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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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 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.