Radar Altimeter Research Articles

Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry.

Mesoscale eddies, which are fast-moving rotating water bodies in the ocean with horizontal scales ranging from 10 km to 100 km and above, are considered to be the weather of the oceans. They are of interest to marine biologists, oceanographers, and geodesists for their impact on water mass, heat, and nutrient transport. Typically, gridded sea level anomaly maps processed from multiple radar altimetry missions are used to detect eddies. However, multi-mission sea level anomaly maps obtained by the operational processors have a lower effective spatiotemporal resolution than their grid spacing and temporal resolution, leading to inaccurate eddy detection. In this study, we investigate the use of higher-resolution along-track sea level anomaly data to infer daily two-dimensional segmentation maps of cyclonic, anticyclonic, or non-eddy areas with greater accuracy than using processed sea level anomaly grid map products. To tackle this challenge, we propose a deep neural network that uses spatiotemporal contextual information within the modality of along-track data. This network is capable of producing a two-dimensional segmentation map from data with varying sparsity. We have developed an architecture called Teddy, which uses a Transformer module to encode and process spatiotemporal information, and a sparsity invariant CNN to infer a two-dimensional segmentation map of classified eddies from the ground tracks of varying sparsity on the considered region. Our results show that Teddy creates two-dimensional maps of classified eddies from along-track data with higher accuracy and timeliness when compared to commonly used methods that work with less accurate preprocessed sea level anomaly grid maps. We train and test our method with a carefully curated and independent dataset, which can be made available upon request.

Expected Precision of Gravity Gradient Recovered from Ka-Band Radar Interferometer Observations and Impact of Instrument Errors

Full tensor of gravity gradients contains extremely large amounts of information, which is one of the most important sources for research on recovery seafloor topography and underwater matching navigation. The calculation and accuracy of the full tensor of gravity gradients are worth studying. The Ka-band interferometric radar altimeter (KaRIn) of surface water and ocean topography (SWOT) mission enables high spatial resolution of sea surface height (SSH), which would be beneficial for the calculation of gravity gradients. However, there are no clear accuracy results for the gravity gradients (the gravity gradient tensor represents the second-order derivative of the gravity potential) recovered based on SWOT data. This study evaluated the possible precision of gravity gradients using the discretization method based on simulated SWOT wide-swath data and investigated the impact of instrument errors. The data are simulated based on the sea level anomaly data provided by the European Space Agency. The instrument errors are simulated based on the power spectrum data provided in the SWOT error budget document. Firstly, the full tensor of gravity gradients (SWOT_GGT) is calculated based on deflections of the vertical and gravity anomaly. The distinctions of instrument errors on the ascending and descending orbits are also taken into account in the calculation. The precision of the Tzz component is evaluated by the vertical gravity gradient model provided by the Scripps Institution of Oceanography. All components of SWOT_GGT are validated by the gravity gradients model, which is calculated by the open-source software GrafLab based on spherical harmonic. The Tzz component has the poorest precision among all the components. The reason for the worst accuracy of the Tzz component may be that it is derived by Txx and Tyy, Tzz would have a larger error than Txx and Tyy. The precision of all components is better than 6 E. Among the various errors, the effect of phase error and KaRIn error (random error caused by interferometric radar) on the results is greater than 2 E. The effect of the other four errors on the results is about 0.5 E. Utilizing multi-cycle data for the full tensor of gravity gradients recovery can suppress the effect of errors.

GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging

Abstract. Measuring the spatiotemporal dynamics of lake and reservoir water storage is fundamental for assessing the influence of climate variability and anthropogenic activities on water quantity and quality. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only a few hundred lakes worldwide and cannot estimate absolute (i.e. total volume) water storage. We increased the number of measured lakes by a factor of 300 by using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry, in addition to radar altimetry from the Topex/Poseidon; Jason-1, Jason-2 and Jason-3; and Sentinel-3 and Sentinel-6 instruments. Historical time series (1984–2020) of water storage could be derived for more than 170 000 lakes globally with a surface area of at least 1 km2, representing 99 % of the total volume of all water stored in lakes and reservoirs globally. Specifically, absolute lake volumes are estimated based on topographic characteristics and lake properties that can be observed by remote sensing. In addition to that, we also generated relative lake volume changes solely based on satellite-derived heights and extents if both were available. Within this dataset, we investigated how many lakes can be measured in near real time (2020–current) in basins worldwide. We developed an automated workflow for near-real-time global lake monitoring of more than 27 000 lakes. The GloLakes historical and near-real-time lake storage dynamics data from 1984 to current are publicly available through https://doi.org/10.25914/K8ZF-6G46 (Hou et al., 2022c) and a web-based data explorer (http://www.globalwater.online, last access: 12 December 2023).

A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency

Radio-frequency interference is a growing concern as wireless technology advances, with potentially life-threatening consequences like interference between radar altimeters and 5 G cellular networks. Mobile transceivers mix signals with varying ratios over time, posing challenges for conventional digital signal processing (DSP) due to its high latency. These challenges will worsen as future wireless technologies adopt higher carrier frequencies and data rates. However, conventional DSPs, already on the brink of their clock frequency limit, are expected to offer only marginal speed advancements. This paper introduces a photonic processor to address dynamic interference through blind source separation (BSS). Our system-on-chip processor employs a fully integrated photonic signal pathway in the analogue domain, enabling rapid demixing of received mixtures and recovering the signal-of-interest in under 15 picoseconds. This reduction in latency surpasses electronic counterparts by more than three orders of magnitude. To complement the photonic processor, electronic peripherals based on field-programmable gate array (FPGA) assess the effectiveness of demixing and continuously update demixing weights at a rate of up to 305 Hz. This compact setup features precise dithering weight control, impedance-controlled circuit board and optical fibre packaging, suitable for handheld and mobile scenarios. We experimentally demonstrate the processor’s ability to suppress transmission errors and maintain signal-to-noise ratios in two scenarios, radar altimeters and mobile communications. This work pioneers the real-time adaptability of integrated silicon photonics, enabling online learning and weight adjustments, and showcasing practical operational applications for photonic processing.

Shuffled frog Leaping algorithm based circular patterns loaded ring shaped fractal antenna for multistandard wireless applications

This paper describes the design and experimental behavior of a 2nd iterated modified Square Ring Shaped Fractal Antenna (SRSFA). The radiator geometry consists of three symmetrical square-shaped rings loaded with circular patterns at the corners. The designed fractal patch is placed on a slot loaded ground structure. The electrical dimensions possessed by the realized structure are 0.15λ0 x 0.15λ0 x 0.080 λ0, and the whole antenna is implemented using FR4 substrate. In this design, transmission line width ‘WF’ and position ‘FP’ are optimized using Shuffled Frog Leaping Algorithm (SFLA) and Artificial Bee Colony (ABC) strategies. It has been examined that the optimized values of ‘WF’ and ‘FP’ using SFLA and ABC are 2.6 and 12.4 mm, and 3.2 and 15.9 mm, respectively. Results reveal that SFLA offers better solution quality and convergence speed than ABC. Further, experimental analysis has been demonstrated to effectively illustrate the recommended fractal approach. The equivalent circuit of the designed structure is implemented successfully. The conducted measurements show that for VSWR ≤ 2, the fabricated structure produces six resonance points with adorable S11 values. The resonances appear at 1.53 GHz (1.35–1.67 GHz), 4.30 GHz (4.20–4.40 GHz), 7.58 GHz (7.43–7.70 GHz), 11.35 GHz (10.37–11.58 GHz), 14.03 GHz (13.85–14.35 GHz), and 15.93 GHz (15.43–16.25 GHz). The examined −10 dB impedance bandwidth at the respective resonance points is 20.9 %, 4.6 %, 3.5 %, 11.3 %, 2.9 %, and 5.23 %. Healthy concordance is noticed among the experimental measurements and electromagnetic simulations. The demonstrated radiation patterns are arbitrary bidirectional/omnidirectional at the respective resonances with good stability. Additionally, the offered gain is within the range of 4.91–7.00 dB. Hence, by utilizing the above-mentioned approaches, miniaturization of 65 % has been achieved. Practical outcomes suggest that the introduced prototype exhibiting multiband behavior, can be effectively applied in Radio location, Radio astronomy, Long-range tracking, Weather location Broadcasting, Search for extra-terrestrial intelligence, Radar altimeters, High resolution mapping, Aeronavigation, and Miscellaneous Radars.

Sea level rise estimation and projection from long-term multi-mission satellite altimetry and tidal data in the Southeast Asia region

ABSTRACT The Southeast Asian (SEA) region represents a complex coastal geographical location. Most countries in SEA are encircled by the ocean, with lowlands coastal areas. The advantages of long-term satellite altimetry and tide gauge measurements have enabled climate-related research, particularly sea level rise, to be examined extensively in spatial and temporal scales. In this study, sea level anomaly (SLA) derived from 26 years of multi-mission satellite altimeter data plus approximately 28 years of tidal data have been utilized. Then, the projection for every 10 years starting from 2030 until 2100 has been analysed using machine learning regression. The projections are then evaluated based on the global model of the Intergovernmental Panel on Climate Change (IPCC). The findings presented that sea level rise trends around SEA with overall means of 4.70 ± 0.37 mm yr−1 for the satellite altimeter and 3.41 ± 0.24 mm yr−1 for the coastal tide gauge. Sea level projection from coastal tide gauge and satellite altimeter are expected to rise up to 34.87 and 33.71 cm, respectively, in 2100. These outcomes prove that the altimetry data from Radar Altimeter Database System (RADS) and the redundant tidal data are capable in measuring the sea level in SEA. These results are also expected to be valuable for multidisciplinary environmental studies in SEA, such as coastal flooding, coastal erosion, and other effect of global warming. Thus, it would make a meaningful contribution towards Sustainable Development Goal (SDG) 13, climate action, by providing the sea level rate and prediction information.

The ESA Permanent Facility for Altimetry Calibration in Crete: Advanced Services and the Latest Cal/Val Results

Two microwave transponders have been operating in west Crete and Gavdos to calibrate international satellite radar altimeters at the Ku-band. One has been continuously operating for about 8 years at the CDN1 Cal/Val site in the mountains of Crete, and the other at the GVD1 Cal/Val site on Gavdos since 11 October 2021. This ground infrastructure is also supported at present by four sea-surface Cal/Val sites operating, some of them for over 20 years, while two additional such Cal/Val sites are under construction. This ground infrastructure is part of the European Space Agency Permanent Facility for Altimetry Calibration (PFAC), and as of 2015, it has been producing continuously a time series of range biases for Sentinel-3A, Sentinel-3B, Sentinel-6 MF, Jason-2, Jason-3, and CryoSat-2. This work presents a thorough examination of the transponder Cal/Val responses to understand and determine absolute biases for all satellite altimeters overflying this ground infrastructure. The latest calibration results for the Jason-3, Copernicus Sentinel-3A and -3B, Sentinel-6 MF, and CryoSat-2 radar altimeters are described based on four sea-surface and two transponder Cal/Val sites of the PFAC in west Crete, Greece. Absolute biases for Jason-3, Sentinel-6 MF, Sentinel-3A, Sentinel-3B, and CryoSat-2 are close to a few mm, determined using various techniques, infrastructure, and settings.

Performance of ERA5 wind speed and significant wave height within Extratropical cyclones using collocated satellite radar altimeter measurements

ABSTRACT Similar in strength to hurricanes, Extratropical Cyclones (ECs) are responsible for innavigable sea states, coastal inundation and erosion, and subsequent destruction to coastal infrastructure. Across modern operational wave models, there exists a known systematic underestimation of wave heights during these extreme events. Using a global database of EC storm tracks and 36 years of satellite altimeter data, we examine EC structure and assess model performance through storm centered composite analyses of significant wave height ( H s ) and U10 wind speed ( | U 10 | ). Through the collocation of satellite altimeter observations with a state-of-the-art reanalysis product (ERA5), we investigate model performance with respect to | U 10 | and H s within ECs of varying intensities. By rotating our data reference frames, we align all storm directions to account for asymmetry in EC wind fields and subsequent increased wave growth This rotation results in the organization of the strongest wind speed and H s , as well as trends in the ERA5 performance. A characteristic EC radii is calculated and used to normalize data coordinates in the storm centered reference frame, which highlights the organization of EC structures. Performance metrics are then compared within different EC quadrants to explore the relationship between wind forcing accuracy and underestimation of H s .

Quantification of surface water extent and volume in the Inner Niger Delta (IND) over 2000–2022 using multispectral imagery and radar altimetry

Spatio-temporal dynamics of surface water reservoirs at regional and global scales remain poorly understood. Using satellite remote sensing provides a unique opportunity to address this problem. This study aims to (1) quantify the extent and volume of surface water and (2) compare our approach with other datasets. We utilized MODIS-based spectral indices to monitor temporal variations in inundation extent. By interpolating water levels across the surface water extent, we generated water level maps and quantified surface water volume from 2000 to 2022. Evaluation against ICESat-2 data involved 64 comparisons, with approximately 58% showing an R 2 value greater than or equal to 0.5, and 38% were higher than or equal to 0.7. Compared to the flooding model, our method aligns more closely with ICESat-2 data, contrary to the flooding model which tends to overestimate water levels and, consequently, water storage.

Radio altimeter with J-correlation signal processing

Radio altimeter with J-correlation signal processing

Ultrathin and conformal frequency selective surfaces bandpass filter to eliminate the 5G bands on radio altimeters

AbstractThis work proposes a frequency‐selective surface‐based radome structure to selectively allow the radar altimeter operating band and also minimize the interference from Sub‐6GHz 5 G frequency bands. A Jerusalem cross and combinational loop (JCL) based frequency selective surfaces (FSS) unit cell of overall dimension 0.37λ was synthesized using a thin flexible substrate for use in fuselage of airborne systems. The proposed FSS unit cell exhibits transmission zero at 3.61 and 6.025 GHz and highly selective pass band at 4.3 GHz. The effect of bends and angle of incidence on the frequency response of the synthesized FSS was studied in transverse electric and transverse magnetic modes. A lumped element‐based equivalent circuit model for the proposed design was also presented. Finally, the fabricated prototype was experimentally validated in an Anechoic chamber setup and the suitability of the proposed FSS layers for aviation radar systems was confirmed.

Coexistence of 5G Communication Systems With Radar Altimeters

Coexistence of 5G Communication Systems With Radar Altimeters

River Water Level and Water Surface Slope Measurement From Spaceborne Radar and LiDAR Altimetry: Evaluation and Implications for Hydrological Studies in the Ganga River

River Water Level and Water Surface Slope Measurement From Spaceborne Radar and LiDAR Altimetry: Evaluation and Implications for Hydrological Studies in the Ganga River

Analysis of Attitude Errors Effect on the Measurement of Interferometric Radar Altimeter Based on the Imaging Principle

Analysis of Attitude Errors Effect on the Measurement of Interferometric Radar Altimeter Based on the Imaging Principle

Temporal and Spatial Variations in the Radar Altimeter Signal Penetration Error in Greenland

Temporal and Spatial Variations in the Radar Altimeter Signal Penetration Error in Greenland

A Method for Along-Track Interferometric Phase Elimination of Large Baseline Imaging Radar Altimeter

A Method for Along-Track Interferometric Phase Elimination of Large Baseline Imaging Radar Altimeter

A modified Goldstein filter for interferogram denoising of interferometric imaging radar altimeter based on multiple quality-guided graphs.

Aiming at the characteristics that the signal noise ratio (SNR) gradually decreases from the near to far range of the swath, an adaptive phase filtering algorithm based on Goldstein filtering and combined with multiple quality-guided graphs was proposed. Firstly, the components used to determine the filtering parameters were obtained through residue density, pseudo-coherence coefficient and pseudo-SNR, the three quality-guided graphs. Then, the filter parameters were calculated by weighting the three components. Finally, the size of filtering window was determined according to the account of residues, and the interferometric phase noise was removed in frequency domain. Simulated data, TSX/TDX data and airborne interferometric imaging radar altimeter data were used to verify the performance of the new algorithm. Compared with the results of Goldstein filtering and its improved algorithms, the results showed that the proposed algorithm can effectively filter out phase noise while maintaining the edge characteristics of interferometric fringe. The section of filtering result can well match with the section of simulated pure interfeometric phase. Moreover, the algorithm proposed in this paper can effectively remove the noise in the interferogram of TSX/TDX sea ice data, and the residues' filtering rate was above 86%, which can effectively remove the phase residues of the sea ice surface while maintaining the characteristics of the sea ice edge. Experimental results showed that the new algorithm provides an effective phase noise filtering method for imaging radar altimeter data processing.

Performance of Tiangong-2 Interferometric Imaging Radar Altimeter on Marine Gravity Recovery Over the South China Sea

Performance of Tiangong-2 Interferometric Imaging Radar Altimeter on Marine Gravity Recovery Over the South China Sea

A Fully Focused SAR Omega-K Closed-Form Algorithm for the Sentinel-6 Radar Altimeter: Methodology and Applications

A Fully Focused SAR Omega-K Closed-Form Algorithm for the Sentinel-6 Radar Altimeter: Methodology and Applications

Novel Machine Learning-Based Identification and Mitigation of 5G Interference for Radar Altimeters

Novel Machine Learning-Based Identification and Mitigation of 5G Interference for Radar Altimeters