Synthetic Aperture Radar Altimetry Research Articles

Introducing the Azimuth Cutoff as an Independent Measure for Characterizing Sea-State Dynamics in SAR Altimetry

This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can be detected by the satellite under certain wind and wave conditions. The magnitude of this parameter is closely related to the wave orbital velocity variance, a key parameter for characterizing wind-wave systems. We exploit wave modulations exhibited in the tail of fully-focused SAR waveforms and extract the azimuth cutoff from the radar signal through the analysis of its along-track autocorrelation function. We showcase the capability of Sentinel-6A in deriving these two parameters based on analyses in the spatial and wavenumber domains, accompanied by a discussion of the limitations. We use Level-1A high-resolution Sentinel-6A data from one repeat cycle (10 days) globally to verify our findings against wave modeled data. In the spatial domain analysis, the estimation of azimuth cutoff involves fitting a Gaussian function to the along-track autocorrelation function. Results reveal pronounced dependencies on wind speed and significant wave height, factors primarily determining the magnitude of the velocity variance. In extreme sea states, the parameters are underestimated by the altimeter, while in relatively calm sea states and in the presence of swells, a substantial overestimation trend is observed. We introduce an alternative approach to extract the azimuth cutoff by identifying the fall-off wavenumber in the wavenumber domain. Results indicate effective mitigation of swell-induced errors, with some additional sensitivity to extreme sea states compared to the spatial domain approach.

Estimating and Assessing Monthly Water Level Changes of Reservoirs and Lakes in Jiangsu Province Using Sentinel-3 Radar Altimetry Data

Generating accurate monthly estimations of water level fluctuations in reservoirs and lakes is crucial for supporting effective water resource management and protection. The dual-satellite configuration of Sentinel-3 makes it possible to monitor water level changes with great coverage and short time intervals. However, the potential of Sentinel-3’s Synthetic Aperture Radar Altimetry (SRAL) data to enable operational monitoring of water levels across Jiangsu Province on a monthly basis has not yet been fully explored. This study demonstrated and validated the use of Sentinel-3’s SRAL to generate accurate monthly water level estimations needed to inform water management strategies. The monthly water levels of lakes and reservoirs from 2017 to 2021 were produced using Sentinel-3 level-2 land products. Results showed that, compared with in situ data across eight studied lakes, all lakes presented R (Pearson correlation coefficient) values greater than 0.5 and Root Mean Square Error (RMSE) values less than 1 m. Notably, water level estimates for Tai Lake, Gaoyou Lake, and Luoma Lake were particularly accurate, with R above 0.9 and RMSE below 0.5 m. Furthermore, the monthly water level estimates derived from the Sentinel-3 data showed consistent seasonal trends over the multi-year study period. The annual water level of all lakes did not change significantly, except for Shijiu Lake, of which the difference between the highest and lowest water level was up to about 5 m. Our findings confirmed the water level observation ability of Sentinel-3. The accuracy of water level monitoring could be influenced by internal water level differences, terrain features, as well as the area and shape of the lake. Larger lakes with more altimetry sampling points tended to yield higher accuracy estimates of water level fluctuations. These results demonstrate that the frequent, wide-area coverage offered by this satellite platform provides valuable hydrological information, especially across remote regions lacking in situ data. Sentinel-3 has immense potential to support improved water security in data-scarce regions.

Signal Processing and Waveform Re-Tracking for SAR Altimeters on High Mobility Platforms with Vertical Movement and Antenna Mis-Pointing.

Synthetic aperture radar (SAR) altimeters can achieve higher spatial resolution and signal-to-noise ratio (SNR) than conventional altimeters by Doppler beam sharpening or focused SAR imaging methods. To improve the estimation accuracy of waveform re-tracking, several average echo power models for SAR altimetry have been proposed in previous works. However, these models were mainly proposed for satellite altimeters and are not applicable to high-mobility platforms such as aircraft, unmanned aerial vehicles (UAVs), and missiles, which may have a large antenna mis-pointing angle and significant vertical movement. In this paper, we propose a novel semi-analytical waveform model and signal processing method for SAR altimeters with vertical movement and large antenna mis-pointing angles. A new semi-analytical expression that can be numerically computed for the flat pulse response (FSIR) is proposed. The 2D delay-Doppler map is then obtained by numerical computation of the convolution between the proposed analytical function, the probability density function, and the time/frequency point target response of the radar. A novel delay compensation method based on sinc interpolation for SAR altimeters with vertical movement is proposed to obtain the multilook echo, which can optimally handle non-integer delays and maintain signal frequency characteristics. In addition, a height estimation method based on least squares (LS) estimation is proposed. The LS estimator does not have an analytical solution, and requires iterative solving through gradient descent. We evaluate the performance of the proposed estimation strategy using simulated data for typical airborne scenarios. When the mis-pointing angles are within 10 degrees, the normalized quadratic error (NQE) of the proposed model is less than 10-10 and the RMSE of τ obtained by the re-tracking method fitted by the proposed model is less than 0.2 m, which indicates the high applicability of the model and accuracy of the re-tracking method.

Machine learning based classification of lake ice and open water from Sentinel-3 SAR altimetry waveforms

The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large lakes across the Northern Hemisphere and three ice seasons (2018–2021) were manually labelled using complementary satellite data (Sentinel-1 imaging Synthetic Aperture Radar (SAR), Sentinel-2 Multispectral Instrument (MSI) Level 1C, and MODIS Aqua/Terra data) for the training and testing of the ML algorithms in discriminating between open water, young (thin) ice, growing ice and melting ice. The four ML algorithms tested include Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbor (KNN) and Support Vector Machine (SVM). To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTPP), early tail to peak power (ETPP) and the maximum value of the echo power (Max). Accuracies >95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Among all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice types and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake (out-of-sample testing) revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach presented herein has potential for application on smaller lakes too since data in SAR mode (∼300 m along-track resolution) are used.

Towards the Mitigation of Discrepancies in Sea Surface Parameters Estimated from Low- and High-Resolution Satellite Altimetry

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σv and a new parameter ux, linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe ux as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter ux—between estimated parameters.

A noise autocovariance model for SAR altimeter measurements with implications for optimal sampling

Earlier work has empirically demonstrated some advantages of an increased posting rate of Synthetic Aperture Radar (SAR) altimeters beyond the expected ground resolution of about 320 m in Delay-Doppler (unfocused SAR, UFSAR) processing, corresponding to ∼20 Hz sampling. Higher posting rates of 40–80 Hz were shown to prevent spectral aliasing of the signal, enable to measure swell wave related signal distortions and may lead to a reduced root mean square error of 1 Hz estimates of Sea Surface Heights (SSH), radar cross section (sigma0) and Significant Wave Heights (SWH) from current SAR altimeters. These improvements were explained by the narrow noise autocovariance function of the waveform signal’s power speckle noise in along-track direction on one hand, and frequency doubling by power detection (squaring of the signal) on the other. It has not been explained, however, why the power speckle noise decorrelates faster than anticipated by the predicted Doppler resolution, and whether this decorrelation depends on the altimeter and processing configuration. Also, it has not been shown explicitly that the estimates of SSH, SWH and sigma0 decorrelate in the same way. Describing the noise autocovariance function – or equivalently the noise power spectral density via the Wiener-Kintchin theorem – is necessary on two counts: Knowing the noise autocovariance allows to apply optimal filtering strategies that maximize precision on one hand, while the noise power spectral density predicts the frequencies contained in the noise (and signals), which in turn determines the required sampling frequency according to the Nyquist theorem. Using a newly derived analytic noise autocovariance model for UFSAR-processed altimeter data, we show that the swift signal decorrelation is mainly due to the observation geometry. Furthermore, our results demonstrate that the noise autocovariance functions of power speckle, SSH, SWH and sigma0 estimates in along-track direction are different and depend on the sea state. On top of that, the noise autocovariance functions are strongly dependent on the number of Doppler beams used for multilooking, the used retracker, and the processing choices such as antenna gain pattern compensation and windowing within the UFSAR processing (Level-1b). We validated our noise autocovariance model with segments of 42 Sentinel-3B overpasses. Our findings are in accordance to all earlier work, but indicate that the reported precision improvements with respect to 20 Hz may have been too optimistic and that the SSH, SWH and sigma0 generally decorrelate slower than the power speckle noise. We found that the required posting rate is always higher or equal to 40 Hz. Our results will potentially enable improved spectral analysis and optimal filtering of any UFSAR altimetry data. More importantly, our results can be used to trade off different aspects for determining an optimal posting rate in UFSAR altimeter processing in different sea states and with changing processing parameters, which is necessary in view of strict precision requirements of existing and future SAR altimetry missions.

Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery

Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.

Using a Tandem Flight Configuration between Sentinel-6 and Jason-3 to Compare SAR and Conventional Altimeters in Sea Surface Signatures of Internal Solitary Waves

Satellite altimetry has been providing a continuous record of ocean measurements with numerous applications across the entire range of ocean sciences. A reference orbit has been used since 1992 with TOPEX/Poseidon, which was repeated in the Jason missions, and in the newly launched Sentinel-6 Michael Freilich (in November 2020) to continually monitor the trends of sea level rise and other properties of the sea surface. These multidecadal missions have evolved alongside major technological advances, whose measurements are unified into a single data record owing to continuous intercalibration and validation efforts. However, the new Sentinel-6 provides synthetic aperture radar (SAR) processing, which improves the along-track resolution of conventional altimeters from a few kilometres (e.g., for Jason-3) to about 300 m. This means a major leap in sampling towards higher frequencies of the ocean spectrum, which inevitably means reconciling the assumption of a uniform Brown surface between the footprints of the larger kilometre-scale conventional altimetry and those of the finer-scale SAR altimetry. To explore this issue, this study uses the vantage point of the Sentinel-6/Jason-3 tandem phase to compare simultaneous sea surface signatures of large-scale Internal Solitary Waves (ISWs) between SAR and conventional altimetry. These waves can modulate the sea surface into arrayed sections of increased and decreased roughness with horizontal scales up to 10 km, which inflict sharp transitions between increased and decreased backscatter in the radar altimeters. It is found that Sentinel-6 can provide more detailed structures of ISWs in standard level-2 products, when compared with those from the conventional Jason-3 (similarly to previous results reported from the SAR altimeter from Sentinel-3). However, a new and striking feature is found when comparing the radar backscatter between Sentinel-6 and Jason-3, which are in opposite phases in the ISWs. These intriguing results are discussed in light of the intrinsically different acquisition geometries of SAR and conventional altimeters as well as possible implications thereof.

Conditional sea surface statistics and their impact on geophysical sea surface parameters retrieved from SAR altimetry signals

In this study we present an analytical formulation of synthetic-aperture radar (SAR) altimetry signals including narrow banded nonlinear wave fields and conditional statistics between wave elevation displacements, horizontal wave slopes and vertical wave particle velocities. Considering the wave elevation displacements coskewness with respect to horizontal slopes leads to an analytical formulation of the electromagnetic bias within a SAR-mode altimeter stack. This formulation can be either parametrized by the significant wave height (SWH) and mean wave steepness, or in terms of the variance of vertical wave velocities. The effect of conditional vertical wave particle velocity variances with respect to the observed horizontal wave slopes close to nadir incidence angles leads to an effective reduction of the azimuth blurring of SAR-mode stacks. We present here a formulation of this effect by examining JONSWAP ocean wave spectra. In most cases this effect reduces the azimuth blurring by 10% to 30%. Additionally we investigate the effect of a nonlinear wave elevation displacement probability density function (PDF) on estimated geophysical parameters. We were able to show that including an elevation displacement skewness of 0.13 improves significantly the SWH consistency between altimetry and ECMWF Reanalysis v5 ERA5 results.All of these effects are validated with respect to ERA5 model data in the North East Atlantic region and in situ data located in the German Bight and Baltic Sea.The developed model can be used in both SAR and conventional altimetry retrackers.

Coastal bathymetry inversion using SAR-based altimetric gravity data: A case study over the South Sandwich Island

The global bathymetry models are usually of low accuracy over the coastline of polar areas due to the harsh climatic environment and the complex topography. Satellite altimetric gravity data can be a supplement and plays a key role in bathymetry modeling over these regions. The Synthetic Aperture Radar (SAR) altimeters in the missions like CryoSat-2 and Sentinel-3A/3B can relieve waveform contamination that existed in conventional altimeters and provide data with improved accuracy and spatial resolution. In this study, we investigate the potential application of SAR altimetric gravity data in enhancing coastal bathymetry, where the effects on local bathymetry modeling introduced from SAR altimetry data are quantified and evaluated. Furthermore, we study the effects on bathymetry modeling by using different scale factor calculation approaches, where a partition-wise scheme is implemented. The numerical experiment over the South Sandwich Islands near Antarctica suggests that using SAR-based altimetric gravity data improves local coastal bathymetry modeling, compared with the model calculated without SAR altimetry data by a magnitude of 3.55 m within 10 km of offshore areas. Moreover, by using the partition-wise scheme for scale factor calculation, the quality of the coastal bathymetry model is improved by 7.34 m compared with the result derived from the traditional method. These results indicate the superiority of using SAR altimetry data in coastal bathymetry inversion.

Local Enhancement of Marine Gravity Field over the Spratly Islands by Combining Satellite SAR Altimeter-Derived Gravity Data

The marine gravity field recovery close to land/island is challenging owing to the scarcity of measured gravimetric observations and sorely contaminated satellite radar altimeter-derived data. The satellite missions that carried the synthetic aperture radar (SAR) altimeters supplied data with improved quality compared to that retrieved from the conventional radar altimeters. In this study, we combine the satellite altimeter-derived gravity data for marine gravity field augmentation over island areas; in particular, the feasibility for regional augmentation by incorporating the SAR altimeter-derived gravity data is investigated. The gravity field modeling results over the Spratly Islands demonstrate that the marine gravity field is augmented by the incorporation of newly published satellite altimeter-derived gravity data. By merging the gravity models computed with the Sentinel-3A/B SAR altimetry data, the quasi-geoid and mean dynamic topography are dramatically improved, by a magnitude larger than 4 cm around areas close to islands, in comparison with the results directly derived from a combined global geopotential model alone. Further comparison of regional solutions computed from heterogeneous gravity models shows that the ones modeled from the SAR-based gravity models have better performances, the errors of which are reduced by a magnitude of 2~4 cm over the regions close to islands, in comparison with the solutions modeled with the gravity models developed without SAR altimetry data. These results highlight the superiority of using the SAR-based gravity data in marine gravity field recovery, especially over the regions close to land/island.

Coastal Mean Dynamic Topography Recovery Based on Multivariate Objective Analysis by Combining Data from Synthetic Aperture Radar Altimeter

MDT recovery over coastal regions is challenging, as the mean sea surface (MSS) and geoid/quasi-geoid models are of low quality. The altimetry satellites equipped with the synthetic aperture radar (SAR) altimeters provide more accurate sea surface heights than traditional ones close to the coast. We investigate the role of using the SAR-based MSS in coastal MDT recovery, and the effects introduced by the SAR altimetry data are quantified and assessed. We model MDTs based on the multivariate objective analysis, where the MSS and the recently released satellite-only global geopotential model are combined. The numerical experiments over the coast of Japan and southeastern China show that the use of the SAR-based MSS improves the local MDT. The root mean square (RMS) of the misfits between MDT-modeled with SAR altimetry data and the ocean data is lower than that derived from MDT computed without SAR data—by a magnitude of 4–8 mm. Moreover, the geostrophic velocities derived from MDT modeled with the SAR altimetry data have better fits with buoy data than those derived from MDT modeled without SAR data. In total, our studies highlight the use of SAR altimetry data in coastal MDT recovery.

The Influence of Arctic Landfast Ice on Seasonal Modulation of the M2 Tide

AbstractSeasonal modulation of the M2 tide has been quantified for the entire Arctic Ocean and connected regional seas, using tidal harmonic analysis of water levels derived from Synthetic Aperture Radar altimetry. Results are compared to numerical simulations that model the effect of two limiting cases of seasonal landfast ice cover on the M2 tide. The largest seasonal modulation (up to 0.25 m) is observed along coastlines and in bays. Locally, the presence of landfast ice decreases amplitudes, but in some cases, the opposite effect was observed further afield. In most of the Arctic, winter months experience a later arrival of the tide, except for Hudson Bay where phase advance is observed. Most of the altimeter‐derived seasonal modulation could be explained by the modeled impact of landfast ice. However, particularly in the Hudson Bay system there is a discrepancy between model‐ and altimeter‐derived seasonal modulation. This suggests that other seasonal processes are important. Finally, results suggest that the consequences of variations in Arctic landfast ice are not restricted to the Arctic, but affect tidal water levels on a global scale.

Advances in NE-Atlantic coastal sea level change monitoring by Delay Doppler altimetry

Abstract A significant part of the World population lives in the coastal zone, which is affected by sea level rise and extreme events. Consistent and precise new measurements are needed to assess and predict these changes. New altimeter missions equipped with Synthetic Aperture Radar (SAR) mode provide more accurate sea level heights. In this work we analyse their impacts on the estimation of the coastal sea level variability in the last 10 km. First, by analysing various standard and improved SAR altimetric products it is found that SAR altimetry will reduce the minimum usable distance from five to three kilometres. The best performance is achieved with the SAR coastal retrackers SAMOSA+ and SAMOSA++. A comparable performance is obtained in low resolution Reduced SAR (RDSAR) mode with the Spatio-Temporal Altimeter sub-waveform Retracker (STAR). In both cases, sea level heights are recovered with 4 cm accuracy up to 3 km from the coast at the Helgoland station, and the accuracy does not decrease with the distance to coast. In estuaries and coastal zones with hight tidal regimes, the discrepancy between altimetry and in-situ observations remains large (40 cm standard deviation difference (stdd) with SAR-SAM+). CryoSat-2 and Sentinel-3A have similar accuracy, despite their different repeat cycle and sampling, which require a different post-processing. The monthly coastal variability from the SAR-SAM+ product agrees most favourably with the NEMO-WAM model, with stdd 3.9 and correlation 0.90 for Sentinel-3A. Instead, the maximum departure between altimeter data products is between SAR-SAM+ and RDSAR-TALES CryoSat-2 data (stdd 2.3 and correlation 0.96). An average trend of about 3 ± 1.3 mm/yr has been detected using low resolution altimetry at ninetheen in-situ stations along the German coast over time intervals longer than 15 years. The difference of the trends of altimetry and tide gauge co-located time-series (al-tg) agrees with the GNSS rates within 1.5 mm/yr at half of the co-located locations, with error of al-tg larger than the error of the GPS rate by a factor bigger than two. The larger departure between monthly tide gauge and LRM altimetric time-series as between tide gauge and SAR altimetric time-series confirms the higher accuracy of SAR data compared to LRM. Finally, the sea level trends of the merged LRM and SAR altimetry time-series are consistent with the LRM trends.

A RIP-based SAR retracker and its application in North East Atlantic with Sentinel-3

Just as CryoSat-2, Sentinel-3 embarks on board a radar altimeter (SRAL) with the novel Synthetic Aperture Radar (SAR) mode that enables higher resolution and more accurate altimeter-derived parameters in the coastal zone, thanks to the reduced along-track footprint. Exploiting the SAR data in the recent years, many researchers have already proven that the performance of SAR altimetry with specific coastal retrackers is superior to collocated Pseudo-Low Resolution Mode (PLRM) coastal altimetry algorithms but they also pointed out that residual errors due to land contamination are still present in the very proximity of the land (0–3 km).

Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers

Satellite radar altimetry observations of water surface elevation (WSE) have become an important data source to supplement river gauge records. Sentinel-3 is the first radar altimetry mission operating with a synthetic aperture radar (SAR) altimeter at global scale and with a new on-board tracking system (i.e. open-loop), which has great potential in terms of delivering reliable observations of inland water bodies for the next two decades (several future missions include an open-loop tracking mode). In this context, it is very important to investigate the data quality at an early stage. In this study, a comprehensive evaluation of Sentinel-3A (S3A) is conducted at 50 virtual stations (VS) located on a wide range of rivers in China.The evaluation of Level 1 data shows that, over mountain rivers, a good prior surface elevation estimate on-board is vital to deliver useful datasets using the S3A open-loop tracking system. The Open-Loop Tracking Command version 5 (OLTC V5) has significantly improved the placement of the range window, which was misplaced and resulted in lack of data over many mountain rivers prior to OLTC V5 (March 2019). However, application of S3A over mountain rivers still requires careful evaluation, especially before March 2019.Four retrackers are evaluated including a physical SAR Altimetry Mode Studies and Applications retracker (SAMOSA+), a traditional Offset Center Of Gravity (OCOG), a Primary Peak Center Of Gravity (PPCOG), and a modified Multiple Waveform Persistent Peak (MWaPP+) retracker. For 26 VSs in plain areas, retracked WSE data achieved a root mean square error (RMSE) ranging from 0.12 m to 0.9 m. The comparison of retracking methods reveals that SAMOSA+, OCOG, and PPCOG are unable to handle multi-peak waveforms. But the MWaPP+ can significantly improve the accuracy of the estimated WSE over large rivers, especially when the waveforms are contaminated.Moreover, our result shows no considerable difference between medium (ca. 300 m wide) and large (wider than 500 m) rivers. Instead, surrounding topography and homogeneity of surroundings are very important factors influencing the shape of a waveform. For rivers surrounded by lakes, man-made channels etc., special care must be taken when processing altimetry data. Dedicated retracking methods, such as MWaPP+, and sophisticated methods for outlier detection are needed to improve precision over such rivers, as demonstrated here for the Yangtze River.

Evaluation of SAR altimetry over the antarctic ice sheet from CryoSat-2 acquisitions

Since the 1990s and the launch of ERS-1, Earth’s polar regions have been near continuously monitored by satellite altimetry, improving substantially our knowledge of the ice sheet topography and its evolution. CryoSat-2 is the first satellite carrying on-board a new generation of radar altimeter able to operate in a Synthetic Aperture Radar (SAR) mode. In comparison with the Low Resolution Mode (LRM) used in conventional radar altimetry, SAR mode allows to reduce the along-track footprint from several kilometers to 300 m. In this paper we assess the SAR mode performance over the Antarctic ice sheet using sporadic CryoSat-2 acquisitions carried out in winter 2014 on dedicated zones (Lake Vostok and Adelie land). Level-1 data from two different algorithms are exploited: the CNES Cryosat-2 Processing Prototype (CPP) and the ESA Instrument Processing Facility (IPF). Firstly, results show that the estimated surface elevation from the SAR mode acquisitions over the flat surface of Lake Vostok is within the same order of magnitude as available DEMs. Mean bias is in the order of several centimeters, without any corrections accounting for the snowpack volume scattering. Secondly, the undertaken analyzes evidence that SAR altimetry has either no, or only small, sensitivity to the along-track slope-induced effect, while the across-track slope-induced effect is similar as LRM. Thirdly, SAR altimetry mode appears to be more performant than Pseudo-LRM over the steep slopes of the Antarctica margins. The precision of the estimated surface elevation is improved by 30%, and the processed waveforms are less noisy. Overall, this work demonstrates the improved measuring capability offered by SAR mode altimetry over the ice sheet surface compared to conventional altimetry.

Fenofibrate and Atorvastatin Have Opposite Effects on the Vascularization and Characteristics of Visceral Adipose Tissue in New Zealand White Rabbits

Unlike previous altimetric missions, the CryoSat-2 altimeter features a novel Synthetic Aperture Radar (SAR) mode that allows higher resolution and more accurate altimeter-derived parameters in the coastal zone, thanks to the reduced along-track footprint. The scope of this study is to quantify regionally the skills of CryoSat-2 SAR altimetry for distances to coast smaller than 10 km, during the mission lifetime and at different time scales. The validated geophysical altimeter parameters are the sea surface height above the ellipsoid, the significant sea wave height and wind speed, all computed at 20 Hz. These have been compared to in situ and regional model data along the coasts of German Bight and West Baltic Sea during a time interval of almost six years, from July 2010 to March 2016, to investigate both instantaneous and seasonal behaviour.From CryoSat-2 FBR (Full Bit Rate) data, a Delay-Doppler processing and waveform retracking tailored specifically to the coastal zone has been carried out, by applying a Hamming window and zero-padding, using an extended vertical swath window in order to mitigate tracker errors. Moreover, a dedicated SAMOSA-based coastal retracker (here referred to as SAMOSA+) has also been implemented.Since one of the highest remaining uncertainties in the altimeter parameters estimated in coastal shallow waters arises from residual errors in the applied range and geophysical corrections, innovative and high resolution solutions for ocean tide model, geoid, mean sea surface and wet tropospheric correction have been selected. As CryoSat-2 SAR and LRM (Low Rate Mode) modes are not collocated in time, in order to quantify the improvement in the coastal zone with respect to pulse-limited altimetry, 20 Hz PLRM (pseudo-LRM) data from CryoSat-2 FBR were built and retracked, adopting the ALES adaptive sub-waveform approach, with a numerical Brown-based retracker, here referred to as TALES.The cross-validation proves the good consistency between PLRM and SAR sea level anomaly in the coastal zone. The regional ocean model (BSH) shows the highest agreement with the SAR sea level anomaly, with a standard deviation of the differences (stdd) of 24 cm, whereas the corresponding value with respect to PLRM is 55 cm. Distance to coast plots show that land contamination begins to affect sea level and wave measurements at 2 km from the coast in SAR and at 3.5 km in PLRM TALES. The analysis of monthly mean time-series shows that SAR Altimetry is able to measure the sea level monthly mean in the coastal zone of the region of interest, during the entire mission, more precisely than PLRM. The cross-validation against in situ data also proves the higher accuracy of SAR SAMOSA+ compared to PLRM TALES in the coastal zone, with average SLA stdd of 4.4 cm and 8.4 cm respectively.

A Semianalytical Model of the Synthetic Aperture, Interferometric Radar Altimeter Mean Echo, and Echo Cross-Product and Its Statistical Fluctuations

This paper develops a model of the synthetic aperture, interferometric satellite radar altimeter echo power, and echo cross-product. The model uses the smallness of the satellite pitch and roll angles, and the limited range of satellite altitude to provide a semianalytical echo model, whose numerical dimensions are limited to two in the synthetic aperture case or three in the interferometric case at large roll angles, making its application to extensive data practical. By not implementing the synthetic beam formation, it is demonstrated that the model recovers the conventional case of a pulse-limited altimeter. A theoretical description of the fluctuations in the multilooked, interferometric cross-product, and the synthetic aperture and pulse-limited powers is given by extending the model to describe correlations between individual looks in each case. The model offers the potential to retrieve ocean-surface parameters from synthetic aperture and pulse-limited altimeter data simultaneously, with obvious application to the new generation of altimeters onboard CryoSat-2, Sentinel-3, and Sentinel-6 during the transition to operational synthetic aperture radar altimetry.

Assessment of CryoSat-2 SAR mode wind and wave data

Significant wave height (SWH) and surface wind speed (WS) products from the CryoSat-2 Delay-Doppler, which is commonly known as Synthetic Aperture Radar (SAR), Mode are validated against operational ECMWF atmospheric and wave model results in addition to available observations from buoys, platforms and Jason-2 altimeter. The CryoSat-2 SAR Mode data are processed from Level 1A (also known as Full Bit Rate, FBR, in the CryoSat-2 terminology) up to L1B in accordance to the Delay-Doppler algorithm, and then retracked using SAMOSA (SAR Altimetry MOde Studies and Applications) SAR return waveform model, as implemented in the Grid Processing on Demand (G-POD) service called SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation (SARvatore). The data cover two geographic boxes: one in the northeast Atlantic Ocean (NE Atlantic Box) for the period from 6 September 2010 to 30 June 2014 and the other is in the eastern Pacific (Pacific Box) for the period from 7 May 2012 to 30 June 2014. The amount of data is limited by the CryoSat-2 SAR Mode acquisition mask over ocean but is large enough to ensure robustness and significance of the results. The results show that the quality of both CryoSat-2 SAR SWH and WS products is very high when compared to typical altimetry mission requirements. When compared against model and in-situ data, the correlation coefficients are as high as 0.98 for SWH and 0.95 for WS while the bias and standard deviation of the difference is less than 5% and 0.3 m, respectively, for SWH and less than 0.3 m/s and 1.3 m/s, respectively for WS. The results show that the quality of both CryoSat-2 SAR SWH and WS products is in line with Jason-2 performances and satisfies the typical altimetry mission requirements.