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.

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.

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.

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.

The Río de la Plata estuary, located in the eastern coast of South America, has large social, ecological and economical importance for Argentina and Uruguay, in which margins their capital cities (Buenos Aires and Montevideo) and a number of harbours, resorts and industrial centres are located. Being the estuary the main source of drinking water for the millions of inhabitants in the region and a recreational area, the increasing occurrence of cyanobacteria blooms, consistently composed of Microcystis and Dolichospermum complex, is worrying and the need for a monitoring tool like remote sensing is highly desirable. In the present study we evaluated existing multi- and hyper-spectral red/NIR chlorophyll-a (chl-$a$) algorithms using radiometric and bio-optical field measurements. Empirically derived multi-spectral algorithms showed poor results, while hyper-spectral algorithms showed better and promising results. S3B/OLCI and CHRIS-PROBA chl-a maps were generated using fitted models derived using the existing in situ dataset.

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.

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.

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.

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.

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.

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.

TanDEM-X is a spaceborne mission consisting in two satellites that are operated simultaneously for bistatic SAR acquisitions. The main objective of the mission is the systematic acquisition of a global and homogeneous digital elevation model (DEM) in bistatic stripmap mode. The close formation of the satellites makes the system very flexible and allows the commanding of a diversity of challenging experimental modes as bistatic spotlight or alternating bistatic stripmap modes. This paper gives an overview of the TanDEM-X experimental modes, focused on the analysis of already executed scientific orders, giving an overview of the different possibilities in commanding and image acquisition geometries. Also a first image characterization of the modes is included, in terms of image quality assessment and performance compliance.

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 paper presents selected results from the TerraSAR-X Commissioning Phase from instrument performance, SAR system performance and command generation.

TerraSAR-X is the first German Radar satellite for scientific and commercial applications. The project is a publicprivate partnership between DLR and EADS Astrium GmbH. TerraSAR-X consists of a high resolution Synthetic Aperture Radar at X-Band. The radar antenna is based on active phased array technology that allows the control of many different instrument parameters and operational modes (Stripmap, ScanSAR and Spotlight) with various polarizations. Following the TerraSAR-X launch, it is planned a six month Commissioning Phase covering the characterization and verification of the SAR mission. Within this phase, the Overall SAR System Performance takes care of the correct working and interaction of all SAR system elements essential for obtaining an optimum SAR Performance. The paper covers the first in-orbit characterization and verification results of the SAR system performance for TerraSAR-X operational and experimental modes. This characterization is divided into four phases: Initial Characterization, Scene Characterization -both mostly based on basic and experimental products-, and Verification of TerraSAR- X Instrument Command Generation. The different optimization strategies and performance trade-offs are investigated and discussed, including very first TerraSAR-X images. The result of the real SAR data analysis determines the final system baseline and thus the final image quality, e.g. Temperature compensation, Total Zero Doppler Steering, Up/down chirp toggling, transmitted bandwidth, timing interferences, etc.