Synthetic Aperture Radar Products Research Articles

An Advanced Quality Assessment and Monitoring of ESA Sentinel-1 SAR Products via the CyCLOPS Infrastructure in the Southeastern Mediterranean Region

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA’s Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure’s reliability.

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons.

Research on A Special Hyper-Pixel for SAR Radiometric Monitoring

The objects presented in synthetic-aperture radar (SAR) images are the products of the joint actions of ground objects and SAR sensors in specific geospatial contexts. With the accumulation of massive time-domain SAR data, scholars have the opportunity to better understand ground-object targets and sensor systems, providing some useful feedback for SAR-data processing. Aiming at normalized and low-cost SAR radiometric monitoring, this paper proposes a new hyper-pixel concept for handling multi-pixel ensembles of semantic ground targets. The special hyper-pixel in this study refers to low-rise single-family residential areas, and its radiation reference is highly stable in the time domain when the other dimensions are fixed. The stability of its radiometric data can reach the level of 0.3 dB (1σ), as verified by the multi-temporal data from Sentinel-1. A comparison with tropical-rainforest data verified its availability for SAR radiometric monitoring, and possible radiation variations and radiation-intensity shifts in the Sentinel-1B SAR products ere experimentally monitored. In this paper, the effects of seasonal climate and of the relative geometrical states observed on the intensity of the hyper-pixel’s radiation are investigated. This paper proposes a novel hyper-pixel concept for processing and interpreting SAR-image data. The proposed residential hyper-pixel is shown to be useful in multi-temporal-data observations for normalized radiometric monitoring and has the potential to be used for cross-calibration, in addition to other applications.

Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric and morphological properties. In addition, SAR acquisitions are weather-independent, providing a major advantage over optical imaging during periods of cloud cover. Most often, vegetation properties are measured using vegetation indices, including simple ratios of backscatter intensities. However, this approach can miss the scattered wave purity from vegetation targets. The scattered wave characteristic is an essential parameter as it is susceptible to the complex random structure of vegetation. In this study, we propose a novel dual-polarimetric SAR vegetation descriptor based on the co-pol purity component of the wave. We use this descriptor within a semi-empirical vegetation model to estimate soil moisture. It is noteworthy that this proposed method to retrieve soil moisture uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Therefore, the proposed method has the potential for operational scale monitoring applications at a global scale. This study validated different crop types (viz. canola, oats, forage, maize, soybeans, sugar beet, wheat, winter wheat, winter barley) over two test sites using airborne SAR data. The Root Mean Square Error (RMSE) values for soil moisture are within the range of 4.3% to 7.7% with the Pearson correlation coefficient r greater than 0.6. This novel descriptor will facilitate the operational use of dual-polarimetric GRD SAR data for vegetation monitoring and soil moisture estimation.The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

Assessing the Impact of Positioning Errors in Car-Borne Repeat-Pass SAR Interferometry With a Controlled Rail-Based Experiment

Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1–2 cm using a local GNSS reference station and up to 10–15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.

Nonlocal Model-Free Denoising Algorithm for Single- and Multichannel SAR Data

Among the large number of synthetic aperture radar (SAR) image despeckling approaches existing in literature, nonlocal (NL) filters have received a desirable boost. However, often NL approaches define the similarity criterion based on model assumptions, such as a fully developed speckle model. This assumption may not be verified in high-resolution images of urban environments. To address this issue, a standalone model-free despeckling framework is proposed in this article. The presented approach provides a generic framework for denoising a variety of SAR products, from a single-intensity/amplitude image to polarimetric and interferometric SAR data. In particular, the method is based on the empirical distributional similarity between the patch containing the pixel to be recovered and the patch containing a similar candidate pixel. To decide whether the patches follow a similar distribution, the Kolmogorov–Smirnov test is adapted. Finally, the restoration process aggregates the selected similar pixels based on their relative importance derived from their distribution similarities. To mitigate the blurring effect and preserve the resolution, the inhomogeneity of the ratio image is used to perform the bias reduction step. The designed generic despeckling filter was tested on different products of SAR data. The results show that the method proves to be an unbiased restoration approach and is able to preserve structures and textures. It works fully automatically and efficiently with single and multilook (and multichannel) images.

The Extended Timing Annotation Dataset for Sentinel-1—Product Description and First Evaluation Results

This paper introduces the extended timing annotation dataset (ETAD) product for Sentinel-1 (S-1) which was developed in a joint effort of German Aerospace Center (DLR) and the European Space Agency (ESA). It allows to correct range and azimuth timing of S-1 images for geophysical effects as well as for inaccuracies in synthetic aperture radar (SAR) image focusing. In combination with the precise orbit solution, these effects determine the absolute geolocation accuracy of S-1 SAR images and the relative collocation accuracy of repeat pass image stacks. ETAD contains the gridded timing corrections for the tropospheric and ionospheric path delays, the tidal-based surface displacements, and the SAR processing effects, all of which are computed for each data take using standard models from geodesy and auxiliary atmospheric data. The ETAD product helps S-1 users to significantly improve the geolocation accuracy of the S-1 SAR products to better than 0.2 m and offers a potential solution for correcting large scale interferometric phase variations. The product layout and the product generation are described schematically. The paper also reports first results for different SAR techniques: first, the improvement in geolocation accuracy down to a few centimeters by verification of accurately surveyed corner reflector positions in the range-azimuth plane; second, the well-established offset-tracking technique, that is used for systematic ice velocity monitoring of ice sheets and glaciers, where ETAD can reduce velocity biases down to sub-centimetric values; and third, the correction of atmospheric phase contributions in wide-area interferograms used for national and European ground motion services. These early results proof the added value of the ETAD corrections and that the product design is well suited to be integrated into the processing flows of established SAR applications such as absolute ranging of targets, speckle/feature tracking and interferometry.

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia

Earth observation (EO) satellites facilitate hazard monitoring and mapping over large-scale and remote areas. Despite Synthetic Aperture Radar (SAR) satellites being well-documented as a hazard monitoring tool, the uptake of these data is geographically variable, with the Australian continent being one example where the use of SAR data is limited. Consequently, less is known about how these data apply in the Australian context, how they could aid national hazard monitoring and assessment, and what new insights could be gleaned for the benefit of the international disaster risk reduction community. The European Space Agency Sentinel-1 satellite mission now provides the first spatially and temporally complete global SAR dataset and the first opportunity to use these data to systematically assess hazards in new locations. Using the example of Australia, where floods and uncontrolled bushfires, earthquakes, resource extraction (groundwater, mining, hydrocarbons) and geomorphological changes each pose potential risks to communities, we review past usage of EO for hazard monitoring and present a suite of new case studies that demonstrate the potential added benefits of SAR. The outcomes provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context. Future opportunities to improve national hazard identification will arise from: new SAR sensing capabilities, which for Australia includes a first-ever civilian EO capability, NovaSAR-1; the integration of Sentinel-1 SAR with other EO datasets; and the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes to support operational applications.

A Method to Assess the Performance of SAR-Derived Surface Soil Moisture Products

Synthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C -band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C -band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C -band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged.

Surface soil moisture estimation at high spatial resolution by fusing synthetic aperture radar and optical remote sensing data

The difficulty of accurate and large-scale measurement for surface parameters limits the regional surface soil moisture (SSM) estimation using synthetic aperture radar (SAR). Moreover, the coarse resolution of soil moisture products generated by existing methods, which fuse SAR and passive microwave products, cannot fully satisfy the requirement of specific regional applications. To solve this problem, an SAR-optical data fusion method for soil moisture estimation (SOFSME) based on a cascade neural network is proposed in this study. SOFSME obtains surface parameters from historical soil moisture images and related environmental images to estimate a SSM image with high resolution at large scale from Sentinel-1A C-band SAR data. Validation experiments in single and multiple land-use type areas showed that the SOFSME performed best on bare soil areas with a median root mean square error of 0.0203. The median universal image quality index of estimated soil moisture image was 0.1454, which was better for single cropland areas than multi-land-use type areas. The Pearson correlation coefficient showed a median value of 0.7645 in both experiments. These results showed that the SOFSME had high accuracy, availability, and stability in regional soil moisture estimation. Compared with existing methods, the SOFSME can provide high-quality soil moisture images and does not directly depend on field measurement data. Thus, the proposed SOFSME method is of great value for high-resolution soil moisture estimation in more regional applications.

INTEGRATING TIME-SERIES SAR DATA AND ORYZA CROP GROWTH MODEL IN RICE AREA MAPPING AND YIELD ESTIMATION FOR CROP INSURANCES

Abstract. Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. Parameterised classification with multi-temporal features derived from regularly acquired, C-band, VV and VH polarized Sentinel-1A SAR imagery was used for mapping rice area. A fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded σ0 values, which included strip mosaicking, co-registration of images acquired with the same observation geometry and mode, time-series speckle filtering, terrain geocoding, radiometric calibration and normalization. Further Anisotropic non-linear diffusion (ANLD) filtering was done to smoothen homogeneous targets, while enhancing the difference between neighbouring areas. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations to classify rice pixels. Rice detection was based on the analysis of temporal signature from SAR backscatter in relation to crop stages. About sixty images across four footprints covering 16 samba (Rabi) rice growing districts of Tamil Nadu, India were obtained between August 2017 and January 2018. In-season site visits were conducted across 280 monitoring locations in the footprints for classification purposes and more than 1665 field observations were made for accuracy assessment. A total rice area of 1.07 million ha was mapped with classification accuracy from 90.3 to 94.2 per cent with Kappa values ranging from 0.81 to 0.88. Using ORYZA2000, a weather driven process based crop growth simulation model developed by IRRI, yield estimates were made by integrating remote sensing products viz., seasonal rice area, start of season and backscatter time series. By generating average backscatter for each time series and dB stack for each SoS, LAI values were estimated. The model has generated rice yield estimate for each hectare which were aggregated at administrative boundary level and compared against CCE yield. Yield Simulation accuracy of more than 86–91% at district level and 82–97% at block level from the study indicates the suitability of these products for policy decisions. SAR products and yield information were used to meet the requirements of PMFBY crop insurance scheme in Tamil Nadu and helped in identifying or invoking prevented/failed sowing in 529 villages and total crop failure in 821 villages. In total 303703 farmers were benefitted by this technology in getting payouts of INR 9.94 billion through crop insurance. The satellite technology as an operational service has helped in getting quicker payouts.

High‐speed optical synthetic aperture radar target recognition system

With the development of synthetic aperture radar (SAR), it is more and more important to recognise the image obtained by SAR. SAR is a useful tool for geoscience and remote sensing, and there are a large amount of SAR images and products. How to quickly identify and process this massive information in the shortest time and find the target in time is the focus of research in this field. In the traditional processing method, the digital processing is used. It is time and storage consumption due to its massive data. This study proposes optical methods for target recognisation of SAR images. In this method, it takes the advantages of high speed and high resolution in optical processing methods to solve the bottleneck encountered by traditional methods. The method is based on an optical correlator. The optical processing method has a large capacity for a single processing and has a great advantage over the simple calculation method in processing speed. The experimental results demonstrate the effectiveness of the optical processing method.

Micro-Motion Estimation of Maritime Targets Using Pixel Tracking in Cosmo-Skymed Synthetic Aperture Radar Data—An Operative Assessment

In this paper, we propose a novel strategy to estimate the micro-motion (m-m) of ships from synthetic aperture radar (SAR) images. To this end, observe that the problem of motion and m-m detection of targets is usually solved using synthetic aperture radar (SAR) along-track interferometry through two radars spatially separated by a baseline along the azimuth direction. The approach proposed in this paper for m-m estimation of ships, occupying thousands of pixels, processes the information generated during the coregistration of several re-synthesized time-domain and not overlapped Doppler sub-apertures. Specifically, the SAR products are generated by splitting the raw data according to a temporally small baseline using one single wide-band staring spotlight (ST) SAR image. The predominant vibrational modes of different ships are then estimated. The performance analysis is conducted on one ST SAR image recorded by COSMO-SkyMed satellite system. Finally, the newly proposed approach paves the way for application to the surveillance of land-based industry activities.

A low-cost method applicable worldwide for remotely mapping lava dome growth

Lava dome growth and collapse represents both a significant hazard, as it can trigger pyroclastic density currents, and a monitoring challenge, limiting monitoring to a few known active volcanoes. Here, I propose a new differencing technique based on Synthetic Aperture Radar (SAR) amplitude images to quantify the extent of lava dome growth. This differencing technique, which is both low cost and can be utilized worldwide, is applied to SAR amplitude images at Mount St. Helens and validated using 2004–2008 aerial photography observations. Difference of amplitude images accurately characterize the dome growth location. The low ground resolution of the 2004–2008 SAR data leads to underestimation by 10 to 15% of the dome extent, but the accuracy of this method will increase with the improved resolution of current and future SAR missions. Amplitude images are a low-level SAR product available from all SAR satellites, mostly freely, making the proposed method ideal for systematic, low-cost monitoring of lava dome growth worldwide with minimum processing required.

Performance Analysis of TanDEM-X Quad-Polarization Products in Pursuit Monostatic Mode

Since 2010, the two twin synthetic aperture radar (SAR) satellites TerraSAR-X and TanDEM-X have been acquiring high-resolution images to generate a global Earth's digital elevation model (DEM). Both satellites have been flying in a controlled close orbit formation, acquiring data in the nominal bistatic stripmap single-polarization mode. Once the acquisition of the dataset for the generation of the DEM has been completed, the flexibility offered by both SAR instruments in terms of interferometric, imaging, and polarization modes has been further exploited to demonstrate the different capabilities of the TanDEM-X experimental modes. By activating the dual-receive antenna mode, full polarimetric data can be acquired. For the first time, it has been possible to systematically command quad-polarization acquisitions in a dedicated TanDEM-X mission science phase, started in October 2014. In this paper, we present a first performance analysis and quality assessment of such quad-polarization products. The SAR image resolution and the noise equivalent sigma zero have been evaluated to show the quality of the focused SAR products. The influence of different instrument parameters on the SAR and interferometric performance, such as chirp bandwidth, pulse repetition frequency, or block adaptive quantization, has been investigated as well. For the evaluation of the interferometric performance, key parameters such as coherence and interferometric phase error have been analyzed. In this paper, the obtained results are presented and recommendations are given for the optimization in the commanding of TanDEM-X quad-polarization acquisitions.

First Characterization and Performance Evaluation of Bistatic TanDEM-X Experimental Products

TanDEM-X is an innovative bistatic and interferometric mission comprising the two twin synthetic aperture radar (SAR) satellites TerraSAR-X and TanDEM-X, which fly in a configurable close formation with the main goal to generate a global, high-accurate, and homogeneous digital elevation model (DEM) of the Earth’s surface. The DEM is acquired in bistatic stripmap single polarization mode. On top of that, due to the flexibility offered by operating both satellites in different interferometric and imaging constellations, combinations of several experimental modes are possible. A dedicated science phase of the mission started in October 2014 and opened the full exploitation of such experimental modes. These unique data will support a variety of scientific remote sensing applications and will demonstrate new and innovative SAR techniques. In this paper, the expected SAR and interferometric performance of the operationally processed experimental modes available during the bistatic global DEM acquisition phase is modeled and characterized with in-orbit data. Key parameters for the determination of the quality of an SAR product, such as image resolution or noise equivalent sigma zero, or for the interferometric performance estimation, such as coherence or relative height error, are analyzed. Finally, possibilities to increase the quality of the generated DEMs are addressed.

Advanced InSAR techniques for deformation studies and for simulating the PS-assisted calibration procedure of Sentinel-1 data: case study from Thessaloniki (Greece), based on the Envisat/ASAR archive

ABSTRACTUsing state-of-the-art InSAR techniques, namely persistent scatterers (PSs) and small baseline subset (SBAS) approaches, this study contributes to open geotechnical questions in the area of Thessaloniki (Greece) from a remote-sensing perspective. It also demonstrates the potential of these techniques for calibration purposes, with reference to the new C-band synthetic aperture radar (SAR) sensor on board the Sentinel-1 mission satellites. By exploiting the historical archive of Envisat/ASAR data, as well as a pair of the first Sentinel-1A SAR images, recent (2004–2010) deformation rates up to 18 mm year–1 are detected over the study area. These results are then compared to the findings of previous InSAR and geophysical observations, indicating for example, subsidence or tectonic activity. On the other hand, the usefulness of the PS technique is shown over the same region for external SAR calibration purposes. This process simulates the PS-assisted calibration procedure to be applied systematically to Sentinel-1 SAR products.

The InSAeS4 Airborne X-Band Interferometric SAR System: A First Assessment on Its Imaging and Topographic Mapping Capabilities

We present in this work a first assessment of the imaging and topographic mapping capabilities of the InSAeS4 system, which is a single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR). In particular, we first provide a brief description of the InSAeS4 sensor. Then, we discuss the results of our analysis on the SAR and interferometric SAR products relevant to the first flight-test campaign. More specifically, we have exploited as reference the GPS measurements relevant to nine Corner Reflectors (CRs) deployed over the illuminated area during the campaign and a laser scanner Digital Elevation Model (DEM). From the analysis carried out on the CRs we achieved a mean geometric resolution, for the SAR products, of about 0.14 m in azimuth and 0.49 m in range, a positioning misalignment with standard deviation of 0.07 m in range and 0.08 m in azimuth, and a height error with standard deviation of 0.51 m. From the comparison with the laser scanner DEM we estimated a height error with standard deviation of 1.57 m.

Agreement and Complementarity of Sea Ice Drift Products

Changes in Arctic sea ice have raised questions about changes in sea ice drift patterns. Reduced sea ice coverage may open up the Arctic to further exploration of maritime activities, particularly during the summer months. Given such changes, it is important to investigate differences between available sea ice drift products. Products based on synthetic aperture radar (SAR), radar scatterometer, and radiometer are compared for both motion speed and direction within this study. Two C-band SAR and one L-band SAR product are used in the comparison. Differences in temporal and spatial resolutions of the drift estimates spanning from July 2010 until June 2011 are investigated. High temporal and spatial resolution was proven useful to fully capture the sea ice drift in the Fram Strait. For summer coverage, SAR data are a prerequisite and L-band is desirable. The two C-band SAR products have a mean speed correlation of 0.90 and exhibit high conformity, despite being generated by separate processing methods. The L-band SAR product and the scatterometer and radiometer products are to a lower degree in agreement with each other and the C-band SAR products, which may be attributed to the products’ dependency on the temporal baseline. Depending on the choice of sensor or combination of sensors, the resulting 12-month mean drift varies between 0.09 and 0.12 m/s excluding L-band SAR. The latter shows a particularly low drift of 0.05 m/s, which we attribute to an over-representation of slow ice.