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.

Diagenetic snow facies represent distinct zones of snow and ice characterized by unique snow physical properties and attributes. These facies serve as indicators of changes in the surface mass balance of ice sheets, making them particularly relevant for monitoring the response of snow and firn cover to climatic changes. In this study, we propose a novel, fully unsupervised deep learning method based on convolutional neural networks (CNNs) to monitor snow zones on ice sheets using a decade of single-pass, bistatic interferometric synthetic aperture radar (InSAR) TanDEM-X data over Greenland. To do so, we develop an innovative iterative training approach to effectively manage a large variety of InSAR acquisition geometries with the goal of optimizing a robust, geometry-invariant model. The proposed approach achieves an average classification accuracy of 93.4% across varying acquisition geometries, segmenting the Greenland ice sheet into five distinct partitions. By analyzing these partitions in terms of elevation, snow density, and cumulative melt data, we link them to classical diagenetic snow facies and uncover trends correlated with climate events over the past decade, estimating a loss of over 454,000 km2 in dry snow extent on the Greenland ice sheet due to the 2012 extreme melt event, with a continuing trend of expanding percolation facies. The proposed method is adaptable to current and future single-pass SAR missions, such as the ESA 10th Earth Explorer Harmony mission, enhancing data robustness against varying acquisition geometries and demonstrating the potential of spaceborne single-pass InSAR missions for long-term monitoring of ice sheet dynamics.

Repeat-pass interferometric SAR (InSAR) is widely used for a variety of application scenarios, such as terrain displacement and subsidence monitoring or measuring the state of infrastructures. In this context, the development of effective algorithms to detect temporal and spatial changes in the radar targets becomes of paramount importance. Typically, state-of-the-art methods only return the spatial, temporal, or both locations of the occurred changes without any information about the causes. In this article, we present a novel change detection method able to infer not only whether a target has changed and when but also the reason why a change is detected, defining the concepts of definitive and temporary changes (TCs). This is done by jointly exploiting four radar amplitude images and the corresponding six interferometric coherences computed at different temporal baselines. To this aim, we propose a new deep learning (DL)-based framework based on a fully convolutional neural network (CNN) called deep analysis for nonstable InSAR targets network (DANI-NET). The network design and training strategy are driven by explainable AI (XAI) principles. Here, we rely on the development of fully synthetic training and testing datasets by following a robust statistical derivation, which allows for a full understanding of the network outcomes. We evaluate the DANI-NET performance on an independent synthetic dataset and we compare it to the state-of-the-art permutational change detection (PCD), a nonparametric statistical approach, achieving extremely competitive results. Moreover, we also provide a feature analysis on the prediction explainability using the SHAP method. Finally, we apply DANI-NET to two real-case scenarios, by considering a Sentinel-1 repeat-pass dataset acquired over Iceland during the 2023–2024 Sundhnúkur eruptions and a TanDEM-X multitemporal stack acquired over an open-pit mining site. We validate the method over the Iceland dataset, where we compare the predicted lava field extension with external reference measurements. In both cases, DANI-NET produces high-quality results and adds the possibility of investigating the nature of the changes caused by either natural or man-induced phenomena.

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.

Deep learning models trained in a fully supervised way have shown encouraging capabilities for mapping forests with TanDEM-X interferometric data, being able to generate time-tagged forest maps at large-scale over tropical forests. These maps have been generated at 50 m resolution to reduce the computation burden. In this work, we now aim to exploit the high-resolution capabilities of the TanDEM-X interferometric dataset, processed at only 6 m resolution. In order to cope with the lack of reliable reference data at such high resolution, we focus on the investigation of self-supervised learning approaches. The availability of a reference map over Pennsylvania, USA, based on Lidar acquisitions at 1 m resolution, allows us to compare different deep learning approaches. First promising results show the possibility to extend the proposed self-supervised learning approach over areas where the lack of reference data prevent us from using fully supervised deep learning methods.

A crucial facet of ice sheet monitoring involves delineating distinct layers within the snowpack, known as snow facies, each characterized by unique physical properties. Variations in melt levels and snow properties across these facies exert influence on the radar wave penetration of spaceborne synthetic aperture radar (SAR) systems. This, in turn, affects the estimation of the radar mean phase center, a critical parameter for generating digital elevation models (DEMs), introducing penetration bias and leading to an underestimation of the surface topographic height. Accurate estimation of this bias is pivotal for reducing uncertainties in determining snow depth, ice thickness, and glacier mass balance through DEM differencing. In this paper, we explore the use of bistatic interferometric SAR features (InSAR) to monitor changes in the snow properties of the Greenland ice sheet (GIS), establishing links between these changes and anticipated variations in the radar penetration bias. We propose to combine machine learning-based models for snow facies segmentation and surface elevation bias estimation to achieve a more accurate estimation of the latter, while unveiling the importance of each feature for minimizing the prediction error. The surface elevation bias is estimated using a random forest baseline model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements acquired during the boreal winter season of 2010/11 in Greenland, achieving a coefficient of determination of R2= 84% and an RMSE of 0.70 m. Furthermore, we show that the derived snow facies are the most important feature for the final prediction.

Highly accurate estimates of canopy height (CH) and above ground biomass (AGB) are key parameters for forest disturbance analysis, resource monitoring, and carbon flux analyses. In this work we present a deep learning-based approach for mapping CH and AGB on country-scales from single-baseline, single-polarization, single-pass TanDEM-X InSAR data. The proposed approach consists in a convolutional neural network (CNN), trained and validated on the five test-sites covered by the 2016 AfriSAR campaign. The resulting performance is in line or better than those of current state-of-the-art approaches. The framework is subsequently deployed on a large-scale to map the entire country of Gabon (in West Central Africa), showcasing the flexibility, scalability, and accuracy of our proposed approach for forest parameter estimation.

The TanDEM-X twin satellites have been acquiring bistatic SAR data for 13 years. The main mission goal was achieved in 2016: the generation of a global DEM of unprecedented homogeneous quality and resolution. For the processing of the second global DEM, the TanDEM-X DEM 2020, a gap-free DEM was required. Therefore, we developed a fully automated edition to fill the gaps corresponding to about 0.1% of the land mass and to flatten the water bodies. External reference DEM datasets were used to fill the gaps. The Reference Elevation Model of Antarctica (REMA) was used for editing the Antarctic continent. Here we found large overlapping gaps within both REMA DEM and the TanDEM-X DEM. We developed a new algorithm to patch these regions in the REMA DEM in order to edit the TanDEM-X DEM with a single editing approach. This method considers the recursive calibration of new InSAR-derived DEMs in a mosaicking process. In this paper we present the fully automatic editing approach used for the released version of the TanDEM-X 30m Edited DEM, the challenges faced and the future activities related to this topic.

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.

In this work, we investigate the challenges of implementing a large-scale rainforest mapping with Sentinel-1 short time series. A frequent and accurate monitoring of these ecosystems is of utmost importance in the context of environmental policy-making. In particular, we propose to combine additional descriptive features with deep learning to mitigate the effect of seasonal components on SAR backscatter and interferometric coherences over a year of acquisitions in the Amazon forest. Preliminary analyses suggest that precipitation patterns might play a key role in how discernible land cover classes are with respect to the radar-based input data. Moreover, our findings show that such effects may vary in different regions of the rainforest, so that different configurations of ancillary features might be necessary to achieve a large-scale model able to generalize in both seasonal and regional dimensions.

Single-pass SAR interferometry (InSAR) has demonstrated a great potential for the monitoring of ice and snow melt dynamics. In particular, digital elevation models (DEM) derived from the TanDEM-X bistatic SAR mission are widely used for measuring elevation changes over glaciers through time-tagged DEM differencing. A critical aspect of this approach is represented by the mutual calibration of the input DEMs, which are normally affected by residual offsets and tilts, caused by uncertainties on the baseline estimation. Moreover, a further crucial aspect which needs to be addressed is the penetration of radar waves into the snow pack, which is closely linked to both the properties of snow and the radar parameters, such as frequency and acquisition geometry. This in turn jeopardizes the retrieval of the topographic height of the surface and adds a significant amount of uncertainty when performing DEM differencing over snow-covered areas. In this paper, we present an overview of the activities which are currently being carried out at DLR, together with partner institutions and companies, aimed at providing more reliable estimations of snow depth and glaciers topographic height changes using TanDEM-X bistatic InSAR data. We present a novel technique for performing an automatic selection of reliable calibration points, based on the use of natural targets, together with the mutual calibration procedure. Moreover, we rely on a data-driven machine learning approach for the estimation and compensation of the surface penetration bias. Preliminary results are extremely promising, also in view of future bistatic SAR missions, such as the ESA Harmony Earth Explorer mission.

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.

Large-scale and up-to-date canopy height model (CHM) estimates are key to forest resources assessment and disturbance analysis. In this work we present an investigation of the potential of Deep Learning (DL) for the regression of forest height from TanDEM-X bistatic InSAR data. We propose a novel fully convolutional neural network (CNN) framework, trained and tested on four tropical sites in Gabon, Africa, together with a series of experiments for assessing the impact of different input features with specific focus on bistatic InSAR. The obtained results are extremely promising and already in line with state-of-the-art methods based on theoretical modelling, with the remarkable advantage of requiring only one single TanDEM-X acquisition at inference time.

Tropical rainforests have been established by the SAR community as well-known calibration sites for the estimation and monitoring of the radar antenna pattern shape in elevation. Here, according to the hypothesis of isotropic scattering, the backscattering coefficient in terms of unit area perpendicular to the antenna, called gamma nought, is assumed to remain constant with respect to the incidence angle. Nevertheless, several studies using X- and C-band sensors have shown a slight dependency of the rainforest backscatter on the incidence angle, as well as on the ground target properties and meteorological conditions. The aim of this work is to present a statistical characterization of radar backscatter at X-band over the equatorial Amazon rainforest using TanDEM-X data, and to provide insights on how to best utilize radar backscatter data in this region for SAR calibration and modelling purposes.

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.

In this work, we investigate the potential of SAR interferometry (InSAR) for mapping forests worldwide and retrieve important biophysical parameters, such as land cover and canopy height. We compare single-pass (bistatic) versus repeat-pass InSAR, discussing their main peculiarities and limitations. In particular, we concentrate on the analysis of the interferometric coherence and on the relationship between volume and temporal decorrelation with respect to forest parameters estimation. We present the work done at DLR for mapping forests worldwide at high spatial resolution using the TanDEM-X bistatic coherence, together with the potential of Sentinel-1 InSAR time-series for a regular monitoring of vegetated areas. We discuss the algorithms which are currently under development based on the latest advances in the field of artificial intelligence and, in particular, of deep learning, presenting the first promising results for a more effective exploitation of current EO datasets.

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 mission acquired a global Digital Elevation Model (DEM) between 2011 and 2014 and generated the final DEM product until 2016. Based on the success and high quality of the first DEM a second complete coverage of the Earth was acquired between 2017 and 2021. This DEM is called “TanDEM-X DEM 2020”. Its processing is based on an edited version of the first global TanDEM-X DEM at 30 m resolution. This edited DEM is required to enable the phase unwrapping and significantly accelerates the DEM processing. The paper in hand describes the automatic global editing process for the edited DEM. It explains the detection and discrimination of DEM gaps and the different methods applied to fill these void areas with external information or derived values. Furthermore, for the editing of water bodies different techniques are described which are used to flatten oceans, lakes and rivers respectively. At last, an overview on the status of the global editing process and further improvements are presented.

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.

The estimation of forest parameters, such as canopy height model (CHM) and above ground biomass (AGB), is of ut-most importance for forest monitoring, carbon-cycle modelling, disturbance analysis, resource inventorying and natural disaster prevention. In this work, we profit from the most recent advancements in deep learning research to propose a convolutional neural network (CNN) architecture for frequent forest parameter estimation at large scale. Our technique consists of a fully convolutional, multi-modal framework, which works on a single set of complementary multi-spectral and interferometric SAR data, acquired by ESA's Sentinel-2 and DLR's TanDEM-X missions, respectively. The regression performance of our framework has been tested over four tropical forest test sites in Gabon, Africa. The estimation of CHM shows promising early results when compared to state-of-the-art methods and has the advantage of requiring only a single input image pair instead of a longer time-series, as commonly done for state-of-the-art model-based techniques.

The Earth is a very dynamic system and the topographic height of its landmass changes over time, especially in forested areas, glaciers, permafrost regions or where human activities take place. After the TanDEM-X mission provided a first global DEM of unprecedented quality in 2016, a new complete coverage of the Earth's landmass was acquired mainly between 2017 and 2020. This data is used to create another global DEM. In addition to providing more up-to-date elevation information, these new acquisitions also provide a great dataset to show the changes that have occurred in the few years between the two global datasets. The new product - the TanDEM-X DEM Change Maps - will be produced in 30m and 90m postings and will focus on showing these changes between the first global TanDEM-X DEM and the newly acquired time-tagged DEM scenes. It will also include the in-house automatically edited TanDEM-X DEM.

Phase unwrapping (PU) is among the most critical tasks in synthetic aperture radar (SAR) interferometry (InSAR). Due to the presence of noise, the interferogram usually presents phase inconsistencies, also called residues, which imply a nonunivocal solution. This work investigates the PU problem from a semantic segmentation perspective by exploiting convolutional neural network (CNN) models. In particular, by exploiting a popular deep-learning architecture, we introduce the interferometric coherence as an input feature and analyze the performance increase against classical methods. For the network training, we generate a variegated data set by introducing a controlled number of phase residues, and considering both synthetic and real InSAR data. Eventually, we compare the proposed method to state-of-the-art algorithms on synthetic and real InSAR data taken from the TanDEM-X mission, obtaining encouraging results.

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.

The TanDEM-X Synthetic Aperture Radar (SAR) system allows for the recording of the bistatic interferometric coherence, which adds additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which influences the interferometric coherence, has been proved to be a reliable indicator of vegetated areas and was exploited in [1] to generate the global TanDEM-X Forest/Non-Forest Map, based on a supervised clustering algorithm. In this work, we investigate ad-hoc training strategies to extent the Convolutional Neural Network (CNN) presented in [2] for mapping forests and monitoring the extend of the Amazonas using TanDEM-X. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the clustering approach, with an f-score increase of 0.13, using as reference a forest map of 2010 based on Landsat data. The improvement in the forest classification makes it possible to skip the weighted mosaicking of overlapping images used in the clustering approach for achieving a good final accuracy. In this way, we were able to generate three time-tagged mosaics over the Amazon rainforest, by utilizing the nominal TanDEM-X acquisitions between 2011 and 2017. In the final paper, we will present more consolidated results, including the validation and comparison of the generated mosaics, as well as change detection investigations, aimed at showing the capabilities of Deep Learning approaches for forest mapping and monitoring with bistatic TanDEM-X images.

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.

Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named Φ-Net, for the joint estimation of the interferometric phase and coherence. Φ-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the Φ-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that Φ-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

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.

The EC/ESA Copernicus program provides a long-term data base that is a unique opportunity for the constant monitoring of the dynamic processes of our Planet. The observation of the forest coverage is of primary importance for the study of the carbon cycle and plays a fundamental role for the management of Earth's natural resources. In this paper we present a strategy to map forested areas by exploiting interferometric Sentinel-1 acquisitions. We observe the evolution in time of the temporal decorrelation of Sentinel-1 stacks and provide a processing and classification framework. We show results over the Amazon rainforest, in particular over the Brazilian Rondonia state, where intensive deforestation phenomena take place.

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.

In this paper, we present a novel approach to monitor the evolution of deforested areas in the Amazon rainforest, by combining Sentinel-1 and TanDEM-X SAR data. The idea is firstly to exploit the large coverage and short revisit time provided by the constellation of Sentinel-1 satellites in order to cover the entire arch of deforestation about ones per month. The goal is here to discriminate forest/non- forest by exploiting C-band backscatter signatures in dual polarization together with the behavior of the interferometric coherence in time, in order to identify the so-called deforestation hotspots: local areas characterized by a significant amount of on-going deforestation activities. Secondly, high-resolution time series of bistatic TanDEM-X data can be acquired over these hot spots with a repeat-cycle of 11 days, and used to track fast changes at small scales, aiming at identifying specific on-going deforestation activities. In the final paper, we intent to present more consolidated results, supported by a large scale acquisition scenario.

Digital elevation models (DEMs) are extensively used for a variety of scientific and commercial applications. For the global TanDEM-X DEM, one of the main performance parameters is the relative height accuracy, which is specified to be under 2 m for flat terrain. Land cover types where the radar signal penetrates into a volume, as forest and ice, are excluded from this specification. Knowing the accuracy of the DEM for a specific land cover type is essential for applications relying on it, such as navigation applications, gradient and aspect estimations, and others. This letter is meant to be as add-on the global relative analysis presented in [1]. Here, we present a characterization of the relative height accuracy based on the interferometric coherence assessing the performance for every class defined by the CCI Land Cover Maps at a continental and global basis. This characterization raises the awareness of the advantages and limitations of the DEM for each specific application and helps scientists using the TanDEM-X DEM to interpret their results. In addition, an estimation of the relative height accuracy for acquisitions where volume decorrelation is present is locally performed using the high-frequency component of repeat-pass DEMs differences. For two particular land cover types, both methods are compared. The main source of errors in the DEM generation is clearly associated with strong inhomogeneous signal returns, becoming a driving factor on the actual accuracy of the estimated mean phase center height.

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.

This paper presents the first results generated with the TanDEM-X mission for the monitoring of the topographical changes caused by the series of earthquakes that hit central Italy between summer and autumn 2016. For the purpose, two 300 km long data takes acquired between the Tyrrhenian and the Adriatic coasts and covering the October, 30, earthquake epicenter location have been considered. The takes have an about 5 years' temporal baseline, thus helpful to reveal the large and small scale terrain changes occurred after the 2016 seismic events.

TanDEM-X is a single-pass radar interferometric mission, which is comprised of two formation flying satellites, with the primary goal of generating a global Digital Elevation Model (DEM) of unprecedented accuracy. Between December 2010 and early 2015 all land surfaces have been acquired at least twice, difficult terrain up to seven or eight times and as of September 2016 the final TanDEM-X DEM dataset is available for download. This paper provides a final quality assessment of the TanDEM-X global DEM products with respect to the DEM relative and absolute height accuracy and data coverage both at the global and geocell level.

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.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation [1]-[4]. With a typical separation between the satellites of 120 to 500 m a global Digital Elevation Model (DEM) with 2 m relative height accuracy at 12 m posting has been generated. While the main mission phase for DEM data acquisition has been finished in 2014, the processing of the global TanDEM-X DEM was concluded in September 2016. Final DEMs are well within specifications and feature an extremely low percentage of void areas. Following the DEM data acquisition the capabilities of this unique mission for new scientific application have been demonstrated. Satellite resources allow for a continuation of the joint TerraSAR-X/TanDEM-X mission for several years. Beyond improvements of the global DEM the mission will be dedicated to the generation of a global 3D information change layer and of the corresponding DEM updates as a demonstration of the future climate research and environmental monitoring mission Tandem-L.

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.

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.

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.

Radar backscatter knowledge represents a key parameter for many remote sensing applications which are based on Synthetic Aperture Radar (SAR) systems. The worldwide, interferometric SAR data set of images acquired within the TanDEM-X mission allows for the characterization of X-band backscatter using a statistical modeling approach on a global scale, having the chance to exploit the unique high quality topographic information associated to it. The input measurements are differently assessed by using a quality-based approach. A series of models can be derived, focusing on the backscatter dependency on polarization, incidence angle, and ground classification. Additional models can be derived depending on the acquisition seasonal time of the considered data. Preliminary results obtained from the X-band radar backscatter modeling approach are presented. The generation of up-to-date backscatter models for X-band will provide a useful data base for the development of a large number of remote sensing applications and for the optimization of future radar systems.

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.

Radar backscatter knowledge is a valuable input for many remote sensing applications, which are based on synthetic aperture radar (SAR) systems. The amount of spaceborne data acquired within the TanDEM-X mission allows for the characterization of X-band backscatter on a global scale. The objective of this paper is to present a method for the characterization of radar backscatter behavior using a global statistical approach. The worldwide data set of images acquired within the TanDEM-X mission is taken into account, having the chance to exploit the unique high-quality topography information associated to it. The input measurements are differently assessed, by using a quality-based approach. A series of models can be derived, focusing on the backscatter dependence on polarization, incidence angle, ground classification, and seasonal time. The developed approach for the algorithm's verification is presented as well, together with some preliminary results obtained from TanDEM-X mission data. The generation of up-to-date backscatter models for X-band will provide a useful database for the development of a large number of scientific applications and for the optimization of future radar systems.

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.

Global radar backscatter data can be used for accurate performance estimation and instrument setting optimization for Synthetic Aperture Radar (SAR) systems, e.g. in the TerraSAR-X and TanDEM-X missions. Both missions offer global remote sensing data in order to characterize X-band backscatter by performing a statistical analysis on SAR image quicklooks. A new approach for the estimation of radar backscatter for any polarization, ground classification type and incidence angle is presented, recurring to the use of SAR data coming from the TanDEM-X mission. The introduction of topographical information on the illuminated ground area allows for the discrimination of backscatter samples which are not affected by shadowing and layover, increasing the reliability of the estimation approach. The analysis technique is presented, leading to the generation of a set of X-band backscatter models. First results, obtained using TanDEM-X SAR data, are introduced.

Global backscatter data can be used for accurate synthetic aperture radar (SAR) performance estimation and optimizing instrument settings for SAR systems, e.g. the TerraSAR-X mission (TSM) and the TanDEM-X mission (TDM). The goal of this work is the generation of X-band backscatter maps by mosaicking images acquired by the TSM. An algorithm that allows the estimation of the on-ground backscatter, for any required polarization and incidence angle from the available data, is implemented. In this paper, the backscatter map generation algorithm is presented, together with the first results, obtained from the TSX-1 data. The validity of the interpolation models is also discussed, as they will form the basis for a future global statistical analysis and modeling of backscatter behavior in X-band SAR data.

The TerraSAR-X satellite features an advanced X-Band SAR based on the active phased array technology which allows flexible operation of Spotlight, Stripmap, and ScanSAR mode for various combinations and elevation angles. It combines the ability to acquire high resolution images for detailed analysis as well as wide swath images for overview applications. The SAR performance of the system is analysed with respect to geometric and radiometric parameters. Long-term monitoring of system parameters like instrument characteristics or SAR image quality confirms the continuous stability of the system. By launching a twin satellite TanDEM-X for global DEM acquisition, the TerraSAR-X mission is now supported by two satellites. The approach presented in the following shows how to keep the SAR performance for both satellites, TerraSAR-X and TanDEM-X.

The goal of this work is the generation of an X-Band backscatter map by assembling images acquired by the TerraSAR-X mission. Global backscatter data is required for accurate performance estimation and instrument commanding inside the TerraSAR-X and TanDEM-X missions. Moreover, many scientific applications can be based on the analysis of backscatter behavior and evolution. The complete ground coverage will be achievable with TanDEM-X mission data. An interpolator, that allows the estimation of the backscatter for any required polarization and incidence angle from the available data, has been implemented. In this paper, the backscatter map generation algorithm will be presented, together with the first obtained results, generated using TerraSAR-X data. Moreover, the validity of the interpolation models will also be discussed, presenting the preliminary results of a statistical analysis of backscatter from TerraSAR-X data.