Synthetic Aperture Radar (SAR) represents nowadays a key technology in Earth Observation (EO), evolving its original capabilities into both large-scale monitoring of geophysical parameters and very high-resolution imaging with short revisit times. The increase in system performance and the wide range of application scenarios require significant efforts in the design of current and future SAR missions: one of the most critical on-board operations is the digitization of the received echoes, directly impacting the final image quality and, at the same time, limited by the available downlink capacity. State-of-the-art quantization methods, such as Block Adaptive Quantization (BAQ), offer a good trade-off between signal quality and overall complexity but lack adaptivity to the imaged scenario. This leads to different impacts of the quantization error on the final SAR image. As an evolution of BAQ, Performance-Optimized BAQ (PO-BAQ) is a recently proposed quantization method, which addresses this issue by employing variable quantization rates across the scene, targeting specific performance requirements in the final SAR image. In this paper, we present a feasibility study of variable bitrate allocation in a realistic SAR mission scenario: to ensure flexibility, we consider the bitrate allocation map (BRM) to be uploaded at commanding phase during each ground segment contact, individually tailoring the required performance quality for each acquisition. State-of-the-art uplink data rates are considered, and the complete performance evaluation after SAR processing is carried out using the experimental on-board processor developed within the SOPHOS Horizon 2020 project.

Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the raw data by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.

Next-generation SAR systems will be capable of performing high-resolution, wide-swath acquisitions at frequent revisit times. The overcoming of conventional SAR limitations will also lead to the generation of very large volumes of onboard data which need to be stored and managed by the system and downlinked to the ground. This poses severe constraints in terms of onboard memory requirements and downlink capacity and, in this challenging scenario, the onboard quantization of SAR raw data represents a crucial aspect, acting as a trade-off between the achievable product quality and the resulting onboard volume of data. State-of-the-art quantization schemes allow for enhanced data rate allocation, however, the optimization is directly performed on raw data, without targeting a desired performance on the final higher-level SAR/InSAR product. In this paper, we investigate the use of artificial intelligence (AI), and in particular of deep learning (DL), for developing a flexible onboard SAR raw data quantization method, with the aim of deriving an optimized and fully adaptive data rate allocation given a set of desired performance metrics and requirements in the resulting focused SAR and InSAR products, without relying on a priori information on the acquired scene. Different performance parameters are considered, such as the signal-to-quantization noise ratio (SQNR), the phase errors, the InSAR coherence loss as well as the resulting noise equivalent sigma zero (NESZ), extending the capabilities of the architecture to provide multiple bitrate estimations for a single input scene at the same time, depending on the desired application case. We use experimental TanDEM-X bistatic SAR data, both for the training of the DL model as well as for the validation and demonstration of the suitability of the proposed method. In view of a potential onboard implementation, a possible hardware architecture for the proposed compression scheme is investigated as well.

Advanced Synthetic Aperture Radar (SAR) systems acquire a large volume of data, which necessitates the development of efficient data compression methods, beyond the current conventional techniques. Sentinel-1, as one the most popular SAR missions, provides global freely accessible data. However, the available raw data (i.e., Level-0 products) are quantized before being transferred, thus the statistics are different, hindering the validation of new algorithms mainly based on machine/deep learning paradigms. To enable elaboration of further SAR raw data compression, in this study, we propose a procedure to add random quantization noise to the decoded Sentinel-1 SAR raw data in order to obtain adapted uniformly quantized raw data that resemble the statistics of the uncompressed SAR raw data onboard the satellites. This method opens further opportunities to create large benchmarks for SAR raw data for data compression and other applications. The performance of data compression techniques (Block Adaptive Quantization (BAQ) and a complex-valued autoencoder-based data compression scheme) is evaluated on the adapted uniformly quantized raw data, and the effectiveness of the defined procedure is demonstrated.

Present and next-generation synthetic aperture radar (SAR) missions require an increasing volume of onboard data, due to the employment of large bandwidths, multiple channels and polarizations, and large swath widths acquired by bi- and multi-static sensor configurations. This leads to stringent requirements in terms of onboard memory and downlink capacity, hence making the proper quantization of the SAR raw data represents an task of utmost importance, as it affects the amount of data but also the quality of the SAR and InSAR products. This paper presents novel methods for efficient SAR raw data compression, which make use of artificial intelligence for the joint optimization of bitrate allocation and the resulting performance and exploit the potential of transform and predictive coding schemes for data volume reduction in the context of multi-azimuth channel (MAC) SAR. Simulations and analyses on real data are presented, showing the suitability of the proposed methods.

Recent advances in Synthetic Aperture Radar (SAR) sensors have enabled the acquisition of very high-resolution images with wide swaths, large bandwidth and in multiple polarization channels. As a result of the significant increase of SAR data size, an effective compression of the acquired data is of paramount importance. However, conventional data compression methods demonstrate limited effectiveness when applied to SAR data. In order to tackle this problem, in this study, a Complex-Valued (CV) end-to-end deep learning-based architecture based on convolutional autoencoders is proposed to compress Single Look Complex (SLC) SAR data. By relying on dual polarization SAR data, one of the polarization channels of the data is used as the side information to assist the reconstruction of the compressed channel with lower data loss. The obtained results demonstrate the remarkable potential and capability of CV deep learning-based methods for SAR data compression.

The TanDEM-X Forest/Non-Forest map, derived from the volume decorrelation factor using a supervised fuzzy clustering algorithm, represents the baseline approach for forest mapping with TanDEM-X data at global scale. Deep learning (DL) methods have been demonstrated to be also suitable for mapping forests at large scale with TanDEM-X interferometric data. In this work, we investigate the capabilities of using a U-Net-like architecture with TanDEM-X interferometric data for forest mapping at 6 m resolution. With such high-resolution data, we aim at improving the forest mapping accuracy and to be able to detect forest degradation over the Amazon rainforest caused e.g. by selective logging, fires and natural hazards. The classification improvements already observed applying DL methods on TanDEM-X data allow for the generation of large scale time-tagged mosaics. The explotation of such mosaics over extended areas is a key aspect for the detection and monitoring of forest dynamics worldwide.

Next generation SAR systems will bring a huge improvement in terms of SAR performance and coverage through the use of large bandwidths and digital beamforming techniques in combination with multiple acquisition channels. This will allow for overcoming the limitations imposed by conventional SAR imaging for the acquisition of wide swaths and, at the same time, of finer resolutions. The significant improvements that can be achieved in terms of performance are associated with the generation of large volumes of data, which, in turn, set harder requirements for the onboard memory and downlink capacity of the system.In this scenario, an efficient quantization of SAR raw data is of crucial importance, as it defines the amount of onboard memory and it directly affects the quality of the generated SAR products. In the Performance-Optimized BAQ (PO-BAQ), the basic concept of the original BAQ is further extended according to the approach proposed in [1], which represents a first attempt for an optimization of the resource allocation depending on the performance requirement defined for the final higher-level SAR/InSAR product. As quantization errors are significantly influenced by the local distribution of the SAR intensity, such an optimization is achieved by exploiting the a priori knowledge of the SAR backscatter statistics of the acquired scene.In this contribution we investigate the feasibility of deriving a Performance-Optimized bitrate map through a machine learning-based architecture, in view of a future possible onboard realization.

Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations, and the acquisition of large swath widths at fine spatial resolutions, which result in challenging requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect, as it affects the volume of data to be stored and transmitted to the ground as well as the quality of the resulting SAR products. Dynamic predictive block-adaptive quantization (DP-BAQ) is a novel technique, recently proposed by the authors, consisting of a low-complexity data compression method, and its application is particularly suitable for staggered SAR systems. DP-BAQ exploits the existing correlation among the azimuth raw data samples by applying linear predictive coding (LPC). This results in a data rate reduction of up to 25% with respect to state-of-the-art SAR quantization methods. In this letter, we test and validate the potential of DP-BAQ on airborne SAR data which emulates the system scenario of Tandem-L, a German Aerospace Center (DLR) mission proposal for a bistatic L-band system. For this purpose, an experimental SAR image has been acquired at the L-band by the airborne DLR flugzeug-SAR (F-SAR) sensor over the Kaufbeuren area, in Southern Germany. In order to simulate the staggered SAR acquisition mode, we implemented a dedicated resampling and filtering of the data. Our analyses confirm the effectiveness of DP-BAQ for efficient data volume reduction, exhibiting a consistent and promising performance when tested on areas characterized by different land cover types and backscatter statistics.

Frequency scanning (FScan) is an innovative acquisition mode for synthetic aperture radar (SAR) systems. The method is based on the frequency-dependent beam pointing capabilities of phased array antennas, artificially increased via the combined use of true time delays and phase shifters within the array antenna. By this, typical limitations of conventional SAR systems in terms of achievable swath width and azimuth resolution can be mitigated, and so a wide swath can be imaged maintaining a fine azimuthal resolution. In the first part of the article, we introduce the theoretical concept, which is necessary to evaluate the reduced echo window length (EWL) with respect to equivalent stripmap data and the implications for the transmit pulse characterization. An FScan sensor flying in a TerraSAR-X-like orbit is shown to be capable of imaging an 80-km wide swath with 1-m azimuth resolution. The resulting time–frequency properties of the recorded raw data make the traditional SAR data compression algorithms such as block-adaptive quantization (BAQ) highly inefficient in this case. Therefore, the second part of the article investigates dedicated quantization methods for efficient data volume reduction in FScan systems. Different solutions are investigated and evaluated through simulations. Various transformations of the raw data have been exploited to optimize the encoding process, including deramping, fast Fourier transform (FFT), and blockwise approaches. Compared with standard BAQ in the time domain, the suggested data compression methods significantly improve the resulting signal-to-quantization noise ratio, allowing for the reduction in the overall data volume by about 60% for the considered system scenario, while maintaining robustness in the presence of inhomogeneous scene characteristics at the cost of a modest complexity increase for its on-board implementation.

The TanDEM-X Forest/Non-Forest Map, derived from the volume decorrelation factor using a supervised fuzzy clustering algorithm, represents the baseline approach for forest mapping with TanDEM-X data at large/global-scale. Deep learning methods have been demonstrated to be also suitable for mapping forests with TanDEM-X interferometric data, e.g. by utilizing a U-Net convolutional neural network (CNN) on full-resolution images. In this work, we investigate the capabilities of using a U-Net-like architecture with TanDEM-X interferometric data for forest and water mapping on a large scale. An ad-hoc training strategy has been developed to detect forest and water on TanDEM-X images acquired with different acquisition geometries over the Amazon rainforest. In this case, a significant performance improvement with respect to the clustering approach, with a mean f1-score increase of 0.13 on test images has been measured with respect to the baseline clustering technique. The trained U-Net over the Amazon rainforest has been used to extend the forest and water mapping to other tropical forests over Africa and Asia. The classification improvements applying CNN methods on TanDEM-X data allow for the generation of time-tagged mosaics over the tropical forests by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017, skipping the weighted mosaicking of overlapping images used in the clustering approach for achieving a good final accuracy, as well as avoiding the use of external layers to filter out water surfaces. The explotation of such mosaics over extended areas is a key aspect for the detection and monitoring of deforested areas worldwide.

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance, since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground and, at the same time, it affects the resulting SAR imaging performance. In this paper, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression which aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses on experimental TanDEM-X interferometric data are presented, which demonstrate the potentials of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions.

Decorrelation phenomena are always present in synthetic aperture radar interferometry (InSAR). While this implies a certain level of signal degradation, decorrelation is also a characteristic of the type of imaged target itself and can, therefore, be seen as a source of information. In this letter, we investigate InSAR decorrelation effects at the X-band by fitting volume and temporal decorrelation trends using the unique combination of data provided by the TanDEM-X (TDX) and PAZ spaceborne missions. The innovative use of this constellation allows for the acquisition of both single- and repeat-pass data at short revisit times. The concurrent availability of simultaneous acquisitions and the fine temporal resolution makes this constellation the ideal observation scenario for the study of decorrelation phenomena. Overall, we analyze five test sites, characterized by the presence of different land cover classes, and for each of them, we provide volume and temporal decorrelation fitting parameters. The performed analysis gives a first insight on the potential of combining bistatic and repeat-pass InSAR acquisitions also in view of future spaceborne constellations, which could benefit from the TDX/PAZ experience.

Decorrelation phenomena are always present in Synthetic Aperture Radar Interferometry (InSAR). While this implies a certain level of signal degradation, the decorrelation is also a characteristic of the type of imaged target itself and can therefore be seen as a source of information. Correctly accounting for the type and amount of decorrelation is crucial when using InSAR systems for land classification purposes. In this paper we aim at modeling InSAR decorrelation effects at X-band for several land cover classes. In particular we model the volume and temporal decorrelation, by exploiting TanDEM-X and PAZ joint time-series. The uniqueness of the combined use of these two missions is the availability of simultaneous bistatic as well as short revisit time repeat-pass acquisitions, making it the ideal observation scenario for the study of decorrelation phenomena. The paper shows the preliminary results of the analysis on the city of Madrid (Spain) and for two land cover classes.

Staggered synthetic aperture radar (SAR) is an innovative SAR acquisition concept which exploits digital beamforming (DBF) in elevation to form multiple receive beams and continuous variation of the pulse repetition interval to achieve high-resolution imaging of a wide continuous swath. Staggered SAR requires an azimuth oversampling higher than an SAR with constant pulse repetition interval (PRI), which results in an increased volume of data. In this article, we investigate the use of linear predictive coding, which exploits the correlation properties exhibited by the nonuniform azimuth raw data stream. According to this, the prediction of each sample is calculated onboard as a linear combination of a set of previous samples. The resulting prediction error is then quantized and downlinked (instead of the original value), which allows for a reduction of the signal entropy and, in turn, of the onboard data rate achievable for a given target performance. In addition, the a priori knowledge of the gap positions can be exploited to dynamically adapt the bit rate allocation and the prediction order to further improve the performance. Simulations of the proposed dynamic predictive block-adaptive quantization (DP-BAQ) are carried out considering a Tandem-L-like staggered SAR system for different orders of prediction and target scenarios, demonstrating that a significant data reduction can be achieved with a modest increase of the system complexity.

In this letter, a novel method for onboard data reduction for multichannel synthetic aperture radar (SAR) (MC-SAR) systems is presented. Such systems allow for high-resolution imaging of a wide swath but, on the other hand, require for their operation the acquisition and downlink of a huge amount of data: together with the intrinsic requirement related to resolution and swath width, this is due to the use of a pulse repetition frequency (PRF) typically higher than the processed Doppler bandwidth (PBW), which introduces a certain oversampling in the azimuth raw data. In this context, we propose a convenient data reduction strategy, named multichannel block-adaptive quantization (MC-BAQ), which exploits the existing correlation between subsequent azimuth samples by performing a discrete Fourier transform (DFT) of the MC-SAR data block. Then, a variable-bit quantization is applied which allows for the optimization of the resulting performance and data rate. Simulations have been carried out on scenes with distributed scatterers showing different backscatter characteristics to demonstrate that the proposed MC-BAQ allows for a significant reduction of the data volume to be downlinked to the ground at the cost of a modest increase of onboard computational effort.

For the generation of the TanDEM-X digital elevation model (DEM), with a resolution of 12 m×12 m, two global mappings and up to 10 coverages over difficult terrain have been acquired. From such a dataset, the global TanDEM-X Forest/Non-Forest Map has been generated by mosaicking more than 500,000 quick-look images at a resolution of 50 m×50 m. Such a huge amount of data can be further exploited to investigate the potentials of the TanDEM-X Fores/Non-Forest Maps available at different times for change detection, adding a new layer and valuable information to this kind of products. At a local scale, TanDEM-X full resolution images, with an interferometric resolution of 12 m×12 m, can be used for forest monitoring applications. Fine spatial resolution allows for an increase of detail in forest/non-forest classification. By combining the digital elevation information with the forest/non-forest classification, provided by the Forest/Non-Forest Map, it is possible to detect changes due to deforestation activities as well as changes due to forest degradation caused by natural phenomena, such as fires or storms. This paper addresses the investigation and first results of the potentials of TanDEM-X products for change detection purposes and the possibilities offered by TanDEM-X high-resolution images for forest monitoring.

The interferometric synthetic aperture radar (InSAR) data set, used for the generation of the global TanDEM-X (TDX) DEM, includes multiple acquisitions with different parameters. It enables a big opportunity for scientific geo-applications, such as for land characterization, classification, and monitoring. One valuable information that can be derived from interferometric SAR data for land classification describes the presence/absence of vegetation. At X-band, volume scattering produces decorrelation, even in the presence of short vegetation. As TerraSAR-X and TanDEM-X satellites are still acquiring data, the exploitation of the signatures for specific or detailed vegetation is possible. In August 2016 a ground field campaign was conducted in the Bavaria region, while dedicated TanDEM-X data takes over the same area were commanded by using different acquisition geometries and configurations. The aim of this paper is to characterize the interferometric signatures of agricultural areas from single-pass bistatic TDX acquisitions at 12 meters posting, using the on ground typification for classification purposes.

Nonlocal filters show outstanding performance in the field of interferometric phase restoration by providing strong filtering power together with high spatial features preservation. In this work we focus on the generation of Digital Elevation Models (DEM) from a pair of interferometric SAR images. In the specific, we aim at comparing the performance of state-of-the-art InSAR filtering approaches on the basis of their noise suppression and detail preservation capabilities. We exploit a dataset of TanDEM-X SAR data relative to the volcanic area of the Kamchatka region (Russia).

The increasing complexity of multi-channel SAR sensors and the real-time on-board phase/amplitude correction requirement pose new challenges for the calibration, which cannot rely on state-of-the-art calibration techniques. On the other hand, the digital hardware utilized in multi-channel SAR systems, offer new opportunities for the calibration such as on-board error correction and digital calibration. This paper addresses the internal calibration strategy for future digital beamforming SAR instruments and details the implementation of a dedicated calibration simulator software.

This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions. The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results. In this context, if two or more sensors are available to perform the surveys, a set of quasi-simultaneous data can be achieved for a certain time instant. It is understood that for such data the temporal decorrelation effect as well as the atmospheric artefacts will be strongly mitigated. By varying the acquisition geometry, it is in principle now possible to achieve cross-range resolution and retrieve the vertical profile via SAR tomography. The present paper focuses on a two-satellite scenario like TanDEM-X [1], Tandem-L [2], SAOCOM-CS [3]. In particular, TanDEM-X data, acquired in a pursuit monostatic mode, is employed to perform the demonstration over boreal as well as tropical forest.

In this paper we describe the method that has been implemented to derive the forest/non-forest maps from TanDEM-X interferometric synthetic aperture radar (InSAR) data, globally acquired in stripmap single polarization (HH) mode. Among the several observables systematically provided by the TanDEM-X system, the volume decorrelation contribution, derived from the interferometric coherence, shows to be consistently sensitive to the particular land cover type, and is therefore used as an input data set for applying a classification method based on a fuzzy clustering algorithm. Since the considered InSAR quantity strongly depends on the geometric acquisition configuration, namely the incidence angle and the interferometric baseline, a multi-clustering classification approach is used. Once the Forest/NonForest classification for individual acquisitions is generated, overlapping acquisitions are mosaicked together to improve the resulting accuracy. The final step in the Forest/NonForest map production is to apply a binary Forest/Non-Forest decision and the decision threshold is found through comparison with similar data and statistical analysis. Verification and validation of the final product will be accomplished through comparison to other forest maps. In summary, this paper covers the processing and production status of the global TanDEM-X Forest/Non-Forest map which is foreseen to be made available to the scientific community in 2017.

A calibration scheme for future SAR systems utilizing digital beamforming, is suggested. These systems pose new challenges for the calibration, as the increased complexity of multi-channel SAR does not allow for the extrapolation of current calibration techniques. Further, depending on the operation mode, real-time error measurement and correction is unavoidable, which requires a reconsideration to current calibration approaches. On the other hand, the digital hardware already present in multi-channel SAR systems offer new possibilities for novel calibration such as on-board error correction and digital calibration. This paper presents a calibration scheme suitable for multi-channel digital beamforming SAR.

For present and next-generation spaceborne synthetic aperture radar (SAR) missions, the use of always larger bandwidths, higher pulse repetition frequencies, and multiple acquisition channels is being required. Among the numerous parameters characterizing an SAR system, the specific range and azimuth bandwidth, selected for the SAR image formation, are of primary importance, since they directly affect the quality, the resolution, and the accuracy of the derived products. The purpose of this letter is to investigate their influence with particular focus on interferometric SAR (InSAR) applications. Exploiting the well-known relationships available from SAR theory, the impact of the range and the azimuth bandwidths on the coherence and on the interferometric phase errors is evaluated by means of simulations based on typical TanDEM-X acquisition scenarios. Some examples from real TanDEM-X data are provided as well. The results discussed in this letter can be used as recommendation for those who want to apply for a TanDEM-X science acquisitions proposal, exploiting the high commanding flexibility of the TanDEM-X system, and represent a valuable input for all users dealing with interferometric SAR data and for the design of future InSAR systems in general.

Among the several factors that may affect the quality of interferometric synthetic aperture radar (SAR) products, volume decorrelation represents the coherence loss contribution due to the presence of multiple scatterers within a single resolution cell, which results in an increase in the interferometric phase uncertainty. In this letter, we investigate the effects of volume decorrelation on X-band TanDEM-X interferometric data. TanDEM-X is the first bistatic spaceborne SAR mission and provides a unique, global, and manifold interferometric data set to be exploited for a variety of scientific and commercial applications. The main goal of this letter is to provide the scientific community with a characterization of volume decorrelation effects occurring at X-band for different land cover types and acquisition geometries. The potentials of volume decorrelation contribution at X-band for land classification are discussed as well and some application examples are presented.

TanDEM-X is a spaceborne mission consisting in two satellites that are operated simultaneously for bistatic SAR acquisitions. The flexibility offered by both SAR instruments allows the acquisition of full polarimetric data by activating the experimental dual-receive antenna (DRA) mode. For the first time on the TanDEM-X mission, it is possible to systematically command quad polarization acquisitions. We have estimated the quality of such full polarimetric products by a first interferometric performance analysis. The influence of different instrument parameters on the interferometric performance, such as chirp bandwidth or block adaptive quantization, have been investigated. In this paper first results are presented and recommendations for the optimization of the TanDEM-X quad polarization products are given.

In this paper a method for the generation of a global forest/non-forest map from TanDEM-X interferometric data is presented. The quality of interferometric products is strongly influenced by the specific characteristics of the illuminated land cover type. Over forested areas the presence of multiple scatterers at different heights and within a single resolution cell results in an increase of the interferometric phase uncertainty (the so-called volume decorrelation), whose intensity depends on several factors, such as the acquisition geometry, the sensor parameters, and the canopy density. From each TanDEM-X coherence map the volume decorrelation contribution can be estimated, leading to the derivation of a forest/non-forest map. This paper shows the developed approach for discriminating between forested and non-forested areas from TanDEM-X interferometric data, and presents some examples aimed at verifying the validity of the proposed method. From this, mosaics can be generated from the TanDEM-X quicklook data set at a final resolution up to 25 m × 25 m.

This paper presents an approach for locating the different snow facies of the Greenland ice sheet by exploiting bistatic TanDEM-X interferometric SAR acquisitions. Large-scale mosaics of radar backscatter and volume decorrelation contribution, derived from the interferometric coherence, are generated from the systematic TanDEM-X interferometric acquisitions. They represent the starting point for applying a classification method based on the c-Means fuzzy clustering algorithm. The presented results have been obtained starting from a dataset of TanDEM-X acquisitions performed during winter 2010-2011. Different facies can be detected and related to the physical properties of the snow pack, showing a preliminary good agreement between the obtained results and external data of snow melting.

The primary objective of the TanDEM-X mission is the generation of a global high-precision digital elevation model (DEM) by using synthetic aperture radar interferometry. This letter presents the developed strategy for estimating the relative height error of the TanDEM-X DEM on a global scale. The mosaicking process of the final DEM combines all acquisitions at full resolution and is expected to be finished by late 2016. On the other hand, global mosaics can be generated starting from quicklook images already available for each single input data take. These downsized mosaics are operationally used to analyze the performance improvement that can be achieved by combining multiple acquisitions over the same ground areas and are a powerful mean for optimizing further acquisition planning. This letter reports the expected global performance of the final TanDEM-X product in advance of the full-resolution DEM. Knowledge of the global status of the TanDEM-X DEM relative height error is fundamental for optimizing the acquisition strategy and, therefore, the final performance and represents a valuable input for the scientific community as well as for selecting suitable areas for further interferometric experiments on a global scale.

TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) is an innovative spaceborne bistatic SAR system comprising the twin satellites TerraSAR-X and TanDEM-X (TSX and TDX, respectively). The primary objective of the mission is the generation of a worldwide, timely, and consistent digital elevation model (DEM) in a bistatic synthetic aperture radar (SAR) configuration with unprecedented accuracy. For TanDEM-X and for future spaceborne SAR missions, an increasing volume of onboard data is going to be demanded. This is due to the employment of large bandwidths, high pulse repetition frequencies, and multiple polarizations, which implies inevitably hard requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect. The data rate employed for the digitization of the recorded radar signal affects both the amount of data to be stored and transmitted to the ground and the quality of the resulting SAR products. In this paper, the impact of quantization on TanDEM-X monostatic and interferometric data is evaluated. Key quantities in estimating interferometric and SAR performance, such as coherence and phase errors, are investigated in detail. Based on the obtained results, an optimization of the resource-allocation strategy for the global DEM acquisition of TanDEM-X is discussed.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements)is an interferometric SAR mission flying two radar satellites in close orbit formation. Its primary goal is the production of a homogeneous global digital elevation model (DEM) of unprecedented accuracy. Since 2010 all land surfaces have been mapped at least twice and difficult terrain even up to four times. While data acquisition for the DEM generation will be concluded in August 2014 it is expected to complete the processing of the global DEM by the end of 2015. This paper gives a status update on the current acquisition planning and presents quality results from a huge data base of more than 400,000 single DEM scenes and 1700 final DEM products.

In synthetic aperture radar (SAR) applications, raw data quantization represents an aspect of primary importance, since the number of bits employed for radar signal digitization on one hand affects the on-board memory consumption and the data volume to be transmitted to the ground, but also on the other hand affects the quality of the SAR images. In this letter, we introduce a novel azimuth-switched quantization technique, which allows the implementation of non-integer quantization rates in a new, efficient way. This grants higher flexibility in terms of performance design and resource allocation, without increasing the complexity and the computational load of the quantization scheme. The presented results were obtained in the frame of the TanDEM-X mission.

TanDEM-X is a spaceborne synthetic aperture radar (SAR) mission, whose goal is the generation of a global digital elevation model with unprecedented accuracy, by using SAR interferometry. One of the main parameters for asserting the quality of interferometric products is the coherence between the monostatic and bistatic images. The objective of this letter is to present the first global mosaics of the interferometric coherence generated from the TanDEM-X quicklook data set, achieving a resolution down to 25 × 25 ma. This is an improvement in terms of details by several orders of magnitude, with respect to the previously implemented techniques for monitoring the global TanDEM-X interferometric coherence. Critical performance areas are separately analyzed, focusing on the developed approach for optimizing the acquisition strategy, in order to achieve the final mission requirement. Moreover, TanDEM-X mosaics of the interferometric coherence show to be a promising starting point for land classification on a large scale. Finally, they represent a valuable input for the whole SAR community, allowing for the recognition of suitable test areas for further scientific purposes.

TanDEM-X is a spaceborne SAR mission with the goal to derive a global Digital Elevation Model (DEM). This paper gives an overview on the acquisition planning and data analysis after completion of two global coverages. The first part summarizes the DEM acquisition strategy including the satellite formation evolution, coverage status, and the planning concept for further interferometric measurements over difficult terrain. In the second part of the paper, the single acquisitions are analyzed for their interferometric quality, such as coherence and relative height errors. After calibration of systematic baseline offsets and instrument internal effects, the monitoring status of absolute DEM height errors is presented, too.

Quantization of SAR raw data represents an aspect of primary importance, since the number of bits used for radar signal digitization on the one hand controls the on-board memory consumption and the data volume to be transmitted on the ground, and on the other hand affects directly the performance of the SAR images. The TanDEM-X mission started in 2010 and comprises the two twin satellites TerraSAR-X and TanDEM-X. Its primary objective is the generation of a worldwide and consistent digital elevation model (DEM) with an unprecedented accuracy. The two satellites fly in close orbit configuration and act as a large single-pass radar interferometer with the adaptability for flexible baseline selection [1]. In this paper, the impact of quantization on bistatic TanDEM-X data is evaluated. First, the effect on the Noise Equivalent Sigma Zero (NESZ) is investigated. Then, the dependence of interferometric coherence on raw data quantization is assessed, and the impact on relative height accuracy is estimated from TanDEM-X repeated acquisitions. A dedicated analysis aimed at evaluating interferometric performance in presence of inhomogeneities in the backscatter response (the so-called low scatterer suppression) is performed. Based on the presented results, the resource allocation strategy for the second global coverage of TanDEM-X has been consequently adapted to further improve the final DEM performance.

TanDEM-X is an interferometric SAR (InSAR) mission acquiring bistatic images with two satellites. Systematic mapping of the Earth's land masses will provide individual interferometric data sets which will be mosaicked and calibrated into a global Digital Elevation Model (DEM). The concept of InSAR and DEM quality monitoring throughout the acquisition and processing phase is presented in this paper.

The TanDEM-X mission comprises two nearly identical satellites: TerraSAR-X and TanDEM-X. The primary objective of the mission is the generation of a worldwide and consistent digital elevation model (DEM) with an unprecedented accuracy. The two satellites fly in a close orbit configuration and act as a large single-pass radar interferometer with the opportunity for flexible baseline selection, allowing the acquisition of highly accurate cross- and along-track interferograms [1]. One of the key parameters in estimating interferometric performance is the coherence γ. Several error sources may contribute to a coherence loss: in this paper, the impact of limited signal-to-noise ratio (SNR), volume decorrelation, and quantization errors are investigated in detail. Furthermore, a novel azimuth-switched quantization (ASQ) technique is introduced and a preliminary performance assessment is presented.