the gravity noise for EMCCNN method can be reduced by up to about 50% compared to MCCNN method. These findings highlight EMCCNN’s effectiveness in improving gravity recovery in training data-scarce areas.

The azimuth cutoff of interferometric radar altimeter (IRA) image, which is acquired at small incidence angles, is mainly determined by the vertical component of the orbital velocity of ocean waves and is almost independent of the wave direction. Using this property, an inversion method for wind waves and wind speed has been proposed based on the azimuth cutoff of IRA image in combination with the Elfouhaily wind wave spectrum, which can effectively solve the problem of small-scale wind wave parameters loss caused by velocity bunching. In the present work, the wind speed and the significant height of wind waves (SH$_{\mathrm {ww}}$) have been retrieved from five pairs of airborne IRA images acquired in offshore areas. The results show that the differences between SHww retrieved from the five pairs of IRA images used in this article by the new method and the reference SHww are acceptable in ocean wave inversion. However, if the wind fetch is small and the wind direction is inconsistent with wave propagation direction, there is a significant difference between the retrieved wind speed and the reference wind speed when using the new method to retrieve wind speed. Moreover, the results also show that for the sea area with infinite wind fetch, the inversion accuracy of wind speed and SHww determined by the accuracy of the retrieved radar radial significant orbital velocity of wind waves (SV$_{\mathrm {ww}}$). However, for the sea area with finite wind fetch, the inversion accuracy would also be affected by wind fetch.

To address challenges in large-scale deployments and real-time wave observations using Global Navigation Satellite System (GNSS) buoys, we assessed the reliability and precision of the single-frequency L1 time-differenced carrier phase (TDCP) method for determining significant wave heights (SWHs) and average wave periods. Utilizing simulated dynamic velocity measurements derived from International GNSS Service static observations, this study demonstrates that the L1 TDCP method exhibits superior performance compared to dual-frequency TDCP, postprocessed kinematic (PPK), and Doppler algorithms. Notably, L1 TDCP achieves approximately five times greater precision than the Doppler method in vertical direction assessments. In the offshore waters of Qingdao, China, we deployed two GNSS buoys for wave observation experiments, complemented by a nearby accelerometer-equipped wave buoy (WB) for external validation. By integrating the velocity measurements obtained from L1 TDCP, we derived the sea surface displacement, which, after applying a high-pass filter, isolated the wave components. The experimental results confirmed a strong correlation between L1 TDCP and PPK methods in capturing SWH and wave periods. This study found a negligible difference between L1 TDCP and PPK in SWH (0 ± 7.1 mm, 99% correlation) and in average wave period (0 ± 0.13 s, 97% correlation). The wave observations from two independent GNSS buoys at the same location also exhibited remarkable consistency, with a difference in SWH of 0.0 ± 2.5 cm and in average wave period of 0.0 ± 0.1 s. Compared to the independent WB, the L1 TDCP method demonstrated a difference in SWH of$- 1.9\,\,\pm \,\,5.8\,\,\text {cm}$with a 93% correlation and in average period of 0 ± 0.1 s with a 94% correlation. Further, our analysis into GNSS data sampling frequencies highlighted the efficacy of 1-Hz GNSS data in wave inversion though certain spectral limitations were noted. Implementing the L1 TDCP method at 1 Hz offers a substantial reduction in associated hardware costs, storage requirements, and computational demands. Notably, in contrast to PPK, it negates the need for delayed precise ephemeris, facilitating real-time computations. This study highlights the potential of L1 TDCP for broader, real-time GNSS buoy wave observations, harmonizing with the United Nations Decade of Ocean Science’s aspirations for an augmented Global Ocean Observing System.

The topography of the seafloor is highly correlated with the local gravity through intrinsically nonlinear relationships across a particular wavelength band. The purpose of this study is to compare a fully connected deep neural network (FC-DNN) and a convolutional neural network (CNN) with the gravity-geologic method (GGM) to determine whether deep learning can provide superior predictions of bathymetry. We include the short-wavelength gravity and geological models as training parameters, and assess the performance of different models and parameter combinations using various inputs. Compared with the CNN method, the FC-DNN with the short-wavelength gravity as an input reduces the standard deviation of bathymetry differences from 118.6 m to about 73.5 m. The FC-DNN with short-wavelength gravity reduces the standard deviation of bathymetry differences by up to 13.3% compared with the conventional GGM. Furthermore, we demonstrate that the addition of geological information alongside the short-wavelength gravity does not significantly enhance the accuracy. Power spectral density analysis suggests that the FC-DNN is superior for predicting wavelengths shorter than 6 km.

Wide swath near-nadir interferometric altimetry is a newly developed technology for sea surface height (SSH) measurement. However, the absence of actual measurement data makes this novel SSH mapping technique difficult to verify and apply for wide swath interferometric altimeters. To verify the designed performance of the scheduled wide swath single-pass interferometric altimeter in the "Guanlan Mission", an airborne campaign was carried out off the coast of Rizhao, China on November 16, 2020. An airborne dual-frequency interferometric radar altimeter system (ADIRAS) with a single-pass mode was utilized for SSH measurement as the first flight. Two pioneering and fundamental works have been conducted: an intensive altimetry error analysis according to the ADIRAS parameter settings along the incident direction, an effective SSH reconstruction approach based on a multichannel likelihood (ML) function, and detailed validation procedures through airborne campaigns illustrated in this study. The results indicated that the difference between the wave-induced sea surface elevation (WSSE) variances derived by the ML approach and GNSS buoy was 2 cm2, which was smaller than the results of the single band on Ku (11 cm2) and Ka (6 cm2). Moreover, the estimated Significant Waves Height (SWH) bias of joint bands was 10 cm, which was also superior to that of Ku (39 cm) and Ka (24 cm). Both simulated data and real airborne dual-frequency InSAR data were employed in this study for cross-validation of the proposed method. This approach represents an effective technique for SSH reconstruction of future spaceborne/airborne interferometric altimeters.

Based on the nonlinear relationship between multi-source marine geodetic data and seafloor topography, the multilayer perceptron (MLP) neural network is introduced into bathymetry prediction to improve the accuracy of bathymetry model. This method not only integrates multi-source marine geodetic data, but also takes into consideration the nonlinear relationships between these data and seafloor topography. Firstly, we utilize terrain information and the multi-source marine geodetic data (vertical deflection, gravity anomaly, vertical gravity gradient, mean dynamic topography) around the shipborne sounding control points within a 6’×6’ grid as input data, while using the actual bathymetry at control points as output data to train the MLP neural network model. Subsequently, inputting the input data from the central point of a 1’×1’ grid within the study area into the MLP model to predict the bathymetry at the grid’s center. Then, based on the predicted bathymetry, a bathymetry model is established of this research area. Utilizing this methodology, this paper establishes the Gulf of Mexico Bathymetric Chart of the Oceans (MBCO1) model. Due to the influence of complex seafloor topography and the distribution of shipborne bathymetry points, there are differences in training and prediction among different regions. To address this, this study divides the research area into five sub-regions (A, B, C, D, and E) and establishes bathymetry model (MBCO2 models) through each sub-region. Finally, we evaluated the accuracy and effectiveness of this method by comparing it with existing bathymetry models, as well as shipboard depths.

The satellite altimetry missions could measure high-accuracy sea surface heights (SSHs) that can be used to recover the marine gravity field. Traditional methods for estimating the marine gravity field from SSHs all rely on approximate physical correlations between SSHs and gravity, which may neglect nature’s complex nonlinearity. This work presents a new deep network-based method to recover the gravity anomaly. This new method uses a multi-channel convolutional neural network (MCCNN) architecture to capture the nolinear features between ship-borne gravity and a group of input parameters including deflections of the vertical (DOVs), submarine topography and the geo-locations. To validate the gravity, ship-borne gravity anomalies on the two independent cruises were not used in the deep learning process. For comparison, we also estimated the gravity using the traditional inverse Vening Meinesz (IVM) method. Our results indicate that the MCCNN method can derive high-quality marine gravity anomalies. The assessments using 1 mGal-accuracy ship-borne gravity anomalies show that the average accuracy for gravity from MCCNN method is higher than 3 mGal and this method achieves 0.05-0.50 mGal improvement over benchmark methods IVM. Assessed by marine gravity anomaly models with the accuracy of 1-2 mGal, the MCCNN method has shown to improve the accuracy of gravity by at least 4%. Comparisons with the IVM results show that improvements of the MCCNN method were mainly in wavelengths between 8 km and 100 km due to the using of bathymetry. The results show that our deep learning method maintains good performance and is promising for gravity recovering.

This letter describes the new ability to measure wind-generated gravity waves using the airborne$Ka$-band interferometric altimeter (AirKaIA). Although the original definition of wave parameters is derived from wave-induced sea surface elevation (WSSE), it is difficult to directly measure the WSSE, so most remote sensing of waves use other types of signals to obtain wave parameters. Here, we present the measurement of 2-D WSSE from AirKaIA. A new wave retrieval method based on the 2-D WSSE has been developed, tested with simulation data, and applied to AirKaIA data. The results indicate that the retrieved dominant wave directions, dominant wavelengths, and significant wave heights from AirKaIA are in agreement within situmeasurements while highlighting the need for further method tests using more observations. The study of ocean signals from interferometric altimeter is an emerging research topic. AirKaIA’s measurements of wind-generated gravity waves with 2-D WSSE have implications for the assessment of future interferometric altimeter missions.

Interferometric radar altimeter (IRA) is a new ocean remote sensing sensor. It can be used to retrieve sea surface height (SSH) by means of cross-track interferometry. Compared with the traditional cross-track interferometric synthetic aperture radar (XT-InSAR), IRA works at very small incidence angles for higher altimetry sensitivity. In this case, multiple discontinuous surface scatterers at sea surface would be cut into a same range pixel which leads to severe layover. This layover induced by ocean waves will reduce the correlation between the master-slave images acquired by IRA and increase random interferometric phase noise. At present, how to quantitatively analyze the impact of the ocean wave layover on the decorrelation of IRA images is still a problem that needs in-depth discussion. In this letter, theoretical analysis of the effect of ocean waves on the decorrelation of IRA images has been carried out when the ocean waves layover is considered. And the theoretical results are also compared with the airborne IRA data. It is found that the layover of ocean waves has significant influence on the decorrelation between the master-slave IRA images, especially at very low incidence angles.

Global Navigation Satellite System (GNSS) buoys were used to retrieve the significant wave heights (SWHs) and dominant periods of waves in the Qingdao coastal waters off China. The precise point positioning (PPP) and postprocessed kinematic (PPK) techniques were used to obtain the absolute motion of the GNSS buoys. Even though PPP has relatively low absolute positioning accuracy, this accuracy has a minimal influence on the spectrum in the wave frequency band. SWH values calculated from the PPK and PPP modes are nearly identical, with correlation coefficients and symmetric regression slopes both higher than 0.99. The SWH values calculated from PPP also show good agreement with a dedicated wave buoy, with a correlation coefficient of 0.935 and a difference of 0.82 cm ± 4.63 cm. A GaoFen-1 satellite image was used to assess the GNSS PPP wave spectrum via the dispersion relationship; both exhibit wave spectra with two peaks, consistent with the wave characteristics in the area.

The interferometric radar altimeter (IRA) is one of the main payloads of the “Guanlan” ocean science satellite proposed by the National Laboratory for Marine Science and Technology of China. To evaluate the effectiveness and accuracy of the IRA in retrieving the ocean dynamic parameters, such as sea surface height (SSH), ocean wave spectrum, and wind speed, two airborne IRA experiments were carried out at Qingdao Xiaomaidao (XMD) sea area on March 31, 2019, and Rizhao sea area on November 16, 2020. In the present work, wave-induced sea surface elevation (SSE) and its spectrum have been retrieved based on the interferograms acquired by the airborne IRA. To suppress the random phase noise, a mean filtering algorithm has been used in the multilook process of calculating the complex IRA images. The results show that the size of the filter window has a significant effect on the retrieved SSE. If the size of the filter window along THE range direction is too large, the flat earth would cause the spectral density of the retrieved ocean wave to be higher. In addition, the comparisons of the retrieved spectra with the buoy measurements demonstrate that the swell can be well-retrieved by IRA images at low sea-state conditions with significant wave height (SWH) less than 0.7 m. However, for wind wave, because of the effect of the velocity bunching along the azimuth direction, the wind wave spectrum can be extracted only when it propagates approximately along the ground-range direction of the IRA images.

Calibration and validation (Cal/Val) of the sea surface height as measured by satellite radar altimeters is essential to understand altimeter biases, observation trends, and instrument aging. It also supports the long-term stability of the produced climate change records of sea level as determined by altimetry. In this article, we report the calibration of HY-2B and Jason-2/3 using the established research infrastructure and data sharing initiative introduced by the Altimetry Calibration Cooperation Plan (ACCP) of China. Currently, three ACCP calibration sites encompass the Wanshan Islands, and two national oceanic sites are located along the China coastline. For each Cal/Val site, the components of the facilities—the geodetic, sea level, and Global Navigation Satellite System (GNSS) infrastructure—and the followed monitoring procedures and calibration methods are described. The HY-2B performance was primarily evaluated using about two years data, which indicated a mean bias of −0.2 ± 4.2 cm. Confidence in the results is strong, because the HY-2B biases were cross compared and confirmed by all the three independent sites and the three satellite ground tracks. Compared with its predecessor HY-2A, HY-2B shows very stable observations with no linear drift at present. In addition, Jason-2 and Jason-3 were mainly assessed using Qianliyan site. Our results indicate that the Jason-3 sea-surface height bias is approximately 2–3 cm smaller than that of Jason-2 and that the long-term stability of Jason-2/3 shows no significant trend, which in good agreement with the international dedicated sites. The instrument noises of Jason-2/3 and HY-2B were estimated based on the ACCP sites. The results show that the instrument noise in the previous literature is underestimated. This was also consolidated by the result from wavenumber spectrum and global crossover point analysis. The code and Wanshan data used in these Cal/Val experiments are publicly available to facilitate further work in this domain (https://github.com/GenericAltimetryTools/CalAlti).

Validating the wet path delay (WPD) is essential to determining satellite microwave radiometer errors that may deteriorate sea level measurement accuracy. The global navigation satellite system (GNSS) is a favored method for assessing WPD for altimetry. Over Chinese coastal waters, however, validations have not been performed, primarily because of the large gaps between International GNSS Service sites. Herein, we report a long-term assessment of the Jason-2/3 radiometers and the initial performance of the Chinese HY-2B radiometer via comparisons of WPD values derived from three types of GNSS networks along the Chinese coast. A new method based on the second-order derivative of WPD along satellite tracks was developed to detect where land contamination first appears. The results indicate that this method is more sensitive to land influences; the distance at which land contamination first appears was estimated to be approximately 40–50 km over the China coast sea. To compare the WPD at different heights, such as GNSS sites and sea level surface, an exponential function containing the corresponding decay coefficients was adopted. The WPD decay coefficients for each GNSS site were recalculated using the 3-D ERA5 pressure level fields instead of the empirical value of 2000. The results show that the new coefficient may reduce the differences between WPD values at different heights. This WPD comparison over Chinese coastal waters indicates average WPD differences of 3 (Jason-2) and −1 mm (Jason-3), showing a high level of consistency between the GNSS sites and the satellite radiometers. The uncertainties in the WPD differences were estimated to be 14–21 and 7–24 mm for the Jason-2/3 and HY-2 radiometers, respectively, which is consistent with previous studies using global GNSS. After removing the GNSS uncertainty and the spatial decorrelation, the WPD uncertainties of the Jason-2, Jason-3, and HY-2 radiometers over the regional area were estimated to be 13, 14, and 11 mm, respectively. The long-term evaluation of the WPD differences with the GNSS sites indicates that the drift rates of the Jason-2/3 radiometers over China coast waters are 0.3 and −0.9 mm/y, respectively.

Multispectral methods for remote sensing image have been widely applied to shallow water bathymetry by researchers. In nonideal conditions, even with the same spectral radiance, the points still have a very wide range of water depths. This means that spectral features alone are insufficient for water bathymetry. Hence, we need to extract other valuable features from a remote sensing image. This letter introduces a spatial feature for water bathymetry using remote sensing images. We propose a model that utilizes a multilayer perceptron (MLP) to integrate the spectral and spatial location features. Experimental results demonstrate that the proposed model yields a substantial performance improvement. The mean relative error is only 8.41%, and the root mean square error is reduced by 34%–68% when compared with three other models. Furthermore, the proposed model addresses well the problems caused by heterogeneous bottom types.

In this paper, the research progress and its application of the Chinese calibration sites for satellite altimeters are described. The geodetic surveying and the surface subsidence monitoring over the Qianliyan calibration site using a permanent GNSS station are described. The new calibration results for HY-2A, Jason-2&3, Saral, and Sentinel-3A are presented in detail. Then, the Wanshan sites which is still under construction in the southern Chinese coastal area are introduced. In addition, the wet delay of troposphere measured by Jason-2 AMR in 2010-2016 is evaluated through one site from the Chinese coastal GNSS network, which proves the feasibility of calibrating microwave radiometer wet delay through the Chinese coastal GNSS network.

In this paper the calibration methodology, data and models, and the absolute bias of HY-2A, Jason-2, and Saral/AltiKa based on the Qianliyan CAL/VAL site will be presented.

The absolute SSH (sea surface height) biases of three satellite altimeters Jason-2, Saral/AltiKa and HY-2 were determined using our GPS buoy at the Qian Li Yan Island of China, which are 9.3cm, 1.3cm and 66.8cm, respectively. In addition, the altimetry SWH (significant wave height) were assessed using GPS retrieved SWH, which shows good agreement between the GPS buoy and satellite altimeters. The detailed method and result are described in the paper.