Altimeter Data Research Articles

Comments on Castro et al. (2021) “Relative sea-level curve during the Holocene in Rio de Janeiro, Southeastern Brazil: A review of the indicators - RSL, altimetric and geochronological data”

This work presents a few comments on the paper “Relative sea-level curve during the Holocene in Rio de Janeiro, Southeastern Brazil: A review of the indicators - RSL, altimetric and geochronological data”. Surprisingly the authors omitted in this review a former discussion on the interpretation of paleo-sea level indicator and paleo-sea level reconstructions on the Rio de Janeiro coast that questions their results. Therefore, at this present work we resume the previous discussion. We conclude that although at the commented paper the altitudes of the paleo-sea level indicators were determined precisely, they did not consider the vertical distance between a given a paleo-sea level indicator and its current homologous one, and thus do not indicate paleo-sea levels.

Ocean Image Formation Algorithm Using Altimeter Data for Next Generation Satellite SAR

Synthetic aperture radar (SAR) obtains two-dimensional images of the Earth’s surface. Spatial resolution is one of the most important factors in evaluating SAR performance. Therefore, high operating frequencies are preferred for obtaining high resolution images. In fact, Ku-band and Ka-band are used in the automobile SAR (Auto-SAR) and the aircraft SAR (Airborne-SAR). However, the operating frequencies of the satellite SAR (Spaceborne-SAR) are limited to the X-band because of the wide Doppler frequency band caused by the satellite’s high velocity. To overcome this frequency limitation in satellite SAR, we propose a low-complexity SAR algorithm that forms images of the ocean using satellite altimeter data with an operating frequency of the Ku-band (13.575 GHz). The algorithm requires the burst mode pulse transmission technique frequently used in SAR altimeters. To validate the proposed algorithm as being suitable for ocean image formation, point target simulation and analysis are performed, and some quantitative results are provided.

SAR Altimetry Data as a New Source for Swell Monitoring

AbstractThis article shows the first spectral analysis of fully‐focused Synthetic Aperture Radar (FFSAR) altimetry data with the objective of studying backscatter modulations caused by swells. Swell waves distort the backscatter in altimetry radargrams by means of velocity and range bunching. These swell signatures are visible in the tail of the waveform. By locally normalizing the backscatter and projecting the waveforms on an along‐/cross‐track grid, satellite altimetry can be exploited to retrieve swell information. The analysis of FFSAR spectra is supported by buoy‐derived swell‐wave spectra of the National Oceanic and Atmospheric Administration network. Using cases with varying wave characteristics, we discuss the altimetry‐derived spectra and relate them to what is known from side‐looking SAR imaging systems. Besides having a vast amount of additional data for swell‐wave analysis, altimeter data can also help us to better understand the side‐looking SAR spectra.

Accuracy comparison of marine gravity derived from HY-2A/GM and CryoSat-2 altimetry data: a case study in the Gulf of Mexico

SUMMARYThe HaiYang-2A (HY-2A) satellite was launched in August 2011, filling a gap in the field of Chinese altimetry. The application of HY-2A-measured altimeter data in gravity research needs to be studied. CryoSat-2 altimetry data play an important role in constructing global marine gravity models. Based on HY-2A and CryoSat-2 altimetry data of Level-2 plus products from March 2016 to June 2020, marine gravity anomaly models on 1′ × 1′ grids were derived by the inverse Vening–Meinesz (IVM) method over the Gulf of Mexico (80–100°W, 17–32°N). The accuracy of the altimeter-derived gravity was assessed by ship-borne gravity anomalies and the SIO V30.1 global marine gravity model. Then, integrated gravity models on 1′ × 1′ grids were constructed from the altimeter-derived gravity anomaly models by the weighted modification method and the combined altimetry data by IVM method. The results show that the accuracy of altimeter-derived marine gravity anomalies is affected by the coastline and complicated submarine topography. The accuracy of gravity anomalies derived from HY-2A is slightly lower than that derived from CryoSat-2 in the offshore region and regions containing complicated submarine topography, and is consistent with that derived from CryoSat-2 in other regions. The HY-2A-measured altimetry data play a significant role in constructing integrated gravity models. To conclude, the gravity anomalies derived from HY-2A have similar accuracy to those derived from CryoSat-2 during the same period, and HY-2A altimetry data can be used to construct high-precision global/local marine gravity models.

Bathymetry of reservoirs using altimetric data associated to optical images

The semiarid region of Brazil is highly dependent on reservoirs, and precise bathymetric data are essential for understanding the dynamics and storage variability of these water bodies. This study aimed to develop the application of bathymetric calculation using the observed water level and ICESat-2 satellite terrain profile data of large and medium-sized reservoirs. The combination of satellite images with altimetric data allows for the extraction of water contours, whose points contain elevation information. A digital terrain model (DTM) was generated, and an elevation-area-volume (EAV) curve was calculated for two reservoirs in Northeast Brazil, Sobradinho and Poço da Cruz, which have capacities of 34.116 and 0.4837 km3, respectively. The water level from ICESat-2 was compared to that of the observed data from the Sobradinho, and DTM and EAV curves were analyzed for the two reservoirs. Using the water level from Sobradinho reservoir stations, this method covered 99% of the useful volume. The ICESat-2 data covered 50% of the storage capacity in the Sobradinho and 89% in Poço da Cruz. The in-situ and ICESat-2 data-based EAV curves showed volumes with Pearson’s correlations equal to 0.99 for both reservoirs, and normalized root mean square errors of 2.62% and 4.13% for Sobradinho and Poço da Cruz, respectively. The results revealed that this strategy can be economic and viable, as it uses available free data with the potential to generate high-resolution bathymetry in reservoirs with different geometric characteristics.

Atlantic Coastal Sea Level Variability and Synoptic-Scale Meteorological Forcing

Abstract Anomalous sea levels along the mid-Atlantic and South Atlantic coasts of the United States are often linked to atmosphere–ocean dynamics, remote- and local-scale forcing, and other factors linked to cyclone passage, winds, waves, and storm surge. Herein, we examine sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data within the context of synoptic-scale weather pattern forcing. Altimetry data, derived from sea level anomaly (SLA) data between 1993 and 2019, were compared with self-organizing map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their interrelationships with high-water-level conditions at tide gauges. Regional elevated sea level patterns and variability were strongly associated with synergistic patterns of atmospheric circulation and wind. Recurring atmospheric patterns associated with high-tide flooding events and flood risk were identified, as were specific regional oceanographic variability patterns of SLA response. The incorporation of combined metrics of wind and circulation patterns further isolate atmospheric drivers of high-tide flood events and may have particular significance for predicting future flood events over multiple spatial and temporal scales. Significance Statement Mean sea level and minor to moderate coastal flood events, also called blue-sky or high-tide floods, are increasing along many U.S. coastlines. While the drivers of such events are numerous, here we identified key contributing weather patterns and environmental factors linked to increased risk of regional and local high-water conditions along the Atlantic coast. Our results indicate that the predictability of elevated sea levels and high-tide floods is highly dependent upon atmospheric drivers including wind and circulation patterns and, if applied in a tested modeling framework, may prove useful for predicting future floods at various time scales.

A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting

Abstract The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.

Compositional Maps of the Lunar Polar Regions Derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter Data

Abstract Due to the challenging illumination conditions of the lunar polar regions, mineralogic maps have generally been constrained to within 0°–70°N/S. Here we generate a gridded reflectance data cube from the Kaguya Spectral Profiler measurements for each polar region and calibrate it to absolute reflectance using data from the Lunar Orbiter Laser Altimeter. We use this data set to derive the first quantitative mineral maps of iron oxide (FeO), the optical maturity parameter (OMAT), and nanophase iron poleward of 50°N/S at a spatial resolution of 1 km pixel–1. We evaluate potential latitudinal trends in space weathering and optical maturity and characterize the polar geology, with an emphasis on the Artemis region (84°–90°S). The maps of FeO are in excellent agreement with the abundances measured by the Lunar Prospector and provide an increased level of detail, such as the excavation of high- and low-FeO material by De Forest crater. The OMAT maps offer a fantastic view of both polar regions. They highlight small fresh craters, walls, and central peaks, as well as rays extending through multiple degrees of latitude, such as those from Tycho and De Forest, which extend into the Artemis region. Both polar regions are characterized by the ubiquitous presence of noritic anorthosites and anorthositic norite. Low-calcium pyroxene is largely the dominant mafic mineral present. The Artemis region has relatively homogeneous FeO and plagioclase content at the observed spatial resolution. The lowest FeO values are found near Shackleton, between Shoemaker and Faustini, and on the central peak of Amundsen crater.

Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.

Covariance kernels investigation from diffusive wave equations for data assimilation in hydrology

In data assimilation (DA), the estimation of the background error covariance operator is a classical and still open topic. However, this operator is often modeled using empirical information. In order to exploit at best the potential of the knowledge of the physics, the present study proposes a method to derive covariance operators from the underlying equations. In addition, Green’s kernels can be used to model covariance operators and are naturally linked to them. Therefore, Green’s kernels of equations representing physics can provide physically-derived estimates of the background error covariance operator, and also physically-consistent parameters. In this context, the present covariance operators are used in a variational DA (VDA) process of altimetric data to infer bathymetry in the Saint-Venant equations. In order to investigate these new physically-derived covariance operators, the associated VDA results are compared to the VDA results using classical operators with physically-consistent and arbitrary parameters. The physically-derived operators and physically-consistent exponential operator provide better accuracy and faster convergence than empirical operators, especially during the first iterations of the VDA optimization process.

An Improved Algorithm for the Retrieval of the Antarctic Sea Ice Freeboard and Thickness from ICESat-2 Altimeter Data

ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest point method to derive the sea surface height for the retrieval of Antarctic sea ice freeboard and thickness values from ICESat-2 ATL-07 data. We first collocated ICESat-2 tracks to corresponding Sentinel-1 SAR images and calculated lead (seawater) proportions along each track to estimate the sea surface height in the Antarctic Ocean. Then, the Antarctic sea ice freeboard and thickness were estimated based on a local sea surface height reference and a static equilibrium equation. Finally, we assessed the accuracy of our improved algorithm and ICESat-2 data product in the retrieval of the Antarctic sea ice thickness by comparing the calculated values to ship-based observational sea ice thickness values acquired during the 35th Chinese Antarctic Research Expedition (CHINARE-35). The results indicate that the Antarctic sea ice freeboard estimated with the improved lowest point method was slightly larger than that estimated with the ICESat-2 data product algorithm. The root mean squared error (RMSE) of the improved lowest point method was 35 cm with the CHINARE-35 measured sea ice thickness, which was smaller than that determined with the ICESat-2 data product algorithm (65 cm). Our improved algorithm could provide more accurate data on the Antarctic sea ice freeboard and thickness, thus supporting Antarctic sea ice monitoring and the evaluation of its change under global effects.

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.

Remote sensing reconstruction of long-term water level and storage variations of a poorly-gauged river in the Tibetan Plateau

Study regionThe upper Brahmaputra River, China. Study focusThe long-term water level and storage variations of the upper Brahmaputra River (UBR) are not yet clear due to the low accessibility of gauged data in high-altitude and harsh environments. The main objective of this study is to facilitate investigations of long-term river water level and storage variations over the UBR. Here we developed a hypsometric method to reconstruct temporally resolved long-term river water level observations based on remote sensing-derived accumulative water inundation area information and short-term altimetric water level data at 24 virtual stations (VSs) of the UBR. We further analyzed the intra-annual and inter-annual variations of the water level (storage) changes of the studied river reaches. New hydrological insightsThe results reveal that the reconstructed water levels of the UBR are quite consistent with the water levels from the Hydroweb with an average R2 of 0.79 and the reconstructed water levels are generally consistent with the variation of the gauged runoff. The ascending amplitudes of the annual water level cycle of VSs were observed from upstream to downstream of the UBR, varying from 0.58 m to 6.92 m. Additionally, we find that the seasonal change in river water storage approximately accounted for one-tenth of the net mass seasonal amplitude of the UBR basin, as revealed from the Gravity Recovery and Climate Experiment (GRACE) data.

A revisit of global wind-sea and swell climate and variability using multiplatform altimeters

A well-performed (root mean square error ≈ 0.35 m) look-up table model was established to separate the wind-sea and swell significant wave height (Hs) using 10-m sea surface wind speed (U10) and Hs data from altimeters. Based on this model, global wind-sea and swell climate and variability were investigated using 27-year jointly calibrated altimeter data. The climatology of the swell and wind-sea Hs (HWS and HSW) are in good agreement with those of the total Hs and U10, respectively. This is because of the dominance of swells in the World Ocean with respect to both occurrence frequency and energy proportion and the close coupling between wind and wind-seas. The interannual variability of HWS and HSW are closely related to some climate indices, but the responses of wind-seas and swells are different for the same atmospheric oscillation because of the large propagation distances of swells. For long-term variability, significant positive/negative long-term trends of HWS/HSW were found almost all over the World Ocean. Such opposite trends can be explained by the positive/negative trends of U10/Hs: a shorter fetch or duration of wind leads to a lower Hs, but a higher U10 leads to higher wind-sea probability and energy weight. However, large uncertainty still exists in these trends and further exploration is needed.

Cartografía del abandono de cultivos de cítricos mediante el uso de datos altimétricos: LiDAR y fotogrametría SfM

The Comunitat Valenciana region (Spain) is the largest citrus producer in Europe. However, it has suffered an accelerated land abandonment in recent decades. Agricultural land abandonment is a global phenomenon with environmental and socio-economic implications. The small size of the agricultural parcels, the highly fragmented landscape and the low spectral separability between productive and abandoned parcels make it difficult to detect abandoned crops using moderate resolution images. In this work, an approach is applied to monitor citrus crops using altimetric data. The study uses two sources of altimetry data: LiDAR from the National Plan for Aerial Orthophotography (PNOA) and altimetric data obtained through an unmanned aerial system applying photogrammetric processes (Structure from Motion). The results showed an overall accuracy of 67,9% for the LiDAR data and 83,6% for the photogrammetric data. The high density of points in the photogrammetric data allowed to extract texture features from the Gray Level Co-Occurrence Matrix derived from the Canopy Height Model. The results indicate the potential of altimetry information for monitoring abandoned citrus fields, especially high-density point clouds. Future research should explore the fusion of spectral, textural and altimetric data for the study of abandoned citrus crops.

Variation of Internal Tides on the Continental Slope of the Southeastern East China Sea

The semidiurnal internal tides (ITs) on the continental slope of the southeastern East China Sea (ECS) exhibited abrupt enhancement in November of 2017. This enhancement resulted from the intensification of the coherent semidiurnal ITs. Coherent and incoherent semidiurnal ITs had a comparative energy contribution in October; however, coherent semidiurnal ITs dominated with a variance contribution of 90% in November. The variance contribution of vertical modes of the semidiurnal ITs varied between October and November, and the mode with most variance contribution changed from the second mode to the first mode. Altimeter data and the observed background currents indicated that the Kuroshio mainstream meandered and abruptly intruded into the ECS in November. The upper layer background currents were significantly related to the kinetic energy of the semidiurnal ITs, and the correlation coefficient between them reached 0.81. The frequent occurrences of the Kuroshio intrusion have suggested that the ITs in the ECS are susceptible to the modulation of the Kuroshio current. Numerical modeling and predication of ITs should consider the meander of the Kuroshio mainstream.

Circulation in the region of the West India Coastal Current in March 1994 hydrographic and altimeter data

Circulation in the region of the West India Coastal Current in March 1994 hydrographic and altimeter data

Wave Height Estimation and Validation Based on the UFS Mode Data of Gaofen-3 in South China Sea

The ultrafinestrip (UFS) of the Gaofen-3 (GF-3) satellite provides extensive ocean wave details due to the high spatial resolution of 3 m. In this article, a new empirical model is developed to estimate wave height from GF-3 UFS mode data in South China Sea (SCS). Traditional methods either have more input parameters or complex forms or complex transformations, and most of them depend on the visible wave pattern. The model has the advantages of fewer input parameters and unnecessary visual wave patterns. The wave height estimation model is developed based on the collated data pairs between GF-3 UFS data in horizontal-horizontal polarization and the ERA5 reanalysis dataset from 2018 to 2021. The datasets are randomly divided into two groups. One group (about 80%) is used to develop the empirical relation; the other group (about 20%) and some altimeter data are used for significant wave height (SWH) verification. The comparison of the model data with the remaining ERA5 (ECMWF Reanalysis v5) data and the altimeter shows that the root-mean-square error is 0.41 and 0.47 m, the scatter index is 29.24% and 29.79%, and the correlation coefficient is 0.90 and 0.82, respectively. These statistical indices suggest that the developed model is suitable for SWH retrievals. So, the study results indicate that the GF-3 UFS mode data provide valuable information about wave conditions in the SCS.

A New DOV Gridding Method and Its Application in Marine Gravity Recovery

In the grid processing of the deflection of the vertical (DOV) for marine gravity recovery, the traditional least squares collocation (LSC) gridding method only considers the accuracy difference between different satellite radar altimeters, but ignores the accuracy difference between different observation points of the same radar altimeter. In order to solve this problem, a new LSC gridding method weighted by the accuracy of observation points is proposed in this paper. The new method sets the quality parameters of each observed value according to the dispersion of the data and gives corresponding weights to each residual along-track DOV for LSC gridding based on the error propagation law. Then, based on the 1’ × 1’ gridded DOV obtained by the traditional method and the new method, the inverse Vening Meinesz formula with one-dimensional FFT and the remove and restore method were used to recover the 1’ × 1’ gridded gravity field in the South China Sea. The validation of ship-measured gravity data shows that the new DOV gridding method can effectively improve the accuracy of the marine gravity field derived from HY-2A and CryoSat-2 data. The new method is also applicable to other similar satellite altimeter data, and will bring significant improvements to the recovery of higher-precision ocean gravity fields.