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Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30∘) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.

Arctic snow cover dynamics exhibit modification in terms of extent and duration due to recent changes in climate, i.e. increasing temperatures and changing precipitation patterns, i.e. winter rain-on-snow events (WROS). Remote sensing methods based on active radar images (SAR) have demonstrated a significant advantage for snow monitoring, (i) capturing physical and dielectric properties, (ii) overcoming the weakness of optical images limited by cloud cover and polar night.The aim of this study is dedicated to the analysis of the spatial and temporal variability of snow cover in the Ny-Ålesund area on the BrØgger peninsula, Svalbard (N 78°55’ / E 11° 55’). In-situ snow measurements from two automated weather stations (Ny-Ålesund, and Bayelva), regular snowpits around the village and in spring on the Austre Loéven, were compared with the spaceborne dataset.The RADARSAT Constellation Mission (RCM) is comprised of three satellites into closely coordinated orbits operating in C-band (5.4 GHz, 5.5 cm). The high temporal (4-day repeat cycle) and spatial resolution of the sensors in Quad-Pol mode (9-m) or Compact-Pol mode (5-m) provide a helpful performance for detecting the spatial variability of snow properties. RCM data are also compared to images of the TerraSAR-X satellite (DLR, Germany) operated in X-band (9.6 GHz, 3.1 cm) at 5-m spatial resolution. Both RCM QP mode and TSX data were acquired with medium incidence angles (33° to 39°) providing better snow penetration for volume backscattering. The RCM CP data were only available under low (23°) and high (53°) beam angles.The following two snow properties were analyzed:WROS detection: the focus was set on the 16-17 March 2022 event (+ 5.5 °C, 62 mm). RCM data at cross-polarization VH or HV can clearly detect the impact of rain on snow, indicating an intensity drop of -10 dB, even on the glacier at high elevation.Snow depth retrieval: the study covers spring 2021 (March-June) and the complete winter season 2021-2022 (November-June).- Concerning QP mode, better correlation between snow depth and SAR backscattering is observed by the cross-pol VH component, retrieving more volume backscattering information than co-pol configuration or total backscattering power (Span). We observe also that descending orbit images (06 :30 AM) provide a better correlation with snow depth than ascending orbit (15 :30 PM) data.- Concerning CP mode and Span (RH+RV), the low incidence images (23°) do not match the snow depth observations due to main surface backscattering, contrariwise the high incidence images correlate better with in-situ observations. The analyses of the Stokes vector elements showed a satisfying correlation for the g3 element and the Relative Phase polarimetric decomposition.Finally, a comparison of Span temporal values between RCM at C-band and TSX at X-band indicates similar time profiles, but clearly lower values of -5 to -10 dB at the C-band.

This article presents a case study for the combined use of TerraSAR-X and time-lapse photography time series in order to monitor seasonal sea ice processes in Nunavik's Deception Bay. This area is at the confluence of land use by local Inuit, ice-breaking transport by the mining industry, and climate change. Indeed, Inuit have reported greater interannual variability in seasonal sea ice conditions, including later freeze-up and earlier breakup. Time series covering 2015 to 2018 were acquired for each data source: TerraSAR-X images were acquired every 11 d, and photographs were acquired hourly during the day. We used the combination of the two time series to document spatiotemporal aspects of freeze-up and breakup processes. We also report new X-band backscattering values over newly formed sea ice types. The TerraSAR-X time series further show potential for melt and pond onset.

Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar—SAR) can address the issue and outperform methods based on passive microwaves. Thus, high spatial resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) quantified the spatio-temporal variability of the geophysical properties of the snowpack in an Arctic catchment, we then (2) studied the links between snow properties and CPD, considering ground vegetation. Snow depth (SD) could be extracted using the CPD when certain conditions are met. A high incidence angle (> 30°) with a high Topographic Wetness Index (TWI) (> 7.0) showed correlation between SD and CPD (R-squared up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.

This paper reviews achievements with C-band SAR in general and specifically Sentinel-1 for Arctic land monitoring, the implications of the current acquisition strategy including the availability of certain polarizations and discusses differences to other wavelengths through the use of Kennaugh parameterization. Unfrozen as well as frozen season observations provide added value. C-VV has the best availability and can be used for a wide range of land cover and terrain change studies. C-HH has been proven applicable for soil characterization but can only be applied regionally. A combination with other wavelengths (demonstrated for X-band) is promising for specifically mapping of wetlands, which are of interest with respect to permafrost applications.

Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications.Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors.The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-Ålesund area on the Br∅gger peninsula, Svalbard (N 78°55’ / E 11° 55’). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36° to 39°) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites.Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K0, K3, K4, and K7 are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles).The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R2 correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions.Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: Φ CPD = Φ VV - Φ HH. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures).Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients.

The Kennaugh framework turned out to be a powerful tool for the preparation of multi-sensor SAR data during the last years. Using intensity-based (an-) isotropic diffusion algorithms like the Multi-scale Multi-looking or the Schmittlets, even robust pre-classification change detection from multi-polarized images is enabled. The only missing point so far, namely the integration of multi-mode SAR data in one image, is accomplished in this article. Furthermore, the Kennaugh decomposition is extended to multi-spectral data as well. Hence, arbitrary Kennaugh elements, be it from SAR or optical images, can be fused. The mathematical description of the most general image fusion is derived and applied to four scenarios. The validation section considers the distribution of mean and gradient in the original and the fused images by the help of scatter plots. The results prove that the fused images adopt the spatial gradient of the input image with a higher geometric resolution and preserve the local mean of the input image with a higher polarimetric and thus also radiometric resolution. Regarding the distribution of the entropy and alpha angle, the fused images are always characterized by a higher variance in the entropy-alpha-plane and therewith, a higher resolution in the polarimetric domain. The proposed algorithm guarantees optimal information integration while ensuring the separation of intensity and polarimetric/spectral information. The Kennaugh framework is ready now to be used for the sharpening of multi-sensor image data in the spatial, radiometric, polarimetric, and even spectral domain.

Die Erstellung einer taglichen aus Satellitendaten abgeleiteten, Wassermaske und deren Visualisierung ruckt in der Wissenschaft immer mehr in den Fokus. In diesem Artikel wird am Beispiel des Forggensees, der als Stausee fur seine starke Wasserflachenanderung bekannt ist, eine einfache und robuste Methode zur Erstellung einer taglichen Wassermaske beschrieben und so der saisonale Wandel der Wasserflachen fur das Jahr 2016 abgeleitet. Der Forggensee wird jedes Jahr im Herbst um ca. 15,5 m abgesenkt und somit auf ein Viertel seiner Flache reduziert, um im Fruhjahr als Hochwasserschutz zu dienen. Fur die Detektion der Wasserflachen werden die kostenlos verfugbaren Radaraufnahmen des Satelliten Sentinel-1 verwendet. Offene und ruhige Wasserflachen konnen auf Grund ihrer geringen Ruckstreuung im Radarbild mit einem einfachen Schwellenwertverfahren detektiert werden. Eine anschliesende Vektorisierung liefert fur alle verfugbaren 57 Radarszenen die jeweilige aktuelle Abgrenzung zwischen Land und Wasser. Fur ein detaillierteres Verstandnis der saisonalen Veranderung des Forggensees wird jedoch eine tagliche Wassermaske benotigt. Die fehlenden Wasserflachen an Tagen, fur die es keine Sentinel-1-Aufnahmen gibt, werden mit Hilfe einer linearen Interpolation rekonstruiert. Ein Poster sowie eine online frei verfugbare Animation visualisieren den saisonalen Wandel des Forggensees.

Inuit have reported greater inter-annual variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in solid precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we monitored sea ice in the area for three seasons of ice between 2015 and 2018. This article presents a case study for the combined use of TerraSAR- X and time-lapse photography time-series in order to monitor snow-covered sea ice seasonal processes. The X-band median backscattering is shown to reproduce the seasonal evolution expected from C-band data. Two different freeze-up and breakup processes are characterized. New X-band backscattering values from newly formed ice types are reported. The monitoring approach presented in this article has the potential to be applied in other remote locations, and processes outlined here may inform our understanding of other fjords or bays where ice-breakers transit.

The digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) still has known long-wave height errors. These errors occur especially in the continent's interior with a spatial scale of hundreds to thousands of kilometers and a magnitude of several meters. In this letter, for the first time, a method for estimating those long-wave height errors with the intention to improve the absolute height accuracy of SRTM C-band DEM globally is presented. The improvement of the SRTM DEM bases on continuously defined spherical harmonics which are able to model the whole sphere. The highly accurate measurements of the Geoscience Laser Altimeter System instrument aboard the Ice, Cloud, and land Elevation Satellite (ICESat) serve as input for the least-squares adjustment which estimates the relevant coefficients of the spherical harmonics. Selection criteria are applied to these data to get ICESat points on flat and nonvegetated areas which account for the most “reliable” points with an accuracy of approximately 1 m. Our results provide a height improvement of 5–6 m along Eurasia and up to ±4 m along North America. The validation with worldwide Global Positioning System tracks proves that the absolute height accuracy of SRTM could generally be improved to better than 3 m.

International audience; Arctic snow cover dynamics offer a changing face in terms of temporal duration andwater equivalent, due to recent climate change conditions (Callaghan et al., 2011; Lemke & Jacobi,2012). Indeed, the Arctic is now experiencing some of the most rapid and severe climate change onearth. In this context, innovative and improved methods are helpful to enhance management of thesnow-pack resource for climate research, hydrology and human activities. The characteristics of Arcticsnow are different from “temperate” snow (i.e. the Alps), in terms of thickness, internal structure, thermalconductivity, and metamorphism. Ground observation often indicates wind slab at the snow surface,internal rounded grains, depth hoar at the bottom, and often internal ice layer or at the interfacewith ground surface (Dominé et al., 2016; Gallet et al., 2017, for spring snow). This work is part of the“Precip-A2” project (OSUG, Grenoble-France), focusing on snow and its interaction with the atmosphere,especially in terms of chemistry, radiative processes and precipitation. The application site isthe Brøgger peninsula, focused on Ny-Ålesund area, Svalbard, Norway (N 78°55’ / E 11° 55’). Onesub-task of the Precip-A2 project is dedicated to X-band radar measurements (ground and spaceborne)to retrieve physical properties of arctic snow.

In recent years, the German Remote Sensing Data Center DFD of the German Aerospace Center DLR has gained a lot of experience in water surface extraction from synthetic aperture radar SAR data for various application domains. In this context, four approaches have been developed, which jointly form the so-called DFD Water Suite: The Water Mask Processor WaMaPro is based on a simple and high-performance algorithm that processes multi-sensor SAR data in order to provide decision-makers with information about the location of water surfaces. The Rapid Mapping of Flooding tool RaMaFlood has been developed for flood extent mapping using an interactive object-based classification algorithm. The TerraSAR-X Flood Service TFS is used for rapid mapping activities and provides satellite-derived information about the extent of floods in order to support emergency management authorities and decision-makers. It is based on a fully automated processing chain. The last approach is the TanDEM-X Water Indication Mask processor TDX WAM. It is part of the processing chain for the generation of the seamless, accurate, and high-resolution global digital elevation model DEM produced based on data of the TanDEM-X mission. Its purpose is to support the subsequent DEM editing process by the generation of a global reference water mask. In this study, the design of the four approaches and their methodological backgrounds are explained in detail, while simultaneously elaborating on the preferred application domains for the different algorithms. The advantages and disadvantages of the four approaches are identified by qualitatively as well as quantitatively evaluating the water masks derived from data of the TanDEM-X mission for five test sites located in Vietnam, China, Germany, Mali, and the Netherlands.

As the number of space-borne SAR sensors increases, a rising number of different SAR acquisition modes is in use, resulting in a higher variation within the image products. This variability in acquisition geometry, radiometry, and last but not least polarimetry raises the need for a consistent SAR image description incorporating all available sensors and acquisition modes. This paper therefore introduces the framework of the Kennaugh elements to comparably represent all kinds of multi-scale, multi-temporal, multi-polarized, multi-frequency, and hence, multi-sensor data in a consistent mathematical framework. Furthermore, a novel noise model is introduced that estimates the significance and thus the (polarimetric) information content of the Kennaugh elements. This facilitates an advanced filtering approach, called multi-scale multi-looking, which is shown to improve the radiometric accuracy while preserving the geometric resolution of SAR images. The proposed methodology is finally demonstrated using sample applications that include TerraSAR-X (X-band), Envisat-ASAR, RADARSAT-2 (C-band) and ALOS-PALSAR (L-band) data as well as the combination of all three frequencies. Thus the suitability of the Kennaugh element framework for practical use in proved for advanced SAR remote sensing.

Conventional studies to assess the annual mass balance for glaciers rely on single point observations in combination with model and interpolation approaches. Just recently, airborne and spaceborne data is used to support such mass balance determinations. Here, we present an approach to map temporal changes of the snow cover in glaciated regions of Tyrol, Austria, using SAR-based satellite data. Two dual-polarized SAR images are acquired on 22 and 24 September 2014. As X and C-band reveal different backscattering properties of snow, both TerraSAR-X and RADARSAT-2 images are analysed and compared to ground truth data. Through application of filter functions and processing steps containing a Kennaugh decomposition, ortho-rectification, radiometric enhancement and normalization, we were able to distinguish between dry and wet parts of the snow surface. The analyses reveal that the wet-snow can be unambiguously classified by applying a threshold of -11 dB. Bare ice at the surface or a dry snowpack does not appear in radar data with such low backscatter values. From the temporal shift of wet-snow, a discrimination of accumulation areas on glaciers is possible for specific observation dates. Such data can reveal a periodic monitoring of glaciers with high spatial coverage independent from weather or glacier conditions.

The TanDEM-X mission will derive a global digital elevation model (DEM) with satellite SAR interferometry. Two radar satellites (TerraSAR-X and TanDEM-X) will map the Earth in a resolution and accuracy with an absolute height error of 10m and a relative height error of 2m for 90% of the data. In order to fulfill the height requirements in general two global coverages are acquired and processed. Besides the final TanDEM-X DEM, an intermediate DEM with reduced accuracy is produced after the first coverage is completed. The last step in the whole workflow for generating the TanDEM-X DEM is the calibration of remaining systematic height errors and the merge of single acquisitions to 1°x1° DEM tiles. In this paper the current status of generating the intermediate DEM and first validation results based on GPS tracks, laser scanning DEMs, SRTM data and ICESat points are shown for different test sites.

Snow cover plays an important role both globally and regionally as it is fundamental for local water availability, river run-off, and groundwater recharge. Hence, the exact knowledge of extent and dynamic of the snow coverage is essential. This study combines synergetic optical and SAR data with the main object to map the snow cover extent. As the Sentinel-Mission provides a wide swath width and a high revisit time (2-3 days at mid-latitudes with same acquisition geometry), the Sentinel-1 Interferometric Wide Swath Mode (IW) SAR data and Sentinel-2 multi-spectral data are used. Additionally, the TanDEM-X DEM is applied for the exact determination of the snow line as well as for snow classification. The mapping of the snow cover extent is applied on the three test sites Devon Island in Canada, Nordenskiöld, Svalbard, and French Alps, France which are characterized by different topography and land cover. The classification achieved an overall accuracy of 85% for Devon Island, 60% for Nordenskiöld and 88 % for the French Alps.

Water scarcity is one of the main challenges posed by the changing climate. Especially in semi-arid regions where water reservoirs are filled during the very short rainy season, but have to store enough water for the extremely long dry season, the intelligent handling of water resources is vital. This study focusses on Lac Bam in Burkina Faso, which is the largest natural lake of the country and of high importance for the local inhabitants for irrigated farming, animal watering, and extraction of water for drinking and sanitation. With respect to the competition for water resources an independent area-wide monitoring system is essential for the acceptance of any decision maker. The following contribution introduces a weather and illumination independent monitoring system for the automated wetland delineation with a high temporal (about two weeks) and a high spatial sampling (about five meters). The similarities of the multispectral and multi-polarized SAR acquisitions by RADARSAT-2 and TerraSAR-X are studied as well as the differences. The results indicate that even basic approaches without pre-classification time series analysis or post-classification filtering are already enough to establish a monitoring system of prime importance for a whole region.

In its first part this paper describes exemplarily optimisations of the satellite systems RapidEye and TerraSAR-X. For this purpose a short insight into processes, relevant for data production, will be given. Focus of this will be time constraints typical for disaster related rapid mapping. Optimisations of geometric pre-processing of satellite data are described in a second part of this paper. For this purpose different software packages available for radar and optical data were compared and analysed respectively. Results were as far as possible optimised.

Wetlands in semi-arid Africa are vital as water resource for local inhabitants and for biodiversity, but they are prone to strong seasonal fluctuations. Lac Bam is the largest natural freshwater lake in Burkina Faso, its water is mixed with patches of floating or flooded vegetation, and very turbid and sediment-rich. These characteristics as well as the usual cloud cover during the rainy season can limit the suitability of optical remote sensing data for monitoring purposes. This study demonstrates the applicability of weather-independent dual-polarimetric Synthetic Aperture Radar (SAR) data for the analysis of spatio-temporal wetland dynamics. A TerraSAR-X repeat-pass time series of dual-co-polarized HH-VV StripMap data—with intervals of 11 days, covering two years (2013–2015) from the rainy to the dry season—was processed to normalized Kennaugh elements and classified mono-temporally and multi-temporally. Land cover time series and seasonal duration maps were generated for the following four classes: open water, flooded/floating vegetation, irrigated cultivation, and land (non-wetland). The added value of dual-polarimetric SAR data is demonstrated by significantly higher multitemporal classification accuracies, where the overall accuracy (88.5%) exceeds the classification accuracy using single-polarimetric SAR intensity data (82.2%). For relevant change classes involving flooded vegetation and irrigated fields dual-polarimetric data (accuracies: 75%–97%) are favored to single-polarimetric data (42%–87%). This study contributes to a better understanding of the dynamics of semi-arid African wetlands in terms of water areas including water with flooded vegetation, and the location and timing of irrigated cultivations.

In September 2013 the production of TanDEM-X digital elevation model (DEM) started. As the data acquisition for difficult terrain lasted until April 2014, final DEM production started for flat to moderate terrain regions where two final coverages surfice. This paper focuses on a first validation of moderate terrain to prove the absolute height accuracy. In a detailed comparison three DEM tiles from different continents are chosen to validate the TanDEM-X DEM by computing differences to GPS tracks, ICESat validation points, and SRTM. On a global scale all TanDEM-X DEMs produced so far are compared with ICESat and GPS tracks. Both validations presented here for the first time indicate that the absolute height error for moderate terrain for TanDEM-X is below 2m and therefore much better than the specified 10m/LE90.

The German SAR interferometry mission TanDEM-X performed on two TerraSAR-X satellites flying in close formation will provide a global Digital Elevation Model (DEM). A by-product is so-called the Water Indication Mask (WAM). The purpose of this supplementary information layer is to support the DEM editing process. Water surfaces usually show lower coherence in an interferometric data set due to temporal de-correlation and low backscattering. Consequently the corresponding elevation values derived from the interferogram are random and produce a virtual relief. This paper introduces the operational water body detection workflow that synergistically evaluates amplitude and coherence information. The presented results of two test sites reveal that the methodology is globally applicable, classifications are highly accurate and the algorithm is appropriate for operational image processing. The water body detection consists of two steps: the Water Body Detection (WBD) derived of one single DEM scene and the mosaicking of multiple WBD to a single Water Indication Mask (WAM). The fusion strategy for the final TanDEM-X WAM considers all WBD acquired at different times in two global coverages and bases on a fusion by union containing the results of the amplitude and the coherence.

The main product of the TanDEM-X mission is an interferometric DEM product that is finally calibrated due to residual systematic offsets and tilts and where different, generally two coverages, are mosaicked. Above this, a water mask is provided to support later editing of rough water areas. In this presentation the commissioning phase work to set the DEM production chain into operation is described and the first commissioning phase products are shown.