Combining machine learning (ML) with physical models can significantly impact retrieval algorithms designed to invert geophysical parameters from remote sensing data. Such hybrid models integrate physical knowledge with domain expertise through a joint architecture, potentially enhancing performance by increasing the efficiency and flexibility of the physical model as well as the generalization and interpretability of the ML predictions. This work introduces a hybrid model for estimating forest height using single-baseline, single-polarization TanDEM-X interferometric coherence measurements. In this model, the vertical reflectivity profile is derived as a function of input features, including topographic and acquisition geometry descriptors, using a multilayer perceptron network. This profile is then used to invert forest height by leveraging the established physical relationship connecting the vertical reflectivity profile to forest height. The developed model is applied and validated on several TanDEM-X acquisitions over tropical sites with different acquisition geometries, and its performance is assessed against reference data derived from airborne LiDAR measurements.

Differential interferometry is a powerful remote sensing technique that allows radar systems to capture very small displacements from a far observational position. Most systems are monostatic, meaning that they can only capture displacement changes in the line-of-sight (LOS). The simplicity of monostatic systems also restricts their suitability to either displacements with a single principal motion direction or with a known direction in space. Adding a passive receiver (bistatic systems) provides an additional LOS of information, allows to reconstruct more complex vector fields and reduces the assumptions on the displacement direction. This study presents a set of differential interferometric observations retrieved in Aletsch glacier during a winter campaign in March 2022, using KAPRI, a Ku-Band a ground-based radar interferometer. We investigate the glacier motion based on the datasets corresponding to each of KAPRI’s configurations and propose two independent approaches to solve the displacement field: monostatic (1 LOS) and monostatic-bistatic (2 LOS). As the monostatic and bistatic datasets were retrieved simultaneously, it is possible to do a direct comparison of the resulting displacements and of the respective uncertainties. Results show that the accumulated displacement for the 09:01:30 h to 10:13:30 h at the near-range region (ROI 1) is of 3.83mm in the monostatic case and 4.42mm in the monostatic-bistatic case. In the far-range region (ROI 3) the monostatic displacements result in 8.54mm while according to the bistatic approach 17.82 mm. Assumptions used for the monostatic resolution of the glacier displacements hold for the flatter areas corresponding to the main glacier flow but fail in the steep terrain. Monostatic acquisitions present high SNR which maintains a low uncertainty (below 0.5mm for all ROIs) throughout the scene compared to the bistatic dataset. Monostatic-bistatic derived displacements are susceptible to the increased noise in the far range, but yield a more robust (direction and magnitude) and smoother displacement field in the near-mid range. These comparisons provide valuable insights on the advantages and shortcomings of employing monostatic and bistatic configurations for 3D displacement retrieval and serve as proxy for upcoming satellite missions which plan to use bistatic radar configurations.

This work compares the observed interferometric coherence from high-resolution F-SAR data with the predictions of a physical interferometric repeat-pass soil moisture model over agricultural areas. Differences between wheat fields and bare surfaces are analyzed at C- and L-bands. At C-band, the wheat crops strongly influence the temporal coherence and obscure the signal from the changes in soil moisture. At the L-band, the effect of vegetation is less pronounced, and a high coherence is observed. For bare surfaces, C-band coherence is high in areas with no changes and lower in drying areas, better matching the model predictions. At the L-band, the observed coherence is higher than model predictions.

In this study, we trained a supervised neural network for sea ice classification using X-band SAR images obtained during an Antarctic fieldwork campaign and applied it to Arctic X-band data acquired during sea ice fieldwork campaigns. The results revealed a pronounced correlation between the predicted sea ice class and measured snow depth in the Arctic. This correlation is likely attributed to the Arctic acquisitions taking place during the cold winter conditions characterized by thicker snow layers, which resemble conditions in Antarctica.

The change of glacier surface elevation in Karakoram holds significant relevance to the study of global climate change and regional water resource management. In this work, we focused on understanding glacier dynamics in the Karakoram region through the analysis of Digital Elevation Models (DEMs) generated from a comprehensive 481 TanDEM-X CoSSC products spanning 2011 to 2020. An iterative approach based on the residual phase with respect to the reference DEM was employed to generate high resolution DEMs. The systematic approach offered a robust methodology for extracting precise glacier elevation changes in the Karakoram with high temporal and spatial resolution, provided valuable insights into the understanding of complex glacier dynamics.

In this paper, we explore the utilisation of the complex coherence between HH and VV channels as a tool for crop monitoring. The analysis includes the examination of time series of polarimetric data collected from both airborne and space-borne platforms at L and C band. Results suggest that the L band exhibits a higher sensitivity to crop changes than the C-band in both amplitude and phase of the coherence between the co-polar channels. Moreover, incidence angle is identified as a factor influencing the values of the complex coherence, and hence its sensitivity to crop features.

Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band. Our method addresses the limitations of traditional physical modeling techniques by parameterizing the vertical structure function using a ML model. This approach combines machine learning as input for the physical model. The results demonstrate the improvements in correcting elevation biases, thus increasing the accuracy of X-band InSAR DEMs over Greenland. This advancement has the potential for more precise elevation estimation and ice-sheet monitoring.

The High Mountain Asia (HMA) continues to witness an increased frequency of glacial lake outburst floods (GLOFs), which is likely in response to continued global warming. In situ measurements to understand the triggers all across the region will remain inadequate given the vastness and lack of accessibility of the region. This work explores a data-driven logistic regression-based framework to evaluate potential GLOF triggers, such as the lake dam type, its surface area, aspect, distance, freeboard, slope, precipitation, and temperature. A comprehensive inventory of past GLOF events in the region has been compiled, with 25 events verified using pre-& post-event multispectral images acquired between 2016 and 2022. The logistic regression model is developed using samples of the positive class (lakes with confirmed GLOFs) and of the negative class (potentially dangerous lakes that have not experienced a GLOF event). The samples of the negative lake class were collected with resembling characteristics from the nearby areas of the positive class. We randomly keep 80% of the samples for training. The models performance is assessed using adjusted R2and the Akaike Information Criterion (AIC) on the test samples, which are 79% and 21.5, respectively. The classification accuracy is 90%, which is promising. In short, the proposed method is a useful tool to investigate risk of outburst flooding of glacial lakes.

We present two novel experimental setups for automated measurement of coherent backscatter enhancement (also known as the coherent backscatter opposition effect – CBOE) of Ku-band radio waves in seasonal snow cover. The experimental setups were developed in order to enable fast and repeatable measurements of the effect’s properties, through quickly altering the bistatic baseline of the radar system between zero and eight meters. The experimental setups were tested in December 2023 at the High Altitude Research Station Jungfraujoch in Switzerland, yielding a novel dataset exploring the properties of the early-winter snow cover on top of the Great Aletsch Glacier, as well as allowing a comparison of the benefits and drawbacks of the two experimental setups. We present preliminary results of these experiments, as well as discuss the potential of such measurements for improving our understanding of radio wave scattering in snow.

The amount of water in a snow pack can be described by the snow water equivalent (SWE). SWE is a crucial parameter for hydrological models, for example, for flood predictions. Previous studies have shown that the interferometric phase between two repeat-pass synthetic aperture radar (SAR) measurements can be used to determine the change in SWE. However, a limitation of this method is phase wraps. To overcome this, the copolar phase difference (CPD) between the VV and HH channel can be used, which has been proven to be related to the depth of freshly accumulated snow. This study proposes an approach to incorporate the information on the fresh snow accumulation from the CPD into the interferometric SWE retrieval algorithm. The aim is to detect and correct interferometric phase wraps. The first results using airborne SAR data indicate that including the CPD improves the accuracy of the SWE retrieval.

Spaceborne differential SAR interferometry (D-InSAR) has been demonstrated to potentially allow snow water equivalent (SWE) change measurements for dry snow on a spatial scale, resolution, and accuracy unprecedented by other sensor concepts. However, its operational use is hindered mainly because of: 1) low coherence areas resulting from temporal decorrelation, complicating a robust phase unwrapping and 2) an unknown phase offset due to the$2\pi $ambiguity of the interferometric measurement and therefore a strongly biased SWE change estimate. Furthermore, conventional D-InSAR does not provide a direct measurement of the snow density, which is used in the phase-to-SWE inversion and is an important snow parameter. This article presents strategies to potentially overcome these shortcomings by exploiting simultaneously acquired interferograms with different squint angles. The different lines of sight result in differential phase delays introduced by a SWE change. The phase difference between the interferograms may be exploited to produce a low-resolution SWE estimate without the need for phase unwrapping and to resolve the$2\pi $phase ambiguity of the single interferogram. In addition to that, the ratio between the interferograms is a measure of the dielectric permittivity of the snow and can be related to the snow density. The theoretical performance and functionality of the strategies are analyzed for the planned Harmony mission (ESA’s Earth Explorer 10) based on simulated data, indicating great potential of the approach given the large squint diversity of the Harmony constellation.

The German SAR satellites TerraSAR-X and TanDEM-X are in orbit since 2007 and 2010, respectively. This paper gives an overview about the challenges faced by such long-enduring missions, recent measures taken to preserve the satellites and to continue the mission, and major lessons learned for future SAR missions.

Single-pass interferometric synthetic aperture radar (InSAR) offers the potential to generate sea ice digital elevation models (DEMs) despite the inherent dynamics of sea ice. However, the accuracy of sea ice DEM generation from InSAR is hindered by the varying penetration bias across different types of ice. This paper presents a new scheme to generate sea ice DEMs (i.e., snow freeboard) across different ice types from TanDEM-X images. The proposed scheme is applied to generate a sea ice topographic map over a 630 km × 19 km area, revealing a spatial pattern of sea ice elevation. The highest elevations are observed near the Antarctica Peninsula (AP), with a rapid decrease in elevation within approximately 200km from the AP coastline, followed by a gradual increase towards the south.

Mapping seasonal wet snow (WS) is essential for climate research, hydrological assessments and natural hazards management. It is especially crucial in Karakoram as the the water supply in the region is dominated by snow melting. However, the complex terrain in Karakoram brings great challenge when employing synthetic aperture radar (SAR) data for WS mapping. In this work, we present an algorithm that combines SAR and topographic data for WS mapping. The result shows that the proposed algorithm can efficiently reduce the uncertainty and generate robust WS map on a large scale.

We present the results of recent observations of terrestrial snow cover with KAPRI, a Ku-band polarimetric-interferometric radar system with bistatic capabilities. The bistatic configuration allows observation of phenomena which are not observable in the monostatic regime. Observations of polarization phase differences of snow cover on top of the Great Aletsch Glacier revealed that the behaviour of these parameters markedly varies between different times of year, as well as between the monostatic and bistatic acquisition modes. The bistatic capabilities of KAPRI were also recently used to characterize the coherent backscatter opposition effect (CBOE) in seasonal snow. We discuss the potential of such bistatic observations to observe quantities which are unobservable in the monostatic regime, and which could serve as new inputs for snow parameter retrieval methods.

Synthetic aperture radar (SAR) is an advanced measurement technique, which is often the subject of discussions between remote sensing experts, as well as other specialists and members of the general public. These discussions can however be hindered by misunderstandings caused by the rather complex principles of SAR acquisitions. We present the implementation of SAR2, an augmented-reality app for the Microsoft HoloLens, which aims to enhance understanding of synthetic aperture radar through visualization of the SAR acquisition procedure. Its distinguishing feature is real-time simulation of the radar echo, combined with real-life SAR processing algorithms, which ensure verisimilitude and flexibility of the visualization. The virtual environment allows users to directly interact with the satellite and the observed scene, modify acquisition parameters, and visualize data flows during a simulated SAR acquisition. The ultimate aim of development of this app is to serve as an educational outreach tool for experts, educators, and the public alike.

Tensor decompositions are a powerful tool for multidimensional data analysis, interpretation, and signal processing. This work develops a constrained tensor decomposition framework for complex multidimensional Synthetic Aperture Radar (SAR) data. The framework generalizes the Canonical Polyadic (CP) decomposition by formulating it as an optimization problem and allows precise control over the shape and properties of the output factors. The implementation supports complex tensors, automatic differentiation, different loss functions, and optimizers. We discuss the importance of constraints for physical validity, interpretability, and uniqueness of the decomposition results. To illustrate the framework, we formulate a polarimetric time series decomposition and apply it to data acquired over agricultural areas to analyze the development of four crop types at X, C, and L bands over the period of twelve weeks. The obtained temporal factors describe the changes in the crops in a compact way and show a correlation to certain crop parameters. We extend the existing polarimetric time series change analysis with the decomposition to show the changes in more detail and provide an interpretation through the polarimetric factors. The decomposition framework is extensible and promising for joint information extraction from multidimensional SAR data.

ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.

The Snow Water Equivalent (SWE) describes the amount of liquid water stored in a snow pack and is an important parameter for runoff predictions and flood forecasts. Differential Interferometric Synthetic Aperture Radar (DInSAR) can be used to estimate the SWE change between two temporally separated repeat pass SAR acquisitions utilizing the interferometric phase. However, only a limited range of SWE changes can be retrieved unambiguously due to phase wraps of the interferometric phase. In this study, the aim is to include information on snow depth obtained from the Co-polar Phase Difference (CPD) between the polarimetric channels to detect phase wraps and improve the SWE retrieval results. The investigations are performed using airborne SAR acquisitions over the Alps.

Among the most rapid and dramatic changes related to Arctic permafrost thaw are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making their change visible by comparing digital elevation models over time or by identifying the induced vegetation changes in time-series of optical satellite images. In this study we use digital elevation models generated from TanDEM-X observations to detect and derive volume and area change rates for RTSs on the Taymyr Peninsula. The available data takes allows to compare two time periods: from 2010/11/12 to 2016/17 and from 2017/18 to 2020/21. From 2016 onwards optical Sentinel-2 observations are available for which we manually map thaw slumps for each year in a small sub-area and compare the results to the TanDEM-X mapped RTSs. We found a strong, non-linear increase in the second time-period of the TanDEM-X period and by using the optical mapped RTSs we could attribute this increase to the Siberian heatwave in 2020.

Polarimetric Synthetic Aperture Radar (SAR) data has been extensively used to estimate soil permittivity because of its high sensitivity to the dielectric properties of the target. However, the presence of vegetation cover induces bias in the permittivity estimates. This work utilizes the scattering-type parameters: alpha$(\overline{\alpha})$and theta$(\theta_{\text{FP}})$to estimate soil permittivity using the X-Bragg as the dominant surface scattering model. A theoretical study ascertains that the recently proposed$\theta_{\text{FP}}$is fairly robust towards the depolarizing component in the X-Bragg model. Hence, it is expected to produce better inversion accuracy. This study analyzes major phenology stages of Canola using the UAVSAR full-pol SAR data and the ground measurements acquired during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method achieved an RMSE of 5.9 for soil permittivity with a Pearson coefficient,$r=0.83$. Further, the temporal trend of the soil permittivity estimates also agrees with in-situ measurements for the entire timeframe.

The paper provides an up-to-date overview of the German TanDEM-X satellite mission status and its ongoing science activities. The global digital elevation model (DEM) of TanDEM-X became available in 2016 and surpassed all expectations: It has 99.9% coverage, 12-m posting, absolute height accuracy of approximately 1 m and a relative height error (standard deviation) of 0.8 m. This unique data set has been available since then for commercial and scientific applications. In addition, a low-resolution version of the global digital elevation model with 90-m posting has been released for free download for scientific applications. Since 2017 new interferometric acquisitions have been started with the goal to generate a global change layer which will register all the changes of the Earth topography occurred since the first acquisitions of TanDEM-X. The acquisition for the global change layer was completed by mid-2020. Since then a new science phase of TanDEM-X with a focus on interferometric acquisitions over forest, ice sheets and permafrost regions are ongoing.

Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the dynamics of sea ice. Co-polarimetric phase differences (CPD) provide scattering information concerning the sea ice properties. This study estimates the observational CPD from the dual-polarimetric SAR images over young ice (YI) and undeformed ice (UI) in the Western Weddell Sea. The CPD is further modelled as a function of structural anisotropy, incidence angle, ice volume fracture, and volume thickness. The good agreement between the observational and modelled CPD indicates that CPD can be a promising indication for topographic characterization over the YI and UI.

The Ku-band provides opportunities for investigations of snow morphology through radar observations, since it exhibits a relatively high amount of scattering even from snow layers of limited depth, while maintaining low absorption. Due to technological and practical challenges, the bistatic parameter space of Ku-band radar observations of natural media such as snow, has been relatively unexplored. We present radar measurements of snow cover obtained with KAPRI, a bistatic polarimetric Ku-band radar system. In August 2021 and March 2022, we carried out time series observations of the Aletsch glacier in the Swiss Alps, acquiring a fully-polarimetric interferometric time series of both monostatic and simultaneous bistatic observations of the glacier's accumulation zone. This dataset will serve as a test-bed to investigate new snow parameter inversion methods based on bistatic Ku-band radar data. The bistatic polarimetric measurement configuration, as well as preliminary results of the analysis of radar backscatter, are presented.

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θFPand θCP, respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θFPand θCP, unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient,r≥ 0.62.

This article presents the bistatic operation mode and the performance analysis of KAPRI, a terrestrial frequency-modulated continuous-wave (FMCW) Ku-band polarimetric radar interferometer capable of acquiring bistatic full-polarimetric datasets with high spatial and temporal resolution. In the bistatic configuration, the system is composed of two independently operating KAPRI devices, one serving as a primary transmitter and receiver and the other as a secondary receiver. The secondary bistatic dataset is affected by possible offsets between the two devices’ reference clocks, as well as distortions arising from the bistatic geometry. To correct for this, we present a two-chirp bistatic FMCW signal model, which accounts for the distortions, and a reference chirp transmission procedure, which allows correcting the clock offsets in the deramped signal time domain. The second challenge of operation of a bistatic polarimetric system is polarimetric calibration since it is not possible to employ purely monostatic targets such as corner reflectors. For this purpose, we developed a novel active calibration device Variable-Signature Polarimetric Active Radar Calibrator (VSPARC), designed for monostatic and bistatic calibration of all polarimetric channels. VSPARC and its associated novel polarimetric calibration method were then used to achieve full calibration of both KAPRI devices with polarimetric phase calibration accuracy of 20° and 30-dB polarization purity in field conditions. This article thus presents a complete measurement configuration and data processing pipeline necessary for synchronization, coregistration, and polarimetric calibration of bistatic and monostatic datasets acquired by a real-aperture FMCW radar.

This paper analyzes the potential of the joint exploitation of the Spanish PAZ satellite and the two TerraSAR-X/TanDEM-X German satellites for interferometry. Since both platforms are almost identical and they were launched on the same orbital plane, similar images under the same geometry may be acquired by both missions. Several time series over different test sites with distinct land cover types in Germany and Spain that have been acquired by both missions are compared in order to evaluate the possibility of joint exploitation for interferometry. Results show no loss of performance on the in-terferograms obtained while combining satellites of both missions. This allows to effectively reduce the minimum revisit time from 11 days to 4 or 7 days.

The paper provides an up to date overview of the German TanDEM-X satellite mission status and its ongoing science activities. In June 2020 TanDEM-X reached his 10 years of successful operation - a view forward will be presented in the final paper. The global digital elevation model (DEM) of TanDEM-X became available in 2016 and surpassed all expectations: It has 99.9% coverage, 12-m posting, absolute height accuracy of approximately 1 m and a relative height error (standard deviation) of 0.8 m. This unique data set has been available since then for commercial and scientific applications. Last year, a low-resolution version of the global digital elevation model with 90-m posting has been released for free download for scientific applications. Since 2017 new interferometric acquisitions have been started with the goal to generate a global change layer which will register all the changes of the Earth topography occurred since the first acquisitions of TanDEM-X. The data collection for the global change layer will be ready by the end of 2020. Currently, a new science phase of TanDEM-X is running with a focus on interferometric acquisitions over forest, ice sheets and permafrost regions.

South Rimo Glacier, one of the largest glaciers in Karakoram, showed a glacier surge event during 2018–2020. To characterize the dynamics of the surge, we assessed surface height changes by differencing a time-series of DEMs produced from high-resolution TanDEM-X radar data. In addition, we evaluated the evolution of surface velocities with offset tracking using Sentinel-1 imagery. DEM differences show that the glacier started gaining height in the middle stream since 2013 where a bulge built-up and reached a maximum observed height of 28 m in 2017. After surge activation in 2017, the velocity increased by 2–3 order of magnitude with a peak velocity of 11 m/d in August 2019. Our work highlighted the value of high resolution DEM products and velocity maps in pre-identifying glacier surge and mitigating related hazards.

Active microwave sensors, such as synthetic aperture radars (SARs), offer all-weather and daylight independent operability which is of great advantage for monitoring polar regions, where extreme environmental conditions and long period of darkness strongly limit other kinds of sensors. In addition, microwaves allow penetrating into dry snow and ice, making SAR measurements sensitive to the subsurface structure of glaciers and ice sheets. However, the retrieval of glacier subsurface parameters from SAR observations remains difficult due to their sensitivity to a large number of factors, including snow and ice properties, presence of layers, etc. The objective of this study is to attempt advancing the understanding of SAR measurements of glaciers and ice sheets. A combined analysis of polarimetric and tomographic measurements is carried out to derive a 3-D characterization of the scattering mechanisms occurring in a glacier subsurface scenario. The investigation exploits a fully-polarimetric tomographic airborne dataset, acquired over Greenland by the DLR's F-SAR system in the frame of the ARCTIC15 campaign.

Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to rapid thaw in a warming climate. When ice-rich permafrost soil thaws it becomes unstable, leading to changes in the topography. The most rapid and dramatic changes occur due to retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time. Fort his study we used digital elevation models derived from bistatic single-pass radar observation from the TanDEM-X satellite pair over the time span from 2011 to 2017. Here we present area and volumetric change rates for thaw slumps from two study areas in Northern Canada (Mackenzie River Delta uplands and Banks Island). The RTSs in the two study area show large differences in terms of RTS size and growth rates. The computation of typical terrain controls like aspect, slope and RTS location (hillslope, shoreline) did not correlate with RTS activity, suggesting that other factors especially soil properties (e.g. ground ice content) play a larger role for RTS evolution.

Plastics within the ocean have been found to be colonised by microorganisms that, as a by-product of their metabolism, produce surfactants. Short capillary waves on the sea surface can get dampened due to the increased surface elasticity of these surfactants. Radar satellites are sensitive to surface roughness and can therefore detect the dampening of these waves. This research investigates areas inside the Atlantic, Pacific and Indian Ocean gyres using ESA Sentinel-1 and DLR TerraSAR-X data. We found out that we can observe several surfactant instances in the gyres and these are not correlated to medium or high level of chlorophyll. We can exclude that they have origin in biogenic slicks. Among other possible unknown origins, we hypothesise that these surfactants are produced from plastic concentrations within the ocean.

In this submission the bistatic operation mode of KAPRI, a polarimetric-interferometric, ground-based, real-aperture, Ku-band, FMCW radar is presented. Its possible configurations and the synchronization procedures needed to achieve distortion-free single-look complex bistatic images are described. The challenges of polarimetric calibration in the bistatic regime are outlined, and a novel active calibration device and the associated polarimetric calibration method which are used for bistatic calibration of KAPRI are presented. First results of observations of natural environments (vegetation, snow) using bistatic KAPRI are shown.

The paper provides an up to date overview of the German TanDEM-X satellite mission status and its ongoing science activities. In June 2020 TanDEM-X will reach his 10 years of successful operation - a view back and forward will be presented in the final paper. The global digital elevation model (DEM) of TanDEM-X became available in 2016 and surpassed all expectations: It has 99.9% coverage, 12-m posting, absolute height accuracy of approximately 1 m and a relative height error (standard deviation) of 0.8 m. This unique data set has been available since then for commercial and scientific applications. Last year, a low-resolution version of the global digital elevation model with 90-m posting has been released for free download for scientific applications. Since 2017 new interferometric acquisitions have been started with the goal to generate a global change layer which will register all the changes of the Earth topography occurred since the first acquisitions of TanDEM-X. The data collection for the global change layer will be ready by the end of 2020. By that time, it is planned to start a new science phase of TanDEM-X with a focus on interferometric acquisitions over forest, ice sheets and permafrost regions.

Interferometric synthetic aperture radar (InSAR) is an effective technique for sea-ice topographic retrieval. However, the penetration of electromagnetic waves into snow-covered thick ice limits the accuracy of InSAR-derived sea-ice topography. In this study, the data were acquired over the sea ice in the western Weddell Sea, Antarctica. A new method is proposed by merging co-polarization coherence into InSAR processing to measure the sea-ice surface topography.

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking.

In the context of differential synthetic aperture radar interferometry (DInSAR), precise trajectory estimation of the SAR platform is necessary to minimize residual phase errors induced by inaccurate knowledge of the 3D acquisition geometry. Inertial navigation systems (INS) and global navigation satellite system (GNSS) are usually employed to track the position of the platform. However, their unavoidable inaccuracies lead to motion estimation errors that negatively affect the quality of the processed radar data. To assess the positioning performance in a repeat-pass scenario, we used a navigation-grade INS/GNSS system to precisely track the position and the attitude of a platform moving along a rail and carrying a SAR sensor. We analyse the performance of the positioning solution for different scenarios relevant to repeat-pass DInSAR. Since the position of the platform is nearly perfectly repeated at every pass (zero interferometric baseline), the precision of the estimated position can be assessed and the interferometric performance evaluated.

Digital elevation models generated with SAR interferometry (InSAR) are an important information source for glacier and ice sheet mass balance. However, the measured elevations suffer from a penetration bias due to the interferometric phase center being up to several tens of meters below the surface. The penetration of the microwave signals depends on SAR parameters (e.g. frequency) and snow and ice conditions. There is potential to estimate this penetration bias directly from the data by means of polarimetric InSAR models. Existing models fail to describe the data across different test sites and ice conditions and phase centers were found to be deeper than predicted by these models. SAR tomography is employed to assess the vertical distribution of backscattering in the data from an airborne campaign. The data are compared to refined models in order to find better representations of the vertical backscattering distribution, while the model complexity is purposely kept simple to make a phase center estimation possible. Additionally, recent work showed the importance of strong subsurface layers which influence phase center depth. Combining the refined subsurface structure models and dominant subsurface layers allows simulating a variety of ice sheet subsurface scenarios and can be used to assess the potential to estimate the InSAR phase center depth directly from the data.

The paper provides an up to date overview of the German TanDEM-X satellite mission status and its ongoing science activities. The global digital elevation model (DEM) of TanDEM-X became available in 2016 and surpassed all expectations: It has 99.9% coverage, 12-m posting, absolute height accuracy of approximately 1 m and a relative height error (standard deviation) of 0.8 m. This unique data set has been available since then for commercial and scientific applications. Last year, a low-resolution version of the global digital elevation model with 90-m posting has been released for free download for scientific applications. Since 2017 new interferometric acquisitions have been started with the goal to generate a global change layer which will register all the changes of the Earth topography occurred since the first acquisitions of TanDEM-X. The global change layer will be available by 2020. By that time, it is planned to start a new science phase of TanDEM-X with a focus on interferometric acquisitions over forest, ice sheets and permafrost regions.

Steep terrain presents a challenge in soil moisture estimation. In this paper, we illustrate the necessity of polarization orientation angle compensation for soil moisture estimation based on Bragg scattering. We also emphasize the importance of local incidence angle for soil moisture estimation. We found that azimuth slopes cause underestimation, but range slopes can cause over- or underestimated soil moisture depending on range slopes being positive or negative. Theoretically, if both azimuth and range slope measurements are available, soil moisture level can be retrieved precisely. However, to achieve this, a coregistered DEM is indispensable.

In February 2018, we identified an unnamed glacier in the West Kunlun Shan mountains which showed crevasses extremely similar to the detachment patterns of two glaciers on the Western Tibetan Plateau near lake Aru Co before their catastrophic collapse. Here we present an analysis of this unnamed glacier addressing its similarity to the Aru glaciers, its potential to cause a massive ice avalanche, and the potential run-out distance. Similar to the Aru glaciers, we show that the glacier has a polythermal regime and that soft-bed sediments favor a collapse. Digital elevation data from 2000 to 2014 and velocity estimates reveal glacier dynamics very similar to the Aru glaciers. However, ERA-Interim climate data indicate only a limited liquid water input, which might be insufficient for a complete detachment from the glacier bed. Additionally, a broad glacier tongue might stabilize the glacier.

The need of large scale observations with high temporal frequency has promoted airborne and satellite remote sensing techniques for glaciological applications. In particular, active microwave sensors, such as synthetic aperture radars (SARs), offer all-weather and daylight independent operability which is of great advantage at high latitudes, where extreme environmental conditions and long period of darkness strongly limit other kinds of sensors. Moreover, longer wavelengths allow to penetrate significantly into dry snow and ice, interacting with surface as well as subsurface features. On the one hand, this makes SAR measurements suitable to investigate the subsurface structure of glaciers and ice sheets. On the other hand, the complex interaction of microwaves with the subsurface layers makes the interpretation of SAR measurements challenging. This study investigates the potential of SAR techniques to retrieve information about glacier subsurface. SAR polarimetry and tomography are used to gain a 3-D characterization of the scattering scenario of the K-transect, a site located in the ablation zone of Greenland. For this, a fully-polarimetric tomographic airborne dataset, acquired by the DLR's F-SAR system in the frame of the ARCTIC15 campaign, is exploited.

Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to rapid thaw in a warming climate. When ice-rich permafrost soil thaws it becomes unstable, leading to changes in the topography. The most rapid and dramatic changes occur due to retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time. Here we present first results of a retrogressive thaw slump detection method using bistatic single-pass radar observation from the TanDEM-X satellite pair over the time span from 2011 to 2017. Our processing chain include the digital elevation generation process, elevation model differencing, water body mask generation and a method for the detection of significant height changes. We detected 157 active RTS validated by high- to medium-resolution optical data in our study region (3700 km2) located in the Mackenzie River Delta, Northwest Territories, Canada.

This paper reports the first results of a comparative study of tropospheric delays retrieved by means of PSI processing of an interferometric stack of SAR images against those derived independently from a permanent GNSS network. The stack comprises 33 Cosmo-SkyMed stripmap images acquired in the summers between 2008-13 over the Matter Valley in the Swiss Alps. The long-term objective of the study is to explore whether GNSS-derived delays from existing networks (i.e., not deployed specifically for a test site) in Swiss Alpine regions can aid in tropospheric phase corrections in SAR data, or rather the phase corrections derived within the PSI processing being at a higher spatial resolution might be appropriate to build upon the GNSS products by improving their resolution.

The analysis of data from an airborne synthetic aperture radar (SAR) campaign in the percolation zone of Greenland revealed an interferometric coherence undulation behavior with respect to vertical wavenumber, which cannot be explained with existing models. We propose a model extension that accounts for scattering from distinct layers below the surface. Simulations show that the periodicity of the coherence undulation is mainly driven by the vertical distance between dominant subsurface layers, while the amplitude of the undulation is determined by the ratio between scattering from distinct layers and scattering from the firn volume. We use the model to interpret quad-pol SAR data at X-, C-, S-, L-, and P-bands. The inferred layer depths match layer detections in ground-based radar data and in situ measurements. We conclude that in the percolation zone, scattering from subsurface layers has to be taken into account to correctly interpret SAR data and demonstrate the potential to retrieve geophysical information about the vertical subsurface structure.

Synthetic aperture radar (SAR) tomography with repeat-pass acquisitions generally requires a priori phase calibration of the interferometric data stack by compensating for the atmosphere-induced phase delay variations. These variations act as a disturbance in tomographic focusing. In mountainous regions, the mitigation of these disturbances is particularly challenging due to strong spatial variations of the local atmospheric conditions and propagation paths through the troposphere. In this paper, we assess a data-driven approach to estimate these phase variations under a regression-kriging framework. The vertical stratification of the troposphere is modeled functionally, while the impact of the spatial turbulence is considered in a stochastic sense. The methodology entails an initial persistent scatterer interferometry (PSI) analysis. The atmospheric phases isolated for the persistent scatterers (PS) within the PSI processing are considered as samples of the 3-D distribution of the phase delay variations over the scene. These atmospheric phases are regressed against the spatial coordinates in map geometry at PS locations. In turn, kriging predictions are obtained at each location along the elevation profile, where tomographic focusing is intended. A key point of this approach is that the requisite atmospheric corrections are incorporated within the tomographic focusing model. A case study has been performed on a data stack comprising 32 COSMO-SkyMed stripmap images acquired over the Matter Valley in the Swiss Alps, in the summers of 2008-2013. The results show locally improved deformation sampling with tomographic methods compared to the initial PSI solution, primarily due to the improved phase calibration. In general, this paper underscores the indispensability of height-dependent correction of atmospheric phases for SAR tomography.

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A geostatistical analysis of the atmospheric phase screen (APS) affecting Ku-Band terrestrial radar interferometric (TRI) observations of a fast-flowing alpine glacier is made assuming a separable spatio-temporal covariance structure. Such a structure facilitates the mitigation of APS: the atmospheric phase affecting individual interferograms can be extrapolated form a set of persistent scatterers (PS) using regression-Kriging. After removing this estimate the residual APS is only correlated in time; its effect on surface displacement estimation is mitigated with a generalized least squares (GLS) inversion employing an estimate of the temporal covariance of the APS. The applicability of a separable covariance structure and the performance of the APS correction method are assessed on a TRI timeseries of Bisgletscher, a glacier in the southwestern Swiss Alps.

Repeat-pass differential SAR interferometry (DInSAR) using spaceborne SAR data or stationary terrestrial radar data is an established technique to measure surface displacements. However, repeat-pass DInSAR from agile platforms (airborne/car-borne) is challenging due to residual motion errors. This is particularly true for high-frequency radar where motion errors of few millimeters represent a non-negligible fraction of the wavelength. In this paper, an experimental car-borne SAR system is presented. Such a system is complementary to the existing solutions (namely spaceborne, airborne, and terrestrial systems) in terms of geometry of acquisition, and flexibility in the selection of temporal baselines and location of the acquisitions. To meet the need of consistent and precise trajectory information, proper postprocessing procedure must be applied to the raw positioning data collected from the inertial navigation system (INS) and the global positioning system (GNSS). A viable procedure is here presented and first results discussed.

Plastic pollution in world oceans is estimated to have reached 270.000 tones, or 5.25 trillion pieces. This plastic is now ubiquitous, however due to ocean circulation patterns, it accumulates in the ocean gyres, creating “garbage patches”. This plastic debris is colonized by microorganisms which can create unique surfactants and bio-film ecosystems. Microbial colonization is the first step towards disintegration and degradation of plastic materials: a process that releases metabolic by-products from energy synthesis. These byproducts include the release of short-chain and more complex carbon molecules in the form of surfactants, which we hypothesize will affect the fluid dynamic properties of waves (change in viscosity and surface tension) and make them detectable by the SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of the North Pacific and North Atlantic oceans, close to the ocean gyres and away from the coastal interference. Together with SAR processing we conducted contextual image analysis, using ocean geophysical products of the sea surface temperature, surface wind, chlorophyll, wave heights and wave spectrum of the ocean surface. In addition, we started lab experiments under controlled conditions to test the behaviour of microbes colonizing the two most common marine pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of the SAR images had shown that a combination of surface wind speed and Langmuir cells- ocean circulation pattern is the main controlling factor in creating the distinct appearance of the surfactants, sea-slicks and microbial bio-films. The preliminary conclusion of our study is that SAR remote sensing may be able to detect plastic pollution in the open oceans and this method can be extended to other areas.

Terrestrial radar systems are used operationally for area-wide measurement and monitoring of surface displacements on steep slopes, as prevalent in mountainous areas or also in open pit mines. One limitation of these terrestrial systems is the decreasing cross-range resolution with increasing distance of observation due to the limited antenna size of the real aperture radar or the limited synthetic aperture of the quasi-stationary SAR systems. Recently, we have conducted a first experiment using a car-borne SAR system at Ku-band, demonstrating the time-domain back-projection (TDBP) focusing capability for the FMCW case and single-pass interferometric capability of our experimental Ku-band car-borne SAR system. The cross-range spatial resolution provided by such a car-based SAR system is potentially independent from the distance of observation, given that an adequate sensor trajectory can be built. In this paper, we give (1) an overview of the updated system hardware (radar setup and high-precision combined INS/GNSS positioning and attitude determination), and (2) present SAR imagery obtained with the updated prototype Ku-band car-borne SAR system.

In this overview the science activities of the TanDEM-X satellite mission are presented and an update on the proposal submission is given.

In this paper, polarimetric multi-baseline (MB) Synthetic Aperture Radar (SAR) Interferometry data are used to estimate the polarimetric ground component under vegetation. However, the solution of the applied separation algorithm is not unique and depends on the constraints in the regularization. First, the effect of this non-uniqueness is analyzed and then exploited to isolate a ground component with minimized influence of depolarizing scattering mechanisms. Using experimental MB SAR data acquired by DLR's airborne sensor F-SAR, the polarimetric entropy and mean alpha angle of the isolated ground component are compared to the original polarimetry of the full image. Finally, the ground polarimetry is interpreted for changing soil moisture vegetation conditions in corn. To this purpose, three dates are compared characterized by 1) a change in soil moisture, 2) a change in vegetation cover or 3) a simultaneous change of soil moisture and vegetation cover.

Single pass radar interferometry (sp-InSAR) is a well established technique for generation of digital elevation models (DEM). Differencing two DEMs acquired at different times can reveal topographic changes. However snow depth estimation by DEM differencing is still an ongoing topic in radar research: in contrast to snow free surfaces, the snow surface elevation is difficult to detect either because of microwave penetration into dry snow or because of the weak backscatter return from wet snow which significantly decorrelates the interferometric signal. In this study we demonstrate first results of wet snow depth estimation by differencing sp-InSAR DEMs acquired by the TanDEM-X satellite mission. The results show, in contrast to dry snow, a clear sensitivity to wet snow. However, additionally to a high vertical sensitivity of a few ten centimeters a very low noise-equivalent-sigma-zero (NESZ) is crucial for successful snow depth estimation.

Grosser Aletschgletscher, the largest glacier in the European Alps, contains 20% of the entire Swiss ice mass. However, mass balance simulations based on current climate models predict 90% mass loss by 2100. The glacier is a super-testsite for the TanDEM-X satellite mission and data series starting from 2011 are available. In this study we analyzed almost 100 digital elevation models computed from bistatic radar in-terferograms and compare the derived height loss with model predictions. Additionally, we used patch-based offset tracking to determine a new velocity map of the complete glacier. The results show that the current height loss of 3.3 meters per year is close to that mass balance simulation which uses the ensemble median of common European climate models which predict, relative to 1999, a warming of 4.3°C by 2100.

Tandem-L is a proposal for an innovative L-band SAR mission for the systematic observation of dynamic processes on the Earth's surface. The mission concept is based on two SAR satellites flying in close formation featuring latest digital beamforming techniques in combination with a large deployable reflector for increasing the swath width and imaging resolution. This enables innovative operation modes such as polarimetric SAR tomography for determining the vertical structure of vegetation and ice. With novel imaging and processing techniques and the vast recording capacity of up to 8 Terabyte/day, it will provide vital information for solving pressing scientific questions in the biosphere, geosphere, cryosphere, and hydrosphere and will make an essential contribution for a better understanding of the Earth system and its dynamics. This paper gives an overview of the Tandem-L mission project which has successfully passed the intermediate system requirements review of Phase B1.

Differential synthetic aperture radar (SAR) tomography allows separation of multiple coherent scatterers interfering in the same range-azimuth resolution cell as well as the estimation of the deformation parameters of each scatterer. In this way, the spatio-temporal tomographic inversion serves as a means to resolve the layover and simultaneously improve deformation sampling. Compared to metropolitan regions with several man-made structures, the prevalence of coherent scatterers in the villages of alpine regions is generally low, while at the same time layovers are widespread due to the ruggedness of the terrain. Moreover, the drastic height variations in the imaged scene necessitate height-dependent compensation of the atmospheric phase delay variations within the tomographic inversion. This paper addresses these concerns while performing experiments on an interferometric stack comprising 33 Cosmo-SkyMed strimap images acquired in the summers between 2008-13 over Matter Valley in the Swiss Alps. The results show improved deformation sampling along the layover-affected mountainside.

Generating accurate repeat-pass interferometric airborne synthetic aperture radar (SAR) products demands the precise compensation of the 3-D motion of the aircraft. This requires to estimate and correct small residual motion errors (RMEs) within the accuracy limits of the sensor navigation subsystem, e.g., using residual motion compensation (MoCo) algorithms. Because of their data-driven nature, the performance of these algorithms severely degrades for heavily decorrelated interferograms. This letter proposes a generic RME estimation and compensation strategy optimally estimating RME in a stack of SAR acquisitions, where some interferometric pairs are strongly affected by decorrelation. The algorithm works even if the whole scene decorrelates, as long as the coherence magnitude is reasonably high over short temporal and spatial baselines. The approach entails correcting the navigation data of each slave image of a one-to-many interferometric network with a cumulative correction. The summation is over the results of a precursory application of a general data-driven residual MoCo algorithm (e.g., multisquint) to interferometric pairs for which the impact of interferometric decorrelation is marginal (i.e., small temporal and/or spatial baselines). Compared to other RME correction strategies, the main appeal of the proposed approach lies in the simplicity of its implementation. The overall methodology is tested on a zero-baseline time series acquired at L-band by the German Aerospace Center’s airborne system F-SAR.

Differential interferometry using ground-based radar systems permits to monitor displacements in natural terrain with high flexibility in location, time of acquisition, and revisit time. In combination with polarimetric imaging, discrimination of different scattering mechanisms present in a resolution cell can be obtained simultaneously with the estimation of surface displacement. In this paper, we present the preprocessing steps and the calibration procedure required to produce high-quality calibrated polarimetric single-look complex imagery with KAPRI, a new portable Ku-band polarimetric radar interferometer. The processing of KAPRI data into single look complex images is addressed, including the correction of beam squint and of azimuthal phase variations. A polarimetric calibration model adapted to the acquisition mode is presented and used to produce calibrated polarimetric covariance matrix data. The methods are validated by means of a scene containing five trihedral corner reflectors. Data preprocessing is assessed by analyzing the oversampled response of a corner reflector, and the polarimetric calibration quality is verified by computing polarimetric signatures and residual calibration parameters.

Soil moisture and wet biomass changes between two noninstantaneous SAR observations markedly affect the displacement estimates obtainable with Differential Interferometric Synthetic Aperture Radar (DInSAR). The separation, the modeling of these influences besides their uncoupling from the displacement signal, and the atmospheric disturbances are still unsolved issues for several repeat-pass interferometric applications. This paper focuses on the separation of vegetation changes from the other phase contributions affecting repeat-pass measurements over vegetated areas. These phase terms mainly relate to changes in soil moisture, atmospheric delays, and surface deformation. The separation is achieved with a first-order scattering solution decomposing the observed HH and VV DInSAR phases in the sum of several phase terms. The latter mainly consider the changes in soil surface scattering and in the two-way propagation through a vertically oriented vegetation canopy. No assumption is made on the spatiotemporal evolution of the displacement and atmosphere. The overall approach is tested on a L-band data set acquired over an agricultural area. Upon calibration, the model allows for estimating changes in wet biomass based on the nonzero HH-VV DInSAR phase difference observed over several birefringent agricultural fields. The obtained biomass estimates provide then a correction for the effect of vegetation changes on the observed HH and VV DInSAR phases. Deprived of the vegetation contribution, the remainder phase terms can be more easily explored for further analyses, e.g., the estimation of soil moisture changes and/or surface movements in vertically oriented vegetated areas.

This paper presents the first experimental demonstration of the virtual bandwidth synthetic aperture radar (VB-SAR) imaging scheme. VB-SAR is a newly developed subsurface imaging technique which, in stark contrast to traditional close proximity ground penetrating radar schemes, promises imaging from remote standoff platforms such as aircraft and satellites. It specifically exploits the differential interferometric SAR (DInSAR) phase history of a radar wave within a drying soil volume to generate high-resolution vertical maps of the scattering through the soil volume. For this study, a stack of C-band vertically polarized DInSAR images of a sandy soil containing a buried target was collected in the laboratory while the soil moisture was varied-first during controlled water addition, and then during subsequent drying. The wetting image set established the moisture-phase relationship for the soil, which was then applied to the drying DInSAR image set using the VB-SAR scheme. This allowed retrieval of high-resolution VB-SAR imagery with a vertical discrimination of 0.04 m from a stack of 1-m vertical resolution DInSAR images. This paper unequivocally shows that the basic principles of the VB-SAR technique are valid and opens the door to further investigation of this promising technique.

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band.

In this study, the dependence of the polarimetric differential interferometric phases on soil moisture and biomass variations was inferred empirically with multilinear regression techniques. This was done for a fully polarimetric airborne L-band SAR data set. Neglecting the impact of soil moisture, the linear phase model was inverted to derive fresh biomass maps of the inspected test site showing good agreement with the collected ground measurements.

The vertical structure of firn below the surface of the Greenland ice sheet is driven by meteorological conditions like the intensity and duration of melting periods. The formation of ice inclusions through meltrefreeze processes leads to strong scatterers whose vertical distribution influences interferometric coherences. Interferometric coherence profiles from quad-pol SAR data show clear sensitivity to the different subsurfaces of two test sites with different melting periods. The differences in coherence are explained in this study through modelling of the different vertical subsurface structures supported by ground penetrating radar measurements. The shown sensitivity demonstrates the potential of polarimetric SAR interferometry to retrieve information about meltrefreeze processes and layering below the surface of ice sheets.

In this work the science activities of the TanDEM-X satellite mission are presented and an update on the proposal submission and planed announcement of opportunities are given.

Temporally resolved, 3-dimensional surface elevation data provide valuable insight into glacier dynamics, e.g. glaciers surges: short periods of rapid acceleration and mass transport which can pose significant hazard potential. Particularly in remote locations, such hazards can develop unobserved by the downstream communities at risk. This paper demonstrates the relevance of single-pass InSAR missions with two examples: Glacier lake outburst floods (GLOFs) of Kyagar Glacier in the Chinese Karakorum and the AruCo twin glacier collapse in Tibet. Periodic surging of Kyagar Glacier causes GLOFs affecting communities over 500km downstream. Near AruCo, the mysterious but catastrophic collapses of two neighboring glaciers were also preceded by surge-like processes. In both cases typical signs of glacier surging (unusual advance) were absent in satellite images, but TanDEM-X InSAR DEMs revealed characteristic, surface elevation changes. Here, we summarize which information can be gained from surface elevation data to assess surge-related hazard potentials and to understand the underlying processes.

There is a long-stranding need of reliable space-borne observations on snow mass. Current satellite sensors and data products are largely unable to meet requirements presented in particular by numerical prediction and watershed management. Consequently, several concept studies have been initiated to address these specific needs, outlining possibilities for future space sensors focusing on retrieval of snow mass and other characteristics of the terrestrial cryosphere. The results of these of-going mission concept studies are presented and discussed. Several possible sensor options are presented, which would address diverse needs on either hemispheric or regional scales.

AfriSAR is an ESA-funded airborne P- and L-band SAR campaign over the African tropical forests of Gabon carried out by ONERA (July 2015) and DLR (February 2016). The different acquisitions were designed in order to collect multibaseline fully polarimetric data allowing the inversion of key forest vertical structure-based parameters, like e.g. forest height and high resolution reflectivity profiles. Results of processing and parameter inversion with the DLR's F-SAR data are shown in this paper at both P- and L-band.

In this study, we develop a single-baseline Pol-InSAR approach for estimating the ground-to-volume ratios at different polarizations. We then apply it to time series of Pol-InSAR measurements over agricultural crops in C-Band to assess the relationship between these estimated Pol-InSAR parameters and plant biophyical properties such as structure, biomass and water content. The assessment reveals that the temporal patterns of the Pol-InSAR estimated ground-to-volume ratios are influenced by vegetation water content variations. The dependence of these temporal patterns on polarization indicates the role of crop structural properties in the Pol-InSAR measurements.

Differential SAR tomography is a means to resolve layover of temporally coherent scatterers while simultaneously estimating their elevation and average deformation. In alpine regions, drastic height variations result in frequent layovers which are rejected during typical persistent scatterer interferometric (PSI) analyses. In this paper, we explore the potential of tomographic techniques to improve deformation sampling in an alpine region of interest relative to a PSI-based deformation assessment. The mitigation of the atmospheric phase contributions, as required for both tomography and PSI, is often more involved in alpine regions due to strong spatial variations of the local atmospheric conditions and propagation paths through the troposphere. We assume a linear multivariate dependence of atmospheric phase on the spatial location and height of the scatterers, estimate it using universal/regression kriging and subsequently incorporate it within the tomographic focusing. Experiments are performed on an interferometric stack comprising of 32 Cosmo-SkyMed strimap images acquired in the summers of 2008-2013 over Mattervalley in the Swiss Alps.

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.

In forest and agricultural scattering scenarios, the backscattered synthetic aperture radar (SAR) signature consists, depending on the frequency, of the superposition of ground and volume scattering contributions. Using multibaseline SAR data, SAR tomography techniques allow resolving contributions occurring at different heights. Two algorithms for the separation of ground and volume scattering are compared with respect to their ability to provide a coherent volume component that can be further used for parameter inversion, both of them requiring only the a priori known ground topography. Once the volume-only coherences are available, the total ground and volume scattering powers are estimated by means of a least squares fitting. The objective of this letter is to quantitatively evaluate the performance of this estimation by means of a Monte Carlo analysis with simulated data focusing on the impact of vertical resolution, errors in the knowledge of the ground topography and phase calibration residuals.

The estimation of soil moisture and crop biomass based on differential interferometry is questioned by the influence of intercepted rain (i.e., plant surface moisture) on repeat-pass observables. The magnitude, the origin of this effect, as well as its dependence on system and crop biophysical parameters have been only marginally addressed so far. This paper intends to investigate these aspects within the frame of a laboratory experiment carried out in a highly controlled electromagnetic environment. The collection of multifrequency and fully polarimetric scatterometer profiles offers a distinctive data set, which helps to understand the variations of the interferometric observables in response to a varying plant surface moisture. These changes are assessed by comparing the predictions of a first-order scattering solution with the impact found in the experimental data. Furthermore, the connection between plant surface moisture and differential interferometric observables (i.e., the magnitude and phase of the interferometric coherence) is empirically tested with the aid of regression techniques. Irrespective of frequency and polarization, intercepted water is found to impact the interferometric coherence in a similar way as changes in soil and/or plant water status, i.e., the increase of the sensor to target optical path. Changes of plant surface moisture might be erroneously mistaken either for soil water content or fresh biomass variations. Therefore, this paper raises the possibility that, in certain circumstances, intercepted water might represent a potential source of bias for the estimation of these two land surface parameters.

An acknowledgement provided by the authors was lost during production for the above paper[1]. It is provided here as follows:

Differential interferometric synthetic aperture radar (DInSAR) measurements are sensitive to displacements, but also to soil moisture mνchanges. Here, we analyze whether soil moisture can be estimated from three DInSAR observables without making any assumptions about its complex spatio-temporal dynamics, with the goal of removing its contribution from the displacement estimates. We find that the referenced DInSAR phase can be a suitable means to estimate mνtime series up to an overall offset, as indicated by correlations with in situ measurements of 0.75-0.90 in two campaigns. However, the phase can only be referenced when no displacements (and atmospheric delays) occur or when they can be estimated reliably. We study the separability of displacements and mνusing two additional DInSAR observables (closure phase and coherence magnitude) that are sensitive to mνbut insensitive to displacements. However, our analyses show that neither contains enough information for this purpose, i.e., it is not possible to estimate mνuniquely. The soil moisture correction of the displacement estimates is hence ambiguous too. Their applicability is furthermore limited by their proneness to model misspecifications and decorrelation. Consequently, the separation of soil moisture changes and displacements using DInSAR observations alone is difficult in practice, and-like for mitigating tropospheric errors-additional data (e.g., external mνestimates) or assumptions (e.g., spatiotemporal patterns) are required when the mνeffects on the displacement estimates are comparable to the magnitude of the movements. This will be critical when soil moisture changes are correlated with the actual displacements.

The standard TanDEM-X baselines have been designed to optimize the high resolution global Digital Elevation Model (DEM) generation. However, during the Science Phase of the mission longer baselines are available. This allows interferometric measurements with a higher vertical sensitivity, more appropriate for agricultural applications, where the crop heights are too small to be properly detected and analyzed with the standard baselines. This paper evaluates the use of the experimental long baseline TanDEM-X acquisitions for the monitoring of the agricultural changes in dual-pol single-pass interferometric time series.

This paper presents a study of the sensibility of the incoherent (electromagnetic backscattering model) and coherent (DInSAR and PolInSAR inversion) crop height estimation methods of SAR imaging. The methods were compared for paddy-rice crop height monitoring with a TanDEM-X dataset. For this, rice-cultivated agricultural fields located in Northern Turkey were selected. Intensive ground data collection during the cultivation period in 2015 was carried out. The accuracy analysis showed that the requirement of external (vegetation-free) DEM in DInSAR-based crop height estimation decreases its performance compared to the PolInSAR and backscattering inversion methods.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) opens a new era in spaceborne radar remote sensing. A single-pass SAR-interferometer with adjustable baselines in across- and in along-track directions is formed by adding a second (TDX), almost identical spacecraft to TerraSAR-X (TSX) and flying the two satellites in a closely controlled formation. TDX has SAR system parameters which are fully compatible with TSX, allowing not only independent operation from TSX in a mono-static mode, but also synchronized operation (e.g. in a bi-static mode). With typical across-track baselines of 200-600 m DEMs with a spatial resolution of 12 m and relative vertical accuracy of 2 m will be generated. The Helix concept provides a save solution for the close formation flight by combining a vertical separation of the two satellites over the poles with adjustable horizontal baselines at the ascending/descending node crossings.

AfriSAR is an ESA-funded airborne P-band SAR campaign over the African tropical forests of Gabon that is being carried out by ONERA (July 2015) and DLR (February 2016) in support of the development of the geophysical algorithms of the future BIOMASS mission. Multibaseline fully-polarimetric acquisitions have been designed over four test sites in order to further develop and validate algorithms for the estimation of forest upper canopy height, 3-D structure and biomass by implementing PolSAR (SAR Polarimetry), Pol-InSAR (Polarimetric SAR Interferometry) and TomoSAR (SAR Tomography) P-band measurements. In this paper first data processing results will be shown.

Tandem-L is a highly innovative SAR satellite mission for the global observation of dynamic processes on the Earth's surface with hitherto unknown quality and resolution. Thanks to its novel imaging techniques and its unprecedented acquisition capacity, Tandem-L will deliver urgently needed information for the solution of pressing scientific questions in the areas of the biosphere, geosphere, cryosphere and hydrosphere. The feasibility of Tandem-L has been analyzed and confirmed in the scope of a phase A study, which has been conducted in close cooperation between the German Aerospace Center (DLR) and the German space industry. This paper provides an overview of the Tandem-L mission concept and summarizes the actual development status.

The polarimetric synthetic aperture radar (PolSAR) has been demonstrated a huge application potential in soil moisture (SM) inversion under vegetation. However, this promising approach has seldom been reported applied in an oasis of the northwest arid regions of China. This paper presents an analysis of component decomposition based on Radarsat-2 images. The main purpose of the analysis is to provide a preliminary recognition of the scattering mechanisms. The results of the analysis show that the scattering mechanisms in the vegetated oasis are very complex, which implies that scattering mechanisms must be decomposed before SM inversion in a vegetated surface. The analysis provide basis for the SM inversion under vegetation in an oasis of arid regions.

The first experimental demonstration of the Virtual Bandwidth SAR (VB-SAR) scheme is provided. VB-SAR is a new technique that promises subsurface imaging of soils at ultra-high, centimeter-scale resolution at large stand-off distances applicable to aircraft and spacecraft. This paper reports on how a stack of C-band images were used to retrieve high resolution vertical profiles of the backscattering through a soil in the laboratory. The VB-SAR scheme captures the phase behavior of a soil across a stack of DInSAR images as the soil dries. The real frequency of the interrogating radar behaves as a higher, virtual frequency within the soil by virtue of its higher-than-air dielectric. As the dielectric changes with time, the DInSAR stack captures a virtual bandwidth. Using this scheme, it was possible to produce a vertical slice of the backscatter through a soil at 10cm resolution, much improved on the formal 1m resolution offered by the real 150MHz bandwidth.

Polarimetric SAR Interferometry (Pol-InSAR) is a SAR remote sensing discipline with unique and powerful applications related to the vertical structure of natural and man-made volume scatterers. The coherent combination of singleor multi-baseline interferograms acquired at different polarisations provides sensitivity to the vertical distribution of scattering processes and allows their characterisation by using the associated (volume) interferometric coherences [1][2][3][4][5]. Pol-InSAR is today a well established technique that promises a break-through in solving essential radar remote sensing problems. Indeed, structural parameters of volume scatterers in the biosphere and cryosphere such as vegetation height, structure, biomass, snow depth, and ice layering are today critical inputs for ecological process modeling and enable monitoring and understanding of eco-system change. In this paper we review the actual status of PolInSAR techniques and applications in forestry, agriculture and cryosphere; assess results from actual space (TanDEM-X) and air-borne (F-SAR) campaigns and experiments at different frequencies; and finally discuss new potential applications and challenges.

Recently, the 3-D backscattering distribution of forests and ice bodies has been factor of study with the HoloSAR imaging mode, showing its capabilities to obtain 3-D imaging reconstructions of volumes over 360° at very high resolution. In this paper, the first assessment of agricultural crops in the holographic SAR tomography (HoloSAR) mode is presented. The goal of this research is to investigate the retrieval of the three-dimensional backscattering of crop fields as a function of the azimuthal aspect angle and the tomographic constellation. To that end, a HoloSAR campaign was conducted at X-/C-band by DLR's F-SAR sensor in Wallerfing, Germany. The experimental results of this study show 2-D and 3-D polarimetric images, where the co-polar phase is analyzed qualitatively giving some indications about the kind of scattering mechanisms.

This paper presents a study of the processing chain for ice and glacier structures in the holographic SAR tomography (HoloSAR) mode. The algorithm is mainly based on the fast factorized back-projection (FFBP) to solve a three-dimensional space using fully polarimetric data. The motivation of HoloSAR in the cryosphere is mainly driven by recent investigations that have shown the capabilities of this mode to obtain 3-D imaging reconstructions of volumes over 360 ° at very high resolution. In this regard, a HoloSAR campaign was conducted at L-/P-band by DLR's F-SAR sensor in K-Transect, Greenland. The first experimental results of this study show a polarimetric analysis of the resulting images of a single circular flight, where the arc- and circular patterns of the 2-D images in the (x, y) plane already indicate significant wave penetration. The second part presents 3-D images obtained by the combination of the circular and vertical synthetic apertures, which enable an estimation of the vertical profile of the glacier. This mode potentially allows for a more accurate estimation of the vertical profile of ice sheets and bedrock.

The applicability of a recently developed compact polarimetric decomposition and inversion algorithm for C-band to estimate soil moisture under growing agricultural vegetation cover is investigated using simulated L-band compact Polarimetric Synthetic Aperture Radar (PolSAR) data. The surface scattering component is separated from the volume scattering component through a model-based compact polarimetric decomposition under the assumption of a randomly oriented vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physically-based, low frequency surface scattering models such as Extended Bragg (X-Bragg) and Polarimetric Two Scale Model (PTSM). The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR 2006 campaign over the Görmin test site in Germany. The compact PolSAR derived soil moisture is validated against in situ measurements. Including the entire growing season and three different crop types, the estimated soil moisture values indicate an overall RMSE of 9-12 vol.% and 9-15 vol.% using the X-Bragg and PTSM, respectively.

SAR tomography can be used as an add-on to persistent scatterer interferometry (PSI) to increase deformation sampling in urban areas by resolving the frequently occurring layovers that are by definition rejected in the PSI processing. This paper, while focusing on the case of a typical high-rise building in layover, quantitatively assesses the potential gain in deformation sampling achieved by the added use of an advanced SAR tomographic technique relative to a PSI approach. At the same time, the quantity of the detected scatterers is weighed against their quality, as assessed on the basis of root-mean-square (RMS) phase deviation between the measurements and the model fit. The quality of the scatterers is also compared with the quality of the persistent scatterers as identified with a PSI approach. The experiments are performed on an interferometric stack of 50 TerraSAR-X stripmap mode images.

Synthetic Aperture Radar (SAR) remote sensing techniques play a significant role in modern agricultural crop monitoring by relating the plant structure (height, biomass, yield and growth-stage) to the backscattering behavior of the vegetative canopy. The current trend in crop monitoring is towards precision agriculture, which needs detailed morphology information. By predicting the physical structure, one can just determine the under and overgrowth conditions. In this study, we propose a probabilistic inversion algorithm for a Radiative Transfer Theory (RTT) model which relates the backscattering response of a canopy to its physical structure. The outcomes of the inversion provided promising results by estimating the dimensions of the primary structures with a small bias.

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 is being generated. While the main mission phase for DEM data acquisition has been finished in 2014, the processing of the global TanDEM-X DEM will be concluded in autumn 2016. Final DEMs are well within specifications and feature a low percentage of void areas. Following the DEM data acquisition the capabilities of this unique mission for new scientific application have been demonstrated. In the meantime the continuation of the joint TerraSAR-X/TanDEM-X mission was approved and will be dedicated to the generation of DEMs with even higher accuracy for selected areas and further scientific experiments.

Permafrost soils have been shown to respond rapidly to warming temperatures. When ice-rich permafrost soils thaw, the melting ground ice reduces the volume and stability of the soils, inducing changes in the topography. We monitor surface elevation changes in three test sites in Northern Eurasia using single-pass TanDEM-X Science Phase data with submetre vertical precision. The results indicate the suitability of single-pass InSAR data for monitoring thaw-induced topographic changes (e.g. coastal erosion) but they also reveal the spurious impact of late-lying snow packs and water bodies, both of which are common in lowland permafrost areas. Furthermore, the coherence and hence the precision with which elevation changes can be estimated is found to be limited by the noise level in certain cases. As some of these influences could be mitigated using appropriate mission and acquisition designs, we conclude that single-pass interferometry has considerable potential for monitoring thaw-induced surface elevation changes in permafrost areas, which in turn could contribute to assessing their vulnerability, fate, and climate system feedback in a warming climate.

Depth-resolved radar imaging at L- to X-band has barely been applied to soils owing to limitations imposed by wave absorption within the soil and the resolutions attainable from air- or spaceborne platforms. Rather, soils are commonly studied using radar systems that cannot resolve the depth component. In this study, we adapt tomographic profiling to image the wetting and drying of sandy soil using a ground-based radar with a depth resolution of about 10 cm. The depth-resolving capabilities are achieved using synthetic aperture processing of the measurements obtained with downward pointing antennas operating at C-band with 2 GHz bandwidth. The observed subsurface backscatter appears to be governed by the local soil moisture content and the soil moisture content above (absorption). When the soil moisture content changes, the observed differential interferometric phase and coherence are consistent with the notion that the total depth-averaged interferometric return is governed by volume scattering and wave propagation within the soil. However, existing models of the depth-averaged interferometric coherence do not include variations in the volume scattering power induced by soil moisture changes, which the backscatter observations indicate exist. Besides improving our understanding of the radar backscatter from heterogeneous soils, depth-resolved imaging may in future also provide direct information about the spatial variability of soil properties and soil moisture dynamics.

Spaceborne synthetic aperture radars (SARs) represent a powerful tool to perform cryospheric observations due to their high spatial resolution and capability to acquire data during the winter time. Especially at lower frequencies, SAR signals penetrate beneath the glacier surface through the shallow snow cover, interacting with surface as well as subsurface features. This makes the scattering scenario very complex and the interpretation of SAR backscattering from glaciers and ice sheets not straightforward. In the case of polarimetric SARs, the understanding of polarization phase differences represents one of the main open issues. In this letter, a physical model is employed to relate copolarization HH-VV phase difference (CPD) to structural and dielectric properties of snow and firn which characterize the uppermost layers of glaciers. Modelled CPD values are compared to values observed in airborne L-band (1.3 GHz) SAR data, acquired over the Austfonna ice cap, in Svalbard, in spring 2007. The inversion of the employed model shows promising results toward the retrieval of firn properties. In particular, the obtained thickness estimates are found to be in good agreement with the GPR profiles measured in coordination with the SAR acquisitions.

Boreal forests are characterized by a rather homogeneous stand structure that allows by means of a single allometric equation to estimate biomass from forest height with sufficient accuracy and, therefore, to use this equation for quantitative biomass classifications. In this paper, interferometric TanDEM-X DEM data are used to estimate forest height over boreal forest systems. The accuracy of the height inversion is evaluated for single-and dual-baseline scenarios, under summer and winter conditions. Then, an allometric equation is used to transfer forest height to biomass. For this, two forest sites, boreal (Krycklan) and hemiboreal (Remningstorp) in north and southern Sweden, respectively, are investigated. A performance analysis is carried out to estimate the maximum number of biomass classes obtained, depending on the height estimation accuracy. For summer acquisitions, four biomass classes can be obtained, with a maximum biomass class of > 200 Mg/ha. For winter acquisitions or when a mixed summer-winter approach is applied, five biomass classes, up to 220 Mg/ha, can be obtained. This classification shows a good agreement with CORINE, an existing land cover classification, and can improve it by adding quantitative forest biomass classes with a high spatial resolution of 16 × 16 m.

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active-passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters β are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution.

Over the last 30 years, the use of airborne and satellite remote sensing techniques has revolutionized glaciology through unequaled improvements in the scale and in the temporal and spatial resolutions of cryospheric observations. In this sense, the use of synthetic aperture radar (SAR) configurations likely was the greatest advance of the past two decades in the monitoring of the Earth's cryosphere. This paper describes a novel polarimetric scattering model to interpret polarimetric SAR (PolSAR) measurements of glaciers and ice sheets. Different scattering components are considered according to the specific glacier facies, in order to infer physical parameters of the ice volume. Total backscattering is modeled as the incoherent sum of possible surface and volume contributions. Surface scattering is described using the X-Bragg model in order to account for roughness, whereas clouds of spheroidal scatterers (either prolates or oblates) are considered to model volume scattering. The model includes differential propagation effects related to firn anisotropy and is therefore able to interpret polarimetric phase differences often present in PolSAR data acquired over glaciers. The developed model is used to interpret different scenarios imaged at L-band in fully polarimetric mode by the E-SAR sensor of the German Aerospace Center (DLR) over test sites located on the Austfonna ice cap, Svalbard, Norway, as part of the ICESAR2007 campaign. The obtained results are consistent with the reference information provided by the available ground measurements about the subsurface structure of the study area and encourage the use of dedicated scattering models for the different glacier zones.

A novel oriented volume over ground (OVoG) inversion scheme is developed and tested on a data set of simulated agricultural scenarios and real SAR acquisitions. The algorithm makes use of multibaseline measurements to estimate the whole set of the OVoG structural parameters (e.g., crop height, differential extinction between eigenpolarizations, and ground-to-volume ratios) and is significantly robust against non-volumetric decorrelation contributions. The theoretical assessment points out that, in the dual-baseline case, the vegetation height hV can be estimated with a relative root-mean-square deviation (%RMSD) of 7.8% if the selected baselines fulfill the condition 1.2zhVzis the vertical wavenumber). Furthermore, the variance of the estimates is inversely related to the number of baselines Nb. Compared with the dual-baseline case, the RMSD of the differential extinction is reduced by 45% (from 1.1 to 0.6 dB/m) when Nb = 5 baselines are employed, whereas its mean bias is independent of Nb. The proposed scheme has been assessed using a set of repeat-pass F-SAR acquisitions at L-, C-, and X-band of an agricultural area in Germany. Using two baselines, the height of maize and rape fields is estimated with an average 10% %RMSD if the inversion is carried out over L-band acquisitions. On the other hand, when X-band data are employed, one can obtain reliable estimates of wheat and barley height, with a %RMSD better than 24%. The study also indicates the existence of differential wave propagation effects through maize (Δσ = σVV- σHHbetween 0.7 and 1 dB/m) and rape (Δσ = -0.8 dB/m) canopies at L-band.

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against in situ frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils (rmse = 3.1-8.9 vol.%) with inversion rates of around 30-70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions.

Persistent scatterer interferometry (PSI) is in operational use for spaceborne synthetic aperture radar (SAR)-based deformation analysis. A limitation inherently associated with PSI is that, by definition, a persistent scatterer (PS) is a single dominant scatterer. Therefore, pixels containing signal contributions from multiple scatterers, as in the case of a layover, are typically rejected in the PSI processing, which in turn limits deformation retrieval. SAR tomography has the ability to resolve layovers. This paper investigates the added value that can be achieved by operationally combining SAR tomography with a PSI approach toward the objective of improving deformation sampling in layover-affected urban areas. Different tomographic phase models are implemented and compared as regards their suitability in resolving layovers. Single-look beamforming-based tomographic inversion and a generalized likelihood ratio test (GLRT)-based detection strategy are used to detect single and double scatterers. The quantity of the detected scatterers is weighed against their quality as defined in terms of the phase deviation between the single-look complex (SLC) measurements and the tomographic model fit. The gain in deformation sampling that can be derived with tomography relative to a PSI-based analysis is quantitatively assessed, and alongside the quality of the scatterers obtained with tomography is compared with the quality of the PSs identified with a PSI approach. The experiments are performed on an interferometric stack of 50 TerraSAR-X stripmap images. The results obtained show that, although there is a tradeoff between the quantity and the quality of the detected scatterers, the tested SAR tomography approach leads to an improvement in deformation sampling in layover-affected areas.

The polarization diversity of the phase causes ambiguities in the estimation of displacements using differential interferometry. Over natural surfaces such as vegetated areas, the magnitude of these ambiguities is potentially related to complex dynamic processes such as vegetation growth. As the properties and possible origins of such diversity (besides noiselike influences) over changing vegetation canopies are virtually unknown, we propose to investigate them empirically using an L-band zero-baseline data set covering one growing season over different agricultural crops. We frequently observe HH-VV phase differences exceeding 0.5π, corresponding to a displacement discrepancy of 3 cm. The HH-VV phase difference and other properties of the polarimetric coherence regions (e.g., the shape and the relation to the in situ observed biomass) vary with the crop type. The observations over wheat and barley and, to a lesser extent, rape suggest the presence of birefringence within the canopy. By contrast, those over maize and sugar beet, while also showing phase diversity, cannot be explained by birefringence or similarly simple models. Irrespective of the origin of this dependence, its presence and systematic nature indicate the potential importance of vegetation effects in differential interferometry, which may limit the accuracy of the estimated deformations over vegetated areas.

Displacements of the Earth's surface can be estimated using differential interferometric synthetic aperture radar. The estimates are derived from the phase difference between two radar acquisitions. When at least three such acquisitions are available, one can compute the displacement between the first and the third acquisition and compare it with the sum of the two intermediate displacements. These two are expected to be equal for a piston-like spatially uniform deformation. However, this is not necessarily the case in measured data. Such lack of phase closure can be due to decorrelation noise alone. It has also been attributed to complex scattering processes such as soil moisture changes or multiple scattering sources. However, the nature of these nonrandom effects is only poorly understood in cold regions, as the role of snow and freeze/thaw processes has not been studied to date. To distinguish the noise-like and the systematic effects, an asymptotic Wald significance test is proposed. It detects situations when the observed closure error cannot solely be explained by noise. Such situations with p25%) in the X-band observations of ice-rich permafrost regions in the Lena Delta, Russia, indicating the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Satellite-based monitoring of these displacements is thus possibly subject to complex error sources in high-latitude regions.

In this paper the status of the TanDEM-X science activities, the distribution of the digital elevation mode, the schedule for the science phase and first results obtained from this will be presented. The goal of the TanDEM-X mission is to provide a higher level product (the digital elevation model) and to serve the science community with basic interferometric Synthetic Aperture Radar products (a pair of CoSSC's). The focus in this paper will be given to the activities within the last Announcement of Opportunity's for data distribution in terms of description of the science team and their research field of interest, the amount of data acquired and the first results obtained from the science team.

In this study application results are presented derived from multi-parametric SAR observations covering five different thematic domains: forest, agriculture, ocean, urban and cryosphere. In total 21 application products have been selected and described. Their application on different data sets, space- and airborne sensors, was demonstrated and can independently be reproduced by any scientist. The results and algorithms are available soon through Springer.

The NASA Soil Moisture Active Passive (SMAP) mission is designed to produce high-resolution (9 km) global mapping of surface soil moisture based on L-band radar and radiometer measurements. The multi-scale measurements are combined using time-series of active passive microwave data to retrieve the statistical regression parameters (α, β) from successive overpasses. In this study, we introduce a physically-based forward model as well as data-based retrieval of the β-parameter. The forward model stems from analyses of the scattering and loss terms occurring during bare and vegetated soil scattering/emission and allows a physically-based modelling of the β-parameter. This provides possibilities to analyze the different influences of soil roughness as well as vegetation structure and moisture on the β-parameter under different environmental conditions. In addition, a physically-based retrieval of β from one active-passive SMAP acquisition couple is proposed, circumventing lengthy time-series regressions. The key operation enabling a single-pass retrieval of the β-parameter is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal together with an appropriate polarimetric vegetation volume model.

In this paper, agricultural crop volumes are analysed using (polarimetric) tomographic SAR methodologies. A procedure for the separation of the ground and volume multibaseline coherences is proposed. This separation is used in particular to investigate the polarization dependency of the vegetation vertical structure in order to get first insights about orientation effects. This analysis has been carried out for different crops and on different development stages. The presented results have been obtained by processing a multibaseline fully polarimetric data set purposely acquired at C-band by the DLR's airborne sensor F-SAR in 2014.

Dry snow can be considered as a transparent but refractive medium which causes a phase delay in the reflected signal of active radar remote sensing systems. Here, we analyze the phase delay to estimate Snow Water Equivalent (SWE), the depth of fresh snow and the anisotropic orientation of ice grains in the snow volume. SWE is determined from the integrated phase shift measured by differential interferometry. The temporal evolution of the snow anisotropy could be observed because different microwave polarizations show different propagation speeds in anisotropic snow. The depth of fresh snow as well as snow metamorphosis is discussed with respect to characteristic phase-shifts in the co-polar phase difference. Ground based radar observations from the Snow-scat instrument installed at a test site near Sodankylä, Finland, form the data basis for this paper.

Quadratic forms play an important role in Polarimetric and Interferometric Synthetic Aperture Radar (Pol-InSAR) images. This work is aimed at solving (rigorously and with approximations) the integrals of quadratic forms. Specifically, it is possible to derive that the integral of the quadratic form of covariance matrices (i.e. power of a polarization channel) is equal to the third part of the matrix Trace. Additionally, the integral of the Pol-InSAR coherence (expressed with quadratic forms) can be approximated with the same expression where the quadratic forms are substituted by Trace operators. The derived equations are tested on real ESAR (DLR) quad-polarimetric data.

Icebergs are hazards to lives and goods during navigation in cold waters. In the context of iceberg detection with SAR images, an extensive work was carried out for the detection of large icebergs, but the identification of small bergs or target embedded in sea ice is till difficult. In this work, a new detector is propose to tackle this issue based on dual-polarimetric incoherent (i.e. detected) images. The algorithm is based on the principle that small icebergs are contained in a limited area and they are supposed to have a volume contribution that is higher compared to the sea or sea-ice background. The detector is tested on RADARSAT-2 quad-polarimetric data, where only the multi-looked intensities of the HH and HV channels are used.

ALOS-Next/Tandem-L is a proposal for a highly innovative L-band SAR satellite mission for the global observation of dynamic processes on the Earth's surface with hitherto unparalleled quality and resolution. It is based on a collaboration between DLR and JAXA which started with a pre-phase A study in 2013 and is currently undergoing a phase A study. Thanks to the novel imaging techniques and the vast recording capacity with up to 8 Tbytes/day, it will provide vital information for solving pressing scientific questions in the biosphere, geosphere, cryosphere, and hydrosphere. By this, the new L-band SAR mission will make an essential contribution for a better understanding of the Earth system and its dynamics. ALOS-Next/Tandem-L will, moreover, open new opportunities for risk analysis, disaster management and environmental monitoring by employing especially designed acquisition modes and techniques in combination with a reconfigurable tandem satellite configuration and an L-band SAR instrument with advanced digital beamforming techniques.

The paper introduces a new polarimetric scattering model able to interpret and invert coherent polarimetric SAR (PolSAR) measurements over glaciers and ice sheets. Individual scattering components related to ice lenses and pipes are considered to model the subsurface scattering structure of ice sheets. The model is able to interpret the scattering amplitudes, their ratios at the different polarizations as well as the observed polarimetric phase differences. The co-polarization (HH-VV) phase difference is related to the structural anisotropy of the firn layer and will be used to estimate its thickness. The model is validated against L-band PolSAR data acquired by the E-SAR sensor of the German Aerospace Center (DLR) over the Austfonna ice cap in Svalbard during the ICESAR2007 campaign and available GPR profiles.

This work investigates the potential of single and dual-baseline Pol-InSAR acquisitions for crop height estimation. A performance comparison is carried out between the single-baseline 3-stage approach and a dual-baseline Oriented Volume over Ground (OVoG) inversion scheme. The assessment is performed on simulated realisations as well as observed DLR's F-SAR repeat-pass acquisitions.

This paper presents an assessment of the backscattering of glaciers in the holographic SAR tomography (HoloSAR) mode. The motivation of HoloSAR in the cryosphere is mainly driven by recent investigations in the biosphere that have shown the capabilities of this mode to reconstruct volumes with 3-D imaging reconstructions over 360 ° at very high resolution. To that end, an HoloSAR campaign was conducted at L-band by the DLR's F-SAR sensor over the Findel glacier, Monte Rosa, Switzerland. The first part of this study shows a polarimetric analysis of the resulting images of a single circular flight, where the arc- and circular patterns of the 2-D images in the (x, y) plane already indicate wave penetration. The second part presents 3-D images obtained by the combination of the circular and vertical synthetic apertures, which enable an estimation of the vertical profile of snow, ice sheets and bedrock.

The capability of hybrid Polarimetric Synthetic Aperture Radar (PolSAR) to estimate soil moisture (under vegetation) was assessed within the agricultural region of Wallerfing in Lower Bavaria, Germany. A novel methodology based on a hybrid polarimetric decomposition together with a surface component inversion is developed to retrieve surface soil moisture. The model-based, hybrid decomposition technique is used assuming a randomly oriented vegetation volume to obtain the surface scattering component. After vegetation removal, the surface scattering component is inverted for soil moisture by comparison with the surface scattering component, modeled by the Extended Bragg (X-Bragg) model including a depolarization term. The developed algorithm is applied on multi-temporal hybrid polarimetric C-band RISAT-1 data acquired from April to October 2014. The estimated soil moisture values indicate an overall Root Mean Square (RMS) error of 6.2 to 8.5 vol.% accounting for the entire growing season and four different plant types.

Persistent scatterer interferometry (PSI) assumes the presence of a single temporally coherent scatterer in a range-azimuth pixel. Multiple scatterers interfering in the same pixel, as for the case of a layover, are typically rejected. Conventional SAR tomography (3D SAR) is a means to separate the individual scatterers in layover. Advanced tomographic inversion approaches employing extended phase models additionally allow simultaneous retrieval of scatterer elevation and deformation parameters. In this way, SAR tomography can increase deformation sampling and thereby complement a PSI-based analysis. This paper investigates the use of tomography as an add-on to PSI for spatio-temporal inversion of single and double scatterers in urban areas. Results are provided on an interferometric stack of 50 stripmap TerraSAR-X images acquired over the city of Barcelona.

Wetlands are very valuable ecosystem which may present fast dynamics. This study is focused on the observation of the Kafue Flats exploiting TanDEM-X polarimetric and interfer-ometric data. Several analysis are carried out considering classification, change detection and Digital Elevation Model (DEM) estimation. Specifically, it is possible to observe that differences between single-pass DEM acquired during different dates can provide promising estimations of the flood water level and extent (provided that the water is partially covered by vegetation). Additionally, the polarimetric information can add details regarding small areas in the data where swamps are presents also in dry seasons.

Remote sensing techniques with Synthetic Aperture Radar (SAR) provides detailed information of the electromagnetic scattering behaviour of their targets. It is broadly used for agricultural monitoring due to their all-weather acquisition possibility. Based on the nature of SAR systems, they are known to be sensitive to physical changes in the targets, such as plant growth. An uncertainty analysis for a plant morphology based electromagnetic scattering model is assessed in this study. The global sensitiveness of the model to the input variables are tested with respect to their total Sobol' indices. This helps to understand the parameters for each growth stage and polarization channel, which can be used to reduce the complexity of the scattering model.

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 is being generated. While the main mission phase for DEM data acquisition has been finished in 2014, the processing of the global TanDEM-X DEM will be concluded mid-2016. Final DEMs for more than 65% of all land masses are already available for scientific and commercial applications. A 15-month science phase of the TanDEM-X mission started in October 2014 which offers the opportunity to explore the generation of DEMs with even higher accuracy for selected areas, and to demonstrate the capabilities of this unique mission for new scientific applications.

Forest height is an important parameter for the allometric estimation of above-ground forest biomass (AGB). However, variable forest stand densities limit the performance of the allometric estimation of AGB from height measurements alone. Recently, the use of vertical forest structure information as an indicator for the variation of stand density has been proposed and used to improve the allometric estimation of AGB from height measurements. In this letter, the use of vertical radar reflectivity profiles at L-band obtained from SAR tomography, as a proxy for vertical forest structure for the allometric estimation of AGB, is investigated. L-band reflectivity profiles, which are reconstructed from data at different polarizations (HH and HV) and acquired under “moist” and “dry” weather conditions, are investigated. The proposed allometric AGB estimator increases the correlation factor from 0.60 to 0.81 and reduces the root-mean-square error from 50.25 to 36.30 Mg/ha when compared with the AGB estimation from forest height alone. The effect of polarization and weather conditions on the AGB estimation performance is discussed.

Synthetic aperture radar plays a vital role in ship detection due to the possibility of acquiring high-resolution images at nighttime and under cloud cover. This letter is focused on improving ship detection, exploiting the capability of TanDEM-X to collect interferometric data. Currently, along-track interferometry is used to estimate the speed of ocean surface currents or vessels. The detection of ships plays an important role in the retrieval of vessel speed and is mostly executed exploiting only one of the TanDEM-X images (i.e., not taking advantage of the availability of a second interferometric image). The aim of this study is to extend the capabilities of a ship detector previously developed by the authors, namely, geometrical perturbation-polarimetric notch filter (GP-PNF), to include single-pass interferometric information acquired by TanDEM-X. Interestingly, such enhancement makes it possible to employ the GP-PNF with single-polarization data as well. The proposed algorithms and their statistical behavior are tested on five Tandem-X dual-polarimetric HH/VV scenes acquired in the North Sea. The detection results are validated, exploiting the Automatic Identification System location of vessels. All of the new GP-PNF versions show good performance and provide larger vessel-sea contrast compared with single-channel detectors.

Precision agriculture research, which aims to monitor agricultural fields and to manage agricultural practice by considering overall environmental impacts, has gained momentum with the recent improvements in the remote sensing area. The objective of this letter, as a part of precision farming, is to implement Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale assignment in plant growth monitoring by means of SAR. The proposed approach copes with structural heterogeneity in agricultural fields by grouping together similar morphologies. For this, densely cultivated paddy rice fields are analyzed using TerraSAR-X (TSX) co-polar SAR data. For generating structurally similar groups, K-means clustering is used in a polarimetric feature vector space, which is composed of backscattering intensities and polarimetric phase differences. This step is followed by a preliminary classification approach based on the temporal separability of the explanatory parameters. In the last step of the proposed methodology, assigned classes are updated based on the biological principles that are followed in rice cultivation. This letter provides the results of the proposed algorithm and compares them to the standard threshold-based approach in two independent agricultural areas. The results show the superiority of the feature-clustering-based classification compared with the standard approach in handling field heterogeneity.

This paper examines the multifaceted effect of the effective spatial baseline, as expressed through the vertical (interferometric) wavenumber, on the inversion of forest height from polarimetric interferometric synthetic aperture radar (Pol-InSAR) data. First, the role of the vertical wavenumber in relating forest height to the interferometric (volume) coherence is introduced. Through the review of the forest height inversion from Pol-InSAR data, the effect of the vertical wavenumber on the inversion performance is evaluated. The selection of optimum with respect to forest height inversion performance, vertical wavenumbers is discussed. The impact of the acquisition geometry and terrain slopes on the vertical wavenumber and their consideration in the inversion methodology is addressed. The individual effects discussed are demonstrated by means of airborne repeat pass Pol-InSAR acquisitions in L- and P-band acquired over different forest conditions, including a boreal, a temperate, and a tropical forest test site. The achieved forest height inversion performance is validated against reference height data derived from airborne LIDAR acquisitions.

Ship detection is an important topic in remote sensing, and synthetic aperture radar (SAR) has a valuable contribution, allowing detection at nighttime and with almost any weather conditions. In addition, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information has been developed, namely, the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e., a constant false-alarm rate) and a likelihood ratio. The goodness of fit of the clutter pdf is tested with four real SAR data sets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, whereas the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdfs fit the data histograms, and they pass the two sample Kolmogorov-Smirnov and χ2tests.

In contrast to the random nature of synthetic aperture radar (SAR) data, it is also possible to identify bright targets whose scattering properties scarcely vary within imaging and time. These targets are commonly named point-like scatterers and can be found in both urban and natural environments. Permanent-scatterer interferometry techniques single out stable scatterers in a stack of SAR images, which preserve their backscattering stability along time. However, this methodology may not be optimum in natural scenarios, where the temporal stability of the scattering is rather reduced, or when the number of available SAR acquisitions is significantly small. Consequently, alternative methods have come out to detect stable scatters in a single SAR image, thus reducing all constraints related to their temporal behavior. Particularly, spectral diversity techniques are exploited to detect the so-called coherent scatterers. In this paper, a new detection scheme based on the generalized likelihood ratio test approach (GLRTA) is proposed, and its performance is extensively evaluated compared with three of the traditional methods, namely, the sublook coherence approach, the sublook entropy approach, and the phase variance approach. Remarkably, the GLRTA exploits both amplitude and phase information and does not need any further averaging (apart from sublooking processing with reduced signal bandwidth). The presented analysis is conducted both theoretically and with simulated data. For all scenarios, the new detector outperforms the other methods. The obtained results are validated also on real data. Finally, the proposed GLRTA is tested over different scattering scenarios, considering three TerraSAR-X acquisitions.

Infrastructure monitoring applications can require the tracking of slowly moving points of a certain structure. Given a certain point from a structure to be monitored, in the context of available SAR products where the image is already focused in a slant range - azimuth grid, it is not obvious if this point is the scattering center, if it is in layover or if it is visible from the respective orbit. This paper proposes a scattering center monitoring procedure based on refocusing a set of SAR images on a provided high-resolution DEM of the structure. The scattering centers of the refocused image are detected in the 4-D tomography framework by testing if the main response is at zero elevation in the local elevation-velocity spectral distribution obtained using the Capon estimator. The algorithm is validated on real data acquired with the TerraSAR-X satellite over the Puylaurent water dam in France during March-June 2012. The relative displacements between scattering regions show very good agreement with the in situ measurements.

One common method to estimate biomass is measuring forest height and applying allometric equations to get forest biomass. However, changing forest density or forest structure bias the known allometric relations. Remote sensing systems like SAR or LIDAR allow to measure vertical forest structure. In this paper the value of vertical forest structure information for biomass inversion is investigated. First, vertical biomass profiles are calculated from forest inventory data. Then, a vertical structure descriptor based on Legendre polynomials is suggested and its sensitivity to biomass is evaluated. In a second step, this descriptor is used to describe SAR vertical reflectivity profiles. Then, a biomass estimation algorithm is developed. This is a case study based on inventory data from the Traunstein test site, a temperate mixed forest, located in the southeast of Germany.

Paddy rice is a staple food that feeds more than half of the world's population. As such, monitoring paddy rice with Synthetic Aperture Radar (SAR) image is a critical area of research. Many possible measures of rice growth as canopy height, LAI, biomass and etc. are considered in the previous works. Among them, canopy height is the most direct measurement and has direct relationship with growth rate. In this study, to monitor paddy rice fields canopy heights are estimated by SAR images containing phase and amplitude information. These two inherent properties of SAR images are examined to retrieve canopy height by a canopy scattering and by a differential interferometric method at X-band. Accuracy analysis showed that differential interferometric technique gives very precise results in terms of canopy height if the canopy is fresh. However, in the case of dry canopy layer, the X-band canopy backscattering model gives much more precise results than the interferometric method as the X-band radar signals penetrate more into canopy in dry case. Even though interferometric techniques do not give detailed information about the physical structure of the canopy as backscattering model do, in this work it is shown that they can be used in operational monitoring.

SAR tomography as an extension to persistent scatterer interferometry (PSI) approaches has the potential to improve the level of detail of retrievable information, in particular, in the case of layover scenarios in urban areas. In this paper, a processing approach is sketched that eventually allows for retrieving a 3-D point cloud of point-like scatterers based on subsequent PSI and SAR tomography processing of an interferometric stack of high-resolution spaceborne TerraSAR-X data acquired over the city of Barcelona between the years 2008 and 2012. Experimental results are presented in the form of (1) vertical tomographic slices of high-rise buildings and (2) a 3-D point cloud of a larger district of the city of Barcelona retrieved from the tomograms.

L-band fully polarimetric SAR data, acquired within several airborne measurement campaigns from 2006 to 2013 over Germany, are used for soil moisture estimation at high spatial resolution and under distinct agricultural vegetation cover. The fully polarimetric L-band data were acquired by DLR's E-SAR and F-SAR sensors in order to investigate their potential for soil moisture retrieval. A newly developed, iterative, generalized, hybrid decomposition and inversion approach is applied for a flexible soil moisture estimation under a temporally and spatially varying agricultural vegetation. Therefore an iteration-adaptive, generalized scattering model of the vegetation is used. First results of the inverted soil moisture under vegetation cover reveal high inversion rates for the agricultural test sites (>95%). The inverted soil moistures are validated with in situ measurements from simultaneously conducted field campaigns leading to a minimum root mean squarer error (RMSE) of 4.1vol.% for a variety of crop types and in situ moisture conditions (ranging from 5vol.% to 40vol.%).

Ship detection is an important topic for security and surveillance of maritime and costal areas. A solution exploiting satellite-borne SAR sensors is particularly interesting, because it offers wide scale surveillance capabilities, which are not reliant on solar illumination and are rather independent of weather conditions ([1], [2], [3]). In SAR images, the main feature of a ship is a relatively large backscattering signal, which is usually brighter in comparison with the sea background. This led to the idea of using the intensity contrast as a feature to discriminate between targets and sea clutter. Several methodologies were proposed ([1], [3]). Most of these techniques set a statistical test between target and clutter background. Recently, the several ship detectors were proposed that exploits the property of SAR images to perform detection. In this work, two methodologies used for coherent scatterer detection are tested for the first time for ship detection and a comparison of ship detectors based on spectral analysis is performed over L-band ALOS date accompanied by a ground survey.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

Spaceborne synthetic aperture radars (SARs) represent a powerful tool to perform cryospheric observations due to their high spatial resolution and capability to acquire data during the winter time. Especially at lower frequencies, SAR signals penetrate beneath the glaciers surface through the shallow snow cover, interacting with surface as well as sub-surface features. This makes the scattering scenario very complex and the interpretation of SAR backscattering from glaciers and ice sheets not straightforward. In the case of polarimetric SARs (PolSAR), the understanding of polarization phase differences represent one of the main open issues. In this paper, a physical model is employed to relate co-polarization HH-VV phase difference (co-pol phase) to structural and dielectric properties of snow and firn which characterize the first meters of glaciers subsurface. Modelled co-pol phase values are compared to values measured in experimental airborne L-band (1.3 GHz) SAR data, acquired over the Austfonna ice-cap, in Svalbard, in spring 2005 and 2007. A set of ground measurements is also exploited to properly set up the model and to validate the results. Finally, an interpretation of co-pol phase temporal variations over the period 2005-2007 is attempted based on the proposed model.

The polarization diversity of the phase in differential interferometry causes ambiguities in the estimation of displacements: it might be related to complex dynamic processes such as vegetation growth. As the properties and possible origins besides noise-like influences of such diversity over changing vegetation canopies are virtually unknown, we propose to investigate them empirically using an L band data set covering one growing season over different agricultural crops. The polarimetric coherence regions (i.e. the shape and relation to biomass) over wheat, barley and, to a lesser extent, rape suggest the presence of birefringence within the canopy, which can yield phase differences between HH and VV channels exceeding 0.5π. The remaining fields (maize and sugar beet) - while also showing such a dependence on polarization - cannot be explained by birefringence. Irrespective of the origin of this dependence, its presence and magnitude indicate the importance of vegetation effects in differential interferometry, with potentially deleterious influences on the estimation of deformations.

Changes in soil moisture between the two radar acquisitions can impact the observed coherence 7 in differential interferometry: both correlation |γ| and phase φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves - upon calibration with L band data - median correlations ρ at HH of 0.77 for the phase Φ and of 0.50 for |γ|. The depth distribution of the scattering heterogeneities within the soil impacts the sensitvity of the observables to soil moisture changes in a way similar to a changing relative importance of the surface and the volume components, thus leading to similar qualities of fit when the latter is estimated. The first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals.

The second-order statistics are among the most important observables in synthetic aperture radar (SAR) polarimetry and are usually reported as covariance or coherence matrices. They are restricted to particular forms provided the target exhibits a certain kind of symmetry. As these constraints are not exactly fulfilled in real data, statistical tests are proposed for checking the validity of an invariance hypothesis. The application of these likelihood-ratio tests to airborne L-band data reveals a strong dependence of the test statistics on both the land cover and the number of looks; furthermore, temporal changes such as vegetation growth are evident: for example, lack of reflection symmetry for mature rape fields. This finding is at odds with commonly employed models, which assume (and predict) reflection invariance. For the Freeman-Durden decomposition, which relies on reflection symmetry, the connection between this invariance and negative powers (unphysical result) is found to be weak.

TanDEM-X and TerraSAR-X platforms form together the first spaceborne single-pass polarimetric interferometer in space. This allows, for the first time, the acquisition of spaceborne polarimetric synthetic aperture radar interferometry (Pol-InSAR) data without the disturbing effect of temporal decorrelation. This paper aims to assess the potential of such data for forest applications. For this, single- and dual-pol data acquired over a boreal, a temperate, and a tropical site were investigated to characterize X-band penetration and polarization diversity of the interferometric coherence measurements. Pol-InSAR forest height inversion schemes have been proposed and implemented for the singleand dual-pol cases and cross validated against LIDAR reference measurements for all sites. The single-pol inversion relies on an external ground digital terrain model (DTM) and performed well for all sites with correlation coefficients r2between 0.80 and 0.98. The dual-pol inversion does not require an external DTM but depends on the visibility of the whole forest layer. Accordingly, its performance varied with forest structure and season: The best performance was achieved for the summer acquisition of the boreal test site (r2= 0.86) and for the winter acquisition of the temperate test site (r2= 0.77). For the tropical test site, only a weak correlation (r2= ~0.50) could be established.

The possibility to detect changes in land cover with remote sensing is particularly valuable considering the current availability of long time series of data. Synthetic Aperture Radar (SAR) can play an important role in this context since it can acquire complete time series without limitations of cloud cover. Additionally, polarimetry has the potential to improve significantly the detection capability, allowing the discrimination between different polarimetric targets. This paper is focused on developing two new methodologies for testing the stability of observed targets (i.e., equiscattering-mechanism hypothesis) and change detection. Both the algorithms adopt a Lagrange optimization, which can be performed with two eigenproblems. Interestingly, the two optimizations share the same eigenvectors. Three statistical tests are proposed to set the threshold for the change detector. Two of them are mostly aimed at point targets, and one is more suited for distributed targets.

Persistent scatterer interferometry relies on the assumption that only one dominant scatterer is present per range-azimuth resolution cell. If this criterion is not met the point target candidate is discarded during the iterative processing sequence. This one-scatterer assumption contrasts with the fact that in urban scenarios layover is an ubiquitous phenomenon, and, therefore two or even more scatterers per resolution cell occur frequently. SAR tomography has the potential to support persistent scatterer interferometry in urban areas by providing a means to identify and separate two scatterers in elevation direction. In this paper, we explore an interferometric stack consisting of 25 ENVISAT/ASAR SLC images over Bucharest using SAR tomography approaches combined with interferometric point target processing. Elevation profiles are extracted using beamforming and truncated singular value decomposition focusing approaches.

A recently published hybrid decomposition and inversion approach is adapted and enlarged using a generalized scattering model of the vegetation volume for a refined and more flexible soil moisture estimation under a temporally and spatially varying agricultural vegetation cover. Fully polarimetric SAR data of DLR's E-SAR system at L-band are used as observation basis. The results for the AgriSAR campaign, carried out in 2006 within the Peene catchment, reveal a detailed inversion due to the pixel-based procedure and very high inversion rates (>95%) obtaining a gapless inversion along the entire growth cycle in strongly varying agricultural areas. The validation with in situ measurements for a plurality of summer and winter crops states a root mean square error of 4.55vol.%, while a wide moisture range (~2-30vol.%) is covered by the refined soil moisture inversion under vegetation using solely polarimetric SAR techniques.

Remote sensing has a large relevance in sea ice observation. In this context, Synthetic Aperture Radar may play a valuable rule due to the possibility to achieve high resolution images not reliant on solar illumination and relatively independent of weather conditions. This paper deals with the exploitation of polarimetry to solve some ambiguities that may rise in SAR observations due to the orientation angle of the target. The quad-polarimetric datasets exploited in this work were acquired in 2007 by the DLR E-SAR systems in L-band over Svalbard. It is showed that taking into account orientation effects has potentials to improve the final ice classification.

Ship detection with Synthetic Aperture Radar is a largely investigated topic and a series of operational algorithms are currently exploited by the users community. Polarimetry may play an important rule in this context, since the next generation of SAR satellites will be able to perform large swath polarimetric (either dual or compact) acquisitions. In this work, a comparison of promising ship detectors employing polarimetric information is performed over an ALOS-PALSAR quad-polarimetric dataset acquired over Tokyo Bay (October 2008). Interestingly, a survey of the area is available allowing to provide some quantitative comparison of the different detectors. The results show that the quad polarimetric detectors return better detection performance than the dualor single-polarimetric ones.

In this study recent advances in Synthetic Aperture Radar (SAR) polarimetry to observe sea oil slicks and man-made metallic targets are reviewed in the frame of the ESA funded PolSAR-Ap project. The most up-to-dated quad-pol approaches to observe sea oil slicks and metallic targets are reviewed and their performance is discussed against conventional single- and dual-pol approaches using actual L- and C-band Single Look Complex (SLC) SAR data. The unique benefits of quad-pol SAR data to observe both oil slicks and metallic targets are clearly shown.

In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. A RTT model capable of simulating scattering from a snow-covered vegetated terrain was developed and implemented. A sensitivity analysis to snow and vegetation parameters was carried out thus a comparison with real SAR data is presented in the paper.

The CoReH2O mission design is mature; there are two viable technical configurations, and the breadboards of key hardware components, such as the dual-polarized antenna array feeds and high power amplifiers have been built and tested. The baseline retrieval algorithm has been intensively tested using simulated and experimental data. The tests confirm that the threshold performance for SWE products can be met under a wide range of snow conditions. Data from further field campaigns by the airborne SnowSAR acquired Austria, Canada, and Alaska from November 2012 through April 2013 are being analysed to investigate retrieval performance over alpine, glacier and tundra terrains.

Different mechanisms for the impact of soil moisture on interferometric radar data have been proposed, but its magnitude, sign and even presence have barely been studied empirically and thus remain poorly understood. In this study the dependence of the phase and coherence magnitude on soil moisture was inferred empirically with regression techniques: this was done for two airborne data sets at L band. The phase dependence was significant (α = 0.05) for more than 70% of the fields at HH polarization, its sign corresponding to an increase in optical path upon wetting. This trend was similar in both campaigns, whereas the prevalence of soil moisture-related decorrelation differs. These results are only consistent with a dielectric origin of the soil moisture effects, and not with soil swelling or the penetration depth hypothesis.

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multi-angular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6-8 vol.% including all test fields with a variety of crop types.

In the recent years, there has been increased interest in using synthetic aperture radar (SAR) to study and monitor glaciers and ice sheets for glaciological and climate change research. This paper describes the estimation of ice extinctions through the modeling of polarimetric interferometric SAR (Pol-InSAR) coherences as a combination of a surface contribution (from the snow-firn interface and wind-induced sastrugi features) and a volume response. Ground-to-volume scattering ratios derived from a novel polarimetric decomposition are used in conjunction with Pol-InSAR coherence magnitudes to invert the extinction of the ice layer. The inversion is performed for experimental airborne Pol-InSAR data at L-band and P-band acquired by the German Aerospace Center's (DLR) E-SAR system over the Austfonna ice cap in Svalbard, Norway, as part of the 2007 IceSAR campaign. Extinction dependences on frequency and glacier facie are investigated, and validation is performed comparing ground-penetrating radar data to SAR backscatter and extinction values. Best extinction results are obtained at shallow incidence angles with small wavenumbers and for low ground-to-volume scattering ratios. For swath areas in mid range to far range, accuracies of 25% in extinction are anticipated when averaging over 100 effective looks for a four-baseline inversion constraining solutions to vertical wavenumbers of 0.01 ≤kz≤ 0.1. To allow inversion using single-baseline Pol-InSAR, the proposed model is of limited complexity. Suggested extensions for a more realistic scattering scenario include incorporating multiple englacial ice scattering layers and improving the way multiple baselines are combined.

High spatial resolution X-band interferometric SAR data from the TanDEM-X, in the operational DEM generation mode, are sensitive to forest structure and can therefore be used for thematic boreal forest classification of forest environments. The interferometric coherence in absence of temporal decorrelation depends strongly on forest height and structure. Due to the rather homogenous structure of boreal forest, forest biomass can be derived from forest height, on the basis of allometric equations with sufficient accuracy to be used for thematic classification applications. Two test sites in mid- and southern Sweden are investigated. A maximum of 4 biomass classes, up to 250 Mg/ha, are achieved. Larger spatial baselines result in better classification performances.

Polarimetric Synthetic Aperture Radar (PolSAR) images are widely used for agricultural fields monitoring and change detection applications due to their all-weather acquisition possibilities and inherent properties including phase and amplitude information. The techniques used for such temporal applications can be cast in two groups: polarimetric (incoherent) and polarimetric-interferometric (coherent) being represented in this work by the KL-distance and the Mutual Information, respectively. The goal of this work is to characterize these two kinds of different information sources in terms of ground measurement parameters of the agricultural fields, and to figure out the relationship between temporal trends of the similarity measures versus temporal trends of the physical parameters without dealing with inverse problems. For this purpose multi-temporal fully polarimetric SAR images, acquired in the frame of the AgriSAR 2006 campaign with synchronous ground surface measurements over a whole vegetation period are analyzed.

With the enhancing availability and variety of space borne Synthetic Aperture Radar (SAR) data and a growing number of analysis algorithms the need for a vital user community is increasing. Therefore the German Aerospace Center (DLR) together with the Friedrich-Schiller-University Jena (FSU) and the Technical University Munich (TUM) launched the education initiative SAR-EDU. The aim of the project is to facilitate access to expert knowledge in the scientific field of radar remote sensing. Within this effort a web portal will be created to provide seminar material on SAR basics, methods and applications to support both, lecturers and students.

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. Recently, a new PolSAR tracking algorithm has been proposed for glacier surface velocity monitoring. The proposed polarimetric tracking method applies Mutual Information (MI) to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. In this paper, its implementation of interest will be investigated.

In this contribution, tomographic imagery obtained from estimating the 3-D localization and the polarimetric signature of backscattering sources within a forest environment from fully-polarimetric multibaseline SAR data at L- and P-bands are presented. Both, polarization diversity and spatial diversity were exploited jointly within the tomographic focusing to estimate the targets' localization and polarimetric signature. We thereby extend our recently proposed time-domain back-projection (TDBP)-based tomographic focusing approach. The tomographic slices obtained from polarimetric spectral estimation using MLBF, Capon, and MUSIC are opposed to the results previously obtained by tomographic focusing of the individual polarization channels. The results are briefly discussed with respect to potential advancements towards more reliable estimation of forest parameters from SAR tomography data.

Soil moisture retrieval under agricultural vegetation is assessed by a hybrid decomposition and inversion algorithm using fully polarimetric SAR data of DLR's E-SAR system at L-band. The results for the AgriSAR and SARTEO campaigns, conducted in 2006 and 2008 within the Peene and the Rur catchment, reveal a very high inversion rate leading to a spatially continuous inversion along the entire growth cycle in agricultural areas. The validation with in situ measurements for a variety of summer and winter crops states a root mean square error of 6.25vol.% and 5.77vol.% respectively, while a wide moisture range (~2-30vol.%) is covered by the soil moisture inversion under vegetation.

The extensive field data base of the TERENO project is utilized to validate inversion results for soil moisture under vegetation within two terrestrial observatories (Central German Lowland Observatory and Lower Rhine Valley Observatory). The recently developed hybrid decomposition and inversion approach is applied to L-band fully polarimetric SAR data acquired within the TERENO campaign of 2011 by DLR's novel high resolution F-SAR sensor in order to investigate its potential. The soil moisture results state a spatially continuous inversion under vegetation with a RMSE between 3.2vol.% and 8.2vol.% for both observatories. Therefore the results are particularly suited for local analyses on the field level in contrast to soil moisture products with low resolution from passive microwave sensors on the catchment level.

Temporal decorrelation is a critical issue for a successful Pol-InSAR inversion in case of repeat-pass SAR data, as provided by conventional satellite or airborne SAR systems. This paper proposes estimation and compensation of temporal decorrelation effects by using multi-baseline Pol-InSAR data. A new approach to quantify different temporal decorrelation levels (one for volume and the other for the ground layer) is performed without resort to the special case of zero spatial baseline interferograms. Both temporal decorrelation coefficients were separately estimated at temporal baselines ranging from 1 to 15 days and compared to height inversion errors caused by them.

The combination of interferometry and polarimetry (PolInSAR) is a promising candidate for estimating the snow volume using the Random Volume over Ground model (RVoG). The model was originally developed for forest height determination using L-band [1]. The adaption of the RVoG model to snow is not straight forward due to the thin volume of snow and the highly varying penetration depth of X-band microwaves. In this paper we show parameter ranges and recommendation where the RVoG model might work, together with results of interferometry (InSAR) and polarimetric SAR (Pol-InSAR) using TerraSAR-X and TanDEM-X SAR acquisitions.

This paper is focused on the polarimetric change detector (PCD) based on perturbation filters already developed by the authors. The highlight of the PCD is its independence of the overall amplitude of the coherence matrix (scaling factor), focusing exclusively on the polarimetric information. Here, the relationship between the PCD and the polarimetric and interferometric (Pol-InSAR) coherence is investigated. Many Pol-InSAR algorithms are based on the coherence operator, therefore such parameterisation would ease the process of setting the detector threshold. The methodology is tested using E-SAR AgriSAR, L-band quad polarimetric data, showing agreement with the observed changes and the actual interferometric coherence.

Detection of icebergs is a major topic for surveillance of polar maritime areas and coastlines. Synthetic Aperture Radar (SAR) has been shown to be particularly useful because of its all-weather and night capability. In this paper a methodology based on the polarimetric perturbation analysis is presented. The algorithm can be considered to be a negative filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected and considered as targets (i.e. ships, buoys, icebergs). In this work the notch filter is focused on icebergs detection. A test with TerraSAR-X quad polarimetric data was performed over a scene acquired on the Canadian coastal area (Northwest Passages). The detections showed promising results, with identification of areas where large and small icebergs appear, but also ridges seems to be detected.

The use of polarimetric and interferometric SAR (Pol-InSAR) has been demonstrated to bring significant advantages for several remote sensing applications. Sometimes, the hypothesis that the scattering mechanisms (SM) do not suffer large changes in the two acquisitions is performed (ESM). On the other hand, a different set of algorithms should be employed. In this paper a new methodology is proposed to identify errors consequence of possible changes. The algorithm performs a Lagrange optimization of an error factor of the Pol-InSAR coherences, returning also the SM that change maximally and minimally during the two acquisitions. The algorithm was tested on E-SAR L-band data acquired during the AgriSAR 2006 campaign.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture by fused active and passive L-band microwave measurements. With an airborne campaign conducted in the Rur catchment, Germany, in 2012, an active and passive L-band microwave data set is generated. The passive Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were installed on the DLR Dornier DO 228 aircraft. Despite of the differences between the airborne and the future spaceborne sensors, the data set will serve as a test bed for the analysis of existing and development of enhanced future active/passive data fusion techniques. Moreover, the derivation of (vegetation) parameters for the fusion approaches is planned.

Forest characterization and biomass estimation by means of remote sensing systems are nowadays “hot topics” within the remote sensing community, given their importance in the terrestrial carbon budget. In fact, forest vertical structure is a key variable for assessing biodiversity and structural degradation and/or regeneration. Moreover, the (vertical) structure information is important as it can allow the development of accurate and robust (alometric) estimators of the forest biomass. In this paper, potentials and challenges of forest vertical structure estimation with low frequency multibaseline polarimetric synthetic aperture radar are reviewed and discussed.

In recent years an increased interest arose in using synthetic aperture radar (SAR) to study and monitor land ice for glaciological applications and climate change research. At long wavelengths, SAR systems can penetrate into glacier ice for several tens to hundreds of meters. This makes them sensitive to near-surface as well as deeper ice volume features. This paper investigates the performance of a volume scattering component, in the course of an electromagnetic (e.m.) model development, to relate Polarimetric SAR (PolSAR) signatures to glacier facie. Indeed, PolSAR ice signatures are still poorly understood, including the importance of scattering from the glacier ice volume and its dependency on frequency and incidence angle. A comparison is performed with airborne Pol-SAR data at L- and P-band collected by DLR's E-SAR system over the Austfonna ice cap in Svalbard, Norway, within the ICESAR campaign.

The interest in using synthetic aperture radar (SAR) to study snow properties has increased in the last years. Snow properties and snow cover change monitoring on global scale are needed for a better understanding of the role of the cryosphere in the climate system [1]. Snow plays also an important role in biogeochemical cycles since it is a basic water resource for many densely populated areas of the planet. In this sense, Snow Water Equivalent (SWE) is the primary parameter to be retrieved. The Cold Regions Hydrology High-resolution Observatory (CoReH2O) satellite mission, selected by the ESA for feasibility studies within the Earth Explorer Programme, is expected to satisfy this need [2]. In this work, the capabilities of the proposed CoReH2O models for SWE (and snowflakes size) retrieval are investigated using TerraSAR-X dual-pol data. Ground measurements from the NoSREx campaign are used for validation.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission was selected for detailed scientific and technical studies within the Earth Explorer Programme of ESA. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations The mission will deliver spatially distributed snow and ice observations to improve the representation of the croysphere in hydrological and climate models. Primary parameters are the extent and water equivalent (SWE) of the snow pack and snow accumulation on glaciers. Scientific preparations of the mission include the development and testing of algorithms for retrieval of snow parameters, studies on synergy of CoReH2O-type snow products with passive microwave measurements, the assimilation of satellite snow data in process models, and field experiments. Performance of retrievals for snow extent and SWE was tested with simulated and experimental data, including Ku- and X-band SAR images of the airborne SnowSAR system.

Snow extent and water equivalent (SWE) on land and snow accumulation on glaciers are the main parameters to be delivered by the Cold Regions Hydrology High-resolution Observatory (CoReH2O) satellite. Detailed scientific and technical studies for the mission are going on within the Earth Explorer Programme of ESA. The CoReH2O sensor is a dual frequency SAR, operating at 17.2 and 9.6 GHz, VV and VH polarizations. A main task for mission preparation is the development and validation of algorithms for retrieval of snow parameters. A constrained minimization approach is proposed for SWE retrieval, matching backscatter computed with a radiative transfer model and measurements in the four SAR channels by iterating for SWE and snow grain size. The algorithm was validated with simulated and measured backscatter data. The tests confirm the feasibility of the retrieval approach and help to quantify the requirements for statistical background information which is needed for constraining the solution.

The identification of scatterers having a point-like behavior in SAR images has become an important topic with a wide range of applications, from calibration to information extraction. So far, three different approaches are available for the Coherent Scatterer (CS) detection, based on spectral correlation techniques using only a single SAR image. In this paper, a new approach based on the generalized likelihood ratio test (GLRT) is introduced. An extensive performance analysis using the different detection methods is presented, comparing the new proposed method with the previous ones. Lastly, the new approach is tested using real data.

This study used high resolution images as a ground measurement tool for a region, where mapping of spatial and temporal vegetation cover variation by ground survey is impossible. Leaf Area Index (LAI) maps were derived from DigitalGlobe or GeoEye images and resampled to 30m resolution. Landsat TM imagery was used to produce NDVI maps. The NDVI from TM and LAI from high resolution images show good correlation for the three test sites chosen in the lower Tarim river basin. The correlations were then used to produce LAI maps on river basin scale from Landsat TM imagery. The results showed that this method provides good local calibration for converting NDVI maps from Landsat TM imagery to LAI maps to be used in hydrological modeling.

A set of three quad-pol images acquired at the L-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in parallel with the AgriSAR2006 campaign, has been used to provide, for the first time with airborne data, a demonstration of the retrieval of vegetation height from agricultural crops by means of polarimetric SAR interferometry (PolInSAR)-based techniques. Despite the low frequency of the data, hence providing a weak response from the vegetation volume in contrast to the ground, accurate estimates of vegetation height at field level have been obtained over winter rape and maize fields. The same procedure does not yield valid estimates for wheat, barley, and sugar beet fields due to a mismatch with the physical model employed in the inversion and to the specific crop condition at the date of acquisition. These results show the value of the information provided by both interferometry and polarimetry for some agriculture monitoring practices.

The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. In this paper, a new PolSAR change detector which makes use of Kullback-Leibler divergence is described. Kullback-Leibler divergence (KL-divergence) measures the amount of information in common between coherent temporal multi-channel polarimetric SAR acquisitions. Although in this paper KL-divergence measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and interferometric images. KL-divergence measure is also simplified to give an explicit expressions for temporal single channel SAR images. The new results are simple, easy to be used, and superior in providing detailed structure.

Tandem-X successfully launched in June 2010 forms with TerraSAR-X the first single-pass (single- dual- and quad-) polarimetric interferometer in space. This allows for the first time the acquisition and analysis of Pol-InSAR data in X-band from space without the disturbing effect of temporal decorrelation. In this paper TerraSAR-X TanDEM-X datasets are analyzed using Pol-InSAR techniques [1][2] in order to demonstrate the potential of the TanDEM-X mission for forest parameter inversion and forest characterisation. For this purpose dual-polarimetric (HH/VV) and single-pol datasets (HH) acquired during the pursuit monostatic and the bistatic commissioning phase are used. Estimated forest heights are validated against ground and airborne LIDAR measurements.

In this paper, we propose a terrain categorization algorithm for single-polarization high-resolution SAR imagery, and coded the terrain categories by colors that resemble Pauli decomposition color coding based on scattering mechanisms of PolSAR data. The proposed algorithm emphasizes preserving high resolution and making information dissemination easier for single-pol SAR data.

Tandem-L is a mission proposal for an innovative interferometric L-band radar instrument that enables the systematic monitoring of dynamic Earth processes using advanced techniques and technologies. The mission is science driven aiming to provide a unique data set for climate and environmental research, geodynamics, hydrology and oceanography. Important application examples are global forest height and biomass inventories, measurements of Earth deformation due to tectonic processes and/or anthropogenic factors, observations of ice/glacier velocity field and 3-D structure changes, and the monitoring of soil moisture and ocean surface currents. The Tandem-L mission concept consists of two cooperating satellites flying in close formation. The Pol-InSAR and repeat-pass acquisition modes provide a unique data source to observe, analyse and quantify a wide range of mutually interacting processes in the bio-, litho-, hydroand cryosphere. The systematic observation of these processes benefits from the high data acquisition capacity and the novel high-resolution wide-swath SAR imaging modes that combine digital beamforming with a large reflector antenna. This paper provides an overview of the Tandem-L mission concept and its main application areas. It is planned to realise the Tandem-L mission in cooperation with NASA/JPL. The mission concept was developed in detail in a joint two-year pre-phase A study and it will be further studied in the next 18 months. This will allow a cost-effective implementation, whereby each partner contributes its predevelopments and experience. According to current planning, the Tandem-L satellites could be launched in 2019.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission has been selected for detailed scientific and technical studies within the ESA Living Planet Programme. The mission addresses the need for distributed snow and ice observations to improve the representation of the cryosphere in climate models and the prediction of the water cycle. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving snow mass (SWE) and other physical properties of snow and ice. A major task for mission preparation is the development and testing of algorithms for SWE retrieval. The baseline algorithm applies a constrained minimization approach, matching forward computed and measured backscatter by iterating for SWE and effective grain size of the snow volume. Experimental campaigns on Kuand X-band backscatter of snow deliver important information for retrieval development and validation.

This paper investigates the detection of Coherent Scatterers (CSs) in ice and glacier terrain by means of TerraSAR-X time series data in the test site of the Helheim Glacier in Greenland. CSs are evaluated with respect to detection and potential applications. Applying optimized detection scheme can be useful for retrieving information about the glacier movement using time series data. Finally, some conclusions about the temporal stability of natural CSs are obtained by analyzing the rocky area around the glacier.

In recent years, there has been increased interest in using synthetic aperture radar (SAR) to study and monitor glaciers and ice sheets for glaciological and climate change research. However, due to the medium's complexity, SAR backscattering from ice remains poorly understood, including the relative importance of scattering from surface and volume layers and also dependences on frequency and glacier zone. Extreme weather conditions can result in quickly changing surface conditions influencing backscatter signatures while leaving the underlying volume of interest unchanged. Surface and volume components must thus be separated in order to infer information regarding the properties of the ice volume. This paper describes a three-component scattering model to decompose polarimetric SAR (PolSAR) images of glacier ice. Total backscatter is modeled as the incoherent summation of surface, volume, and sastrugi (wind-induced feature) components. The proposed model adapts and extends the Freeman and Durden decomposition for an ice volume scenario in which the volume is a dielectric medium. Forms of the model for both random and oriented volumes are considered, and a new oriented sastrugi component is introduced which is able to explain backscatter behavior between different winter scenes. Validation is performed with airborne PolSAR data at L- and P-band collected using the E-SAR system of the German Aerospace Center over the Austfonna ice cap in Svalbard, Norway, as part of the ICESAR campaign.

SIGNAL is an innovative earth exploration mission proposal with the main objective to estimate accurately and repeatedly topography and topographic changes associated with mass change or other dynamic effects on glaciers, ice caps and polar ice sheets. Elevation measurements are complemented with glacier velocity measurements, providing valuable additional information for a better understanding of the hydrology of glacierized basins and of the Arctic and Antarctic water cycle. SIGNAL is capable of monitoring all critical regions with a high spatial resolution and an adequate revisit time. This paper gives an overview about the actual mission design status and provides a brief description of the topography (DEM - digital elevation map) self-calibration strategy and the estimated global interferometric performance.

In this paper, the first detection result of coherent scatterers (CSs) in forest environment is addressed. As a scatterer associated with CS, the dihedral structure consists of the tree-trunk like a vertical cylinder and the ground surface is assumed. The potential of CSs as being the phase stable scatterers as permanent scatterers will be also demonstrated with P-&L-band Pol-InSAR datasets acquired in repeat-pass InSAR mode over boreal forest test site by German Aerospace Center's E-SAR airborne system.

Tandem-L is a proposal for an innovative interferometric and polarimetric radar mission that enables the systematic monitoring of dynamic processes on the Earth surface. Important mission objectives are global forest height and biomass inventories, large scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The innovative mission concept and the high data acquisition capacity of Tandem-L provide a unique data source to observe, analyze and quantify the dynamics of a wide range of mutually interacting processes in the bio-, litho-, hydro- and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics.

This paper presents an overview of single-pass interferometric Synthetic Aperture Radar (SAR) missions employing two or more satellites flying in a close formation. The simultaneous reception of the scattered radar echoes from different viewing directions by multiple spatially distributed antennas enables the acquisition of unique Earth observation products for environmental and climate monitoring. After a short introduction to the basic principles and applications of SAR interferometry, designs for the twin satellite missions TanDEM-X and Tandem-L are presented. The primary objective of TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is the generation of a global Digital Elevation Model (DEM) with unprecedented accuracy as the basis for a wide range of scientific research as well as for commercial DEM production. This goal is achieved by enhancing the TerraSAR-X mission with a second TerraSAR-X like satellite that will be launched in spring 2010. Both satellites act then as a large single-pass SAR interferometer with the opportunity for flexible baseline selection. Building upon the experience gathered with the TanDEM-X mission design, the fully polarimetric L-band twin satellite formation Tandem-L is proposed. Important objectives of this highly capable interferometric SAR mission are the global acquisition of three-dimensional forest structure and biomass inventories, large-scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The sophisticated mission concept and the high data-acquisition capacity of Tandem-L will moreover provide a unique data source to systematically observe, analyze, and quantify the dynamics of a wide range of additional processes in the bio-, litho-, hydro-, and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics. Enabling technologies and techniques are described in detail. An outlook on future interferometric and tomographic concepts and developments, including multistatic SAR systems with multiple receivers, is provided.

In this paper first results of a sensitivity study using dual polarimetric TerraSAR-X data for ice thickness estimation are presented. The sea ice thickness reference data set was measured, coincident to the SAR data take, by means of a helicopter-borne EM sounding device on April 28, 2008 in the Russian Arctic. For some of the signatures, namely the complex correlation coefficient, a relation to ice thickness could be found that is theoretically predicted for L-band SAR. The first results show, that the new generation of polarimetric space borne SAR sensors like TerraSAR-X may open a new opportunity for thin sea ice thickness monitoring from space.

DLR is currently studying a space borne mission based on two L-band satellites to map Earth surface deformation and vegetation structure from space. In this study the scientific requirements for deformation measurements are collected, traded off versus technical feasibility and a mission concept is investigated that provides a global monitoring capability of geo-tectonic threats.

In this paper a modified three component polarimetric decomposition incorporating multi-angular acquisitions is developed to estimate soil moisture under vegetation cover over agricultural areas. The approach is applied on fully-polarimetric L-band data acquired by DLR's airborne E-SAR sensor in the frame of the OPAQUE campaign conducted in May 2008 in the Weißeritz catchment area, near Dresden, Germany. The results for the estimated soil moisture from the overlapping area of the flight strips demonstrate a significant increase of the inversion rate, if more than one acquisition is used. The inverted soil moisture values are validated against in situ measurements for five test fields with different crop types resulting in an RMSE of approximately 7vol.% for different incidence angle constellations. Finally the results show how topographic effects in the soil moisture retrieval can be compensated by multi-angular constellations.

This paper presents the impact of simulation parameters according to the potential future space-borne mission BIOMASS (P-band) in polarimetric and interferometric SAR (Pol-InSAR) inverison performance. Forest height inversion is obtained from the simulated data sets (bandwidth, NESZ and ambiguities) generated from DLR E-SAR (Experimental Synthetic Aperture Radar) airborne SAR data. For this study two campaign data sets (BioSAR 2007 / INDREX-II) have been selected and investigated. The several comparative results of Pol-InSAR between airborne data and the simulated data (incl. bandwidth, NESZ and range/azimuth ambiguities) will be shown and discussed.

The objective of this work is to investigate the coherent co-polarized behavior of rice plants during the growing stages and to explore their information content for rice monitoring at high frequencies recently available through new SAR satellite missions. Time series of dual-pol TerraSAR-X images have been acquired during the whole cultivation period over a rice site in Spain. Among different observations, the backscattering coefficients at HH and VV channels and the HH/VV ratio have confirmed to show a temporal variation that has a significant correlation with the development of the plants during the vegetative and reproductive phenological phases. In addition, the information content of the HHVV complex coherence and a dual polarimetric target decomposition is investigated and discussed. All the information layers investigated are contributing to the discrimination of rice fields from other crop types. Finally, interferograms computed with pairs of successive images (11 days separation) have been preliminary tested.

The objective of this paper is to make a review of the current status of the PolSARpro v4.0 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by a consortium comprising I.E.T.R at the University of Rennes 1, AELc, DLR-HR and Dr mark Williams from Adelaide. The objective of this current project is to provide Educational Software that offers a tool for self-education in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v4.0 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

Following the success of the first bistatic spaceborne-airborne experiment between TerraSAR-X and F-SAR carried out in November 2007, DLR has performed a second bistatic experiment in July 2008 with new challenging acquisitions. Furthermore, the existing bistatic processing chain has been updated with two significant improvements: a) clock offset synchronisation is now performed without the use of reference targets, and b) SAR imaging is done using a fast focussing technique. The new SAR imaging algorithm, based on the fast factorised backprojection algorithm, has proved very good focussing qualities while dramatically reducing (up to a factor 100 with respect to direct backprojection) the overall computational load. The new processing chain is tested using the image of the first TerraSAR-X experiment. Results of a dualpol acquisition performed during the second TerraSAR-X/F-SAR experiment and showing the first dual-pol bistatic spaceborne-airborne images are also presented in this paper.

In this paper, the potential of using polarimetric SAR (PolSAR) acquisitions for the estimation of volumetric soil moisture under agricultural vegetation is investigated. Soil-moisture estimation by means of SAR is a topic that is intensively investigated but yet not solved satisfactorily. The key problem is the presence of vegetation cover which biases soil-moisture estimates. In this paper, we discuss the problem of soil-moisture estimation in the presence of agricultural vegetation by means of L-band PolSAR images. SAR polarimetry allows the decomposition of the scattering signature into canonical scattering components and their quantification. We discuss simple canonical models for surface, dihedral, and vegetation scattering and use them to model and interpret scattering processes. The performance and modifications of the individual scattering components are discussed. The obtained surface and dihedral components are then used to retrieve surface soil moisture. The investigations cover, for the first time, the whole vegetation-growing period for three crop types using SAR data and ground measurements acquired in the frame of the AgriSAR campaign.

This paper addresses the potential and limitations of polarimetric synthetic aperture radar (SAR) interferometry (Pol-InSAR) inversion techniques for quantitative forest-parameter estimation in tropical forests by making use of the unique data set acquired in the frame of the second Indonesian Airborne Radar Experiment (INDREX-II) campaign - including Pol-InSAR, light detection and ranging (LIDAR), and ground measurements - over typical Southeast Asia forest formations. The performance of Pol-InSAR inversion is not only assessed primarily at L- and P-band but also at higher frequencies, namely, X-band. critical performance parameters such as the ldquovisibility of the groundrdquo at L- and P-band as well as temporal decorrelation in short-time repeat-pass interferometry are discussed and quantitatively assessed. Inversion performance is validated against LIDAR and ground measurements over different test sites.

In this paper the focus is given to the soil moisture retrieval under a vegetation cover, exemplarily for two crop types winter wheat and rape. For this Pol-SAR data over a whole vegetation period were used acquired in the frame of the AGRISAR campaign in 2006 at L-band. The potentials and limitations over time are investigated by modifications of the model based Freeman decomposition in order to decompose the scattering contributions and to invert the surface and dihedral scattering component for soil moisture estimation. The applied decomposition approaches for agricultural land surfaces are suitable, but exhibit deficiencies in modeling the volume component, even though some improvements due to the modifications could be presented. In the end the soil moisture estimation does not perform constantly well over the whole acquisition period due to the complexity of the crop vegetation.

This paper explores the potential to estimate soil moisture in the presence of vegetation using differential SAR interferometry and polarimetry. It is well known that a change in soil moisture changes the penetration depth of the electromagnetic wave, hence inducing an "artificial" change in the observed phase center. The presence of vegetation, or the vegetation vitality itself, can also affect the location of the observed phase center. Finally, a change in the polarimetric signature of the scatterer can also induce a phase center change. The purpose of this paper is to make a first study concerning the possibility to separate those contributions using differential SAR interferometry together with SAR polarimetry. Exemplary results are shown with data acquired by the airborne E-SAR system of DLR in the frame of the AGRISAR campaign.

The objective of this paper is to make a review of the current status of the PolSARpro v3.3 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by a consortium comprising I.E.T.R at the University of Rennes 1, AELc, DLR-HR and Dr mark Williams from Adelaide. The objective of this current project is to provide Educational Software that offers a tool for selfeducation in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v3.3 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

This paper comes within the context of exploring the potential of bistatic Pol-InSAR. Numerical simulations are carried out by means of MIPERS, multistatic interferometric polarimetric electromagnetic model for remote sensing which has already proved many abilities. The latter involves a electro-magnetic coherent approach of a discrete natural media description and enables us to model Pol-InSAR observables over forests in the bistatic configuration. Taking into account bistatic SAR constrains in terms of resolution, critical base-line, SNR, range and height ambiguities, new configurations for Pol-InSAR applications are envisioned.

Synthetic aperture radar (SAR) interferometric data offer the opportunity to measure temperate glacier surface topography and displacement. The increase of the resolution provided by the most recent SAR systems has some critical implications. For instance, a reliable estimate of the phase gradient can only be achieved by using interferogram local frequencies. In this paper, an original two-step method for estimating local frequencies is proposed. The 2-D phase signal is considered to have two deterministic components corresponding to low-resolution (LR) fringes and high-resolution (HR) patterns due to the local microrelief, respectively. The first step of the proposed algorithm consists in the LR phase flattening. In the second step, the local HR frequencies are estimated from the phase 2-D autocorrelation function computed on adaptive neighborhoods. This neighborhood is the set of connected pixels belonging to the same HR spatial feature and respecting the ldquolocal stationarityrdquo hypothesis. Results with both simulated TerraSAR-X interferograms and real airborne E-SAR images are presented to illustrate the potential of the proposed method.

Polarimetric SAR interferometry (Pol-InSAR) is a powerful remote sensing method for forest height estimation by using the random volume over ground model (RVoG). At higher frequencies implementation of forest height estimation in X band is limited to less dense and low forest types where X band is able to penetrate through the volume to the ground. However, the penetration depth at X band is insufficient to cover all forest types. In the paper, forest height inversion at X band using two different approaches is demonstrated with focus on the impact of extinction on forest height estimation.

This paper highlights the potential to measure temperate glacier velocities and surface characteristics by airborne interferometric and polarimetric SAR remote sensing. Indeed, a novel SAR airborne campaign took place in October 2006 over two Alpine glaciers. Simultaneously to the acquisition of repeat pass interferometric, polarimetric and multi-band data, in-situ measurements were carried out to provide useful information for the SAR synthesis, for backscattering analysis and for performance assessment. Analysis of the experimental data as well as early PolInSAR processing results regarding information extraction are presented.

Land surface process modelling might be limited due to lack of reliable model input data. Key surface variables as land cover information or soil moisture conditions have been proven to be observable by remote sensing systems. The integration of remote sensing data into land surface process models might therefore help to improve their simulations results. Longer wavelength SAR data has a higher sensitivity to soil moisture content than higher frequency systems. Recent (ALOS) and planed (e.g. TerraSAR-L) SAR systems are therefore expected to provide valuable information about soil moisture dynamics. The present study investigates the potential to retrieve land cover information and geophysical parameters from L-band SAR data. The retrieval results are assimilated into a state-of-the-art land surface model to evaluate the merit of L-band SAR data assimilation.

The launch of JAXA's ALOS in January 2006 provides - for the fist time since the SIR-C/X-SAR mission's in the 80's - the opportunity to acquire Pol-InSAR data from space. Indeed, PalSAR (i.e. the SAR instrument onboard of ALOS) is able to operate in a quad-pol mode - declared by JAXA as an "experimental mode" - that allows the acquisition of Pol-InSAR data in a repeat- pass mode. In this sense, ALOS-PalSAR allows the application, validation and development of Pol-InSAR inversion techniques on a much wider range of sites distributed world-wide and accessible to a much wider scientific user community than possible with airborne sensors. In this paper we present the analysis of fully- and/or dual-polarimetric and interferometric data sets acquired by ALOS/PalSAR during its CAL-VAL and operational data acquisition phase and discuss the potential and the limitations for different polarimetric interferometric applications. More developed applications as forest height estimation from single- and multi-baseline Pol-InSAR ALOS-PalSAR data inversion - addressed in the frame of JAXA's Karbon & Kyoto initiative - as well as novel applications regarding soherent scatterer's detection and interpretation in urban environments are discussed. The impact of critical mission and operation design parameters as spatial coverage, repeat-pass time, observation scenario, and orbit control are evaluated and discussed.

The last decade has seen an increasing demand for accurate mapping and wide-coverage monitoring of glaciers and ice sheets in order to measure and predict their response to global climate change and their contribution to sea level rise. This in turn requires a more complete understanding of their properties including topography, accumulation rates and vertical profiles. One promising new technique for vertical profile reconstruction using polarimetric interferometric SAR (Pol-InSAR) data is polarization coherence tomography (PCT) and for the first time, PCT is adapted here to a glacier scenario. The inversion algorithm to reconstruct vertical ice profiles is applied to both simulated data to assess its accuracy and sensitivity to input parameters, and to airborne Pol-InSAR data at L- and P-band and InSAR data at X-band collected using DLR's E-SAR system over the Austfonna ice cap in Svalbard, Norway.

In this letter, we present results from the FinSAR project, where the E-SAR and Helsinki University of Technology Scatterometer (HUTSCAT) instruments were operated together in order to validate tree-height retrieval algorithms for boreal forest. The campaign was carried out in Finland in fall 2003. The main instruments of the campaign were the E-SAR airborne radar (operating at L- and X-band) and the HUTSCAT helicopter-borne profiling scatterometer (operating at X- and C-band). We compare and discuss forest height obtained from the inversion quad-pol polarimetric interferometric synthetic aperture radar (SAR) data sets at L-band and forest height obtained from the inversion of single-pol X-band in SAR data with forest height estimates from HUTSCAT scatterometer data. Our results show that the forest height values, which are estimated by means of two different radar instruments, are in good agreement. The correlation between HUTSCAT and E-SAR height estimates ( at L-band and at X-band) underlines the good agreement between the results obtained by the two approaches.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an innovative spaceborne radar interferometer that is based on two TerraSAR-X radar satellites flying in close formation. The primary objective of the TanDEM-X mission is the generation of a consistent global digital elevation model (DEM) with an unprecedented accuracy, which is equaling or surpassing the HRTI-3 specification. Beyond that, TanDEM-X provides a highly reconfigurable platform for the demonstration of new radar imaging techniques and applications. This paper gives a detailed overview of the TanDEM-X mission concept which is based on the systematic combination of several innovative technologies. The key elements are the bistatic data acquisition employing an innovative phase synchronization link, a novel satellite formation flying concept allowing for the collection of bistatic data with short along-track baselines, as well as the use of new interferometric modes for system verification and DEM calibration. The interferometric performance is analyzed in detail, taking into account the peculiarities of the bistatic operation. Based on this analysis, an optimized DEM data acquisition plan is derived which employs the combination of multiple data takes with different baselines. Finally, a collection of instructive examples illustrates the capabilities of TanDEM-X for the development and demonstration of new remote sensing applications.

Tropical rain forest environments are highly complex and heterogeneous in terms of species composition and structure and is often difficult to access. Radar remote sensing is for large tropical regions the only available information source for monitoring. Pol-InSAR is a novel developed radar remote sensing technique sensible to the vertical structure of forest that allows the estimation and mapping of forest height. In this paper we demonstrate forest height inversion at two frequencies - L band and P band - by means of Pol-InSAR using INDREX-II data and addresses the problem of temporal decorrelation.

In this study we concentrate on the application and validation of forest height estimation by polarimetric SAR interferometry for boreal forest. The study material was collected during the FinnSAR campaign, carried out in Finland in fall 2003. The main instruments of the campaign were E-SAR airborne radar (L- and X-band) and HUTSCAT helicopter-borne profiling scatterometer (X- and C-band). The validated forest height estimation algorithm is based on random volume over ground (RVoG) model inversion by using POLinSAR data. We compare POLinSAR-derived forest height with results from profiling HUTSCAT scatterometer measurements and with ground measurements and discuss the results. Our results show that the forest height values, estimated by means of two different instruments, are in good agreement.

It is well known that along track interferometric SAR systems (ATI) are able to provide information on ocean surface currents. For example interferograms acquired during the Shuttle radar topography mission (SRTM) were used at DLR to measure currents in the river Elbe. The ocean current information is important for many practical applications like, e.g., monitoring of sediment transport in coastal areas, optimal siting of current turbines, or forecast of oil slick drift. It has been shown that the TerraSAR-X split antenna mode can be expected to provide current information comparable to SRTM data, i.e., the ocean current component in the antenna look direction on a kilometre scale. This is already valuable information and complements the traditional in situ current measurements very well. However, there is still demand for two-dimensional (2- D) ocean current vectors to get a more complete picture of the 2-D spatial structure of the current field in particular in areas with strong spatial dynamics of the current direction. The presentation will give an overview of a proposal to carry out an experiment in the framework of the Tandem-X mission with the objective to retrieve additional 2-D information on currents and ocean waves. The idea is to operate both SAR instruments in split antenna mode with large squints of one or both satellites. Using this flight configuration it is expected that it will be possible to gain information on 2-D ocean current vectors. Due to the lack of suitable instruments this technique has never been applied using spaceborne platforms before. First experiments with airborne systems demonstrating the potential of the approach have been conducted. Apart from new current field information the proposed configuration enables the derivation of 2-D information on near surface wind fields applying the standard scatterometer approach. Furthermore it is expected that the approach will provide new information on the so called modulation transfer function (MTF) for ocean waves, which is one of the key factors in SAR ocean wave spectra retrieval schemes. The presentation will give a summary of the main technical issues including simulation studies showing the impact of different key parameters, e.g., the squint angle. The potential as well as limitations of the approach will be discussed.

TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is an innovative radar interferometry mission to generate a global, consistent and highly accurate digital elevation model (DEM) and to provide a configurable SAR interferometry platform for demonstrating new SAR techniques and applications. This paper summarizes the mission concept starting from the user requirements, the HELIX orbit and TanDEM-X operational modes to the expected height performance. Examples of new SAR techniques are presented.

This paper investigates the polarimetric and polarimetric interferometric synthetic aperture radar (PolInSAR) information contained in the high-resolution X-band data acquired by the RAMSES airborne SAR system over an area around Avignon, France containing bare surfaces, vegetation, and urban areas. The interferometric coherences are computed over natural and urban areas for all possible baseline copolar polarizations. In the complex plane, the obtained regions of coherence corresponding to most vegetation areas display small angular extents, meaning that if penetration occurs in the foliage, it is shallower than the system height accuracy. To quantify the PolInSAR information, an analysis of the interferometric height accuracy is first performed, and the results are compared with those associated with a theoretical and an empirical model. Concerning vegetation, a 6-m height difference is measured between the different polarimetric phase centers over a sparse pine forest, probably due to the presence of holes in the canopy. Crop study reveals also that wheat-type fields present oriented media properties at X-band due to their vertical structure. Over urban areas, in most cases, building height can be accurately obtained by using Pauli polarimetric phase center information.

In this paper the concept of point-like coherent scatterers (CSs) in urban areas is introduced. The detection of CSs in single and quad-polarized images is addressed and applied on polarimetric and interferometric high-resolution airborne SAR data at L-band. For the detection of CSs two different approaches are proposed and their individual performance is analyzed. The properties of the detected CSs and their polarimetric and interferometric characteristics are assessed. The role of polarimetry on the detection of the CSs is evaluated and the potential of extracting the orientation and dielectric properties of individual CSs is finally addressed.

Abstract : Surface can be characterized in terms of its material (dielectric) and geometric properties. The dielectric properties of the surface are expressed primarily by its moisture content, while the roughness describes the geometric characteristics of surface. Various techniques for information retrieval from remotely sensed data have been proposed in a number of recent studies. Some of them are based on an empirical relationship between the measured return signals and the ground truth. Because of their development from a limited number of observations, these models are generally valid only for the conditions under which those measured data were taken. These models also appear that no dependence on the roughness parameter, l-correlation length. In this work, the potential of using the polarimetric SAR data over surface scatterers in order to invert surface parameters is investigated. The model-based and image-based inversion schemes are investigated and compared; the former is doing retrieval from a dynamic learning neural network trained with the Advanced Integral Equation Model, while the latter is schemed from a decomposition of coherency matrix. In model based approach, only the surface scattering term of total return is used in order to remove the vegetation effects. The image based approach accounts for non-zero cross-polarized, backscattering as well as depolarization by three polarimetric parameters, namely the scattering entropy(H), the scattering anisotropy(A), and the alpha angle(alpha). The features of these two schemes are discussed in terms of numerical aspects and physical implications of the surface parameters being inverted by using experimental E-SAR L-band data . We also show the performances of inversion and discuss the advantages and drawbacks of both schemes.

ESA-ESRIN 2010 (PolSARpro Toolbox: Maintenance and Continuation)

In this paper the detection of point-like coherent scatterers (CSs) in single and quad-pol images is addressed and applied on polarimetric and interferometric high-resolution SAR data. For the detection of CSs two different approaches are described, both based on spectral correlation. The effect of multi acquisitions on the number and quality of CSs is verified and the results are discussed. The role of polarimetry on the detection is evaluated and the potential of polarimetric based information extraction as the estimation of the orientation angle – is addressed.

The quantitative bio/geo physical agricultural and land-use parameter estimation has attained significant prominence as it is a challenge to quantify complex and fast changing media in terms of geometry and material properties. In this work the behavior of agricultural vegetation in terms of polarimetric interferometric SAR (Pol-InSAR) observables is investigated. For this a two layer model, the Oriented Volume over Ground (OVoG) model, is discussed and validated against experimental data

The polarimetric cross-talk calibration algorithm proposed by Quegan, which is more general than the one proposed by van Zyl, is modified to improve polarimetric SAR calibration. L-band polarimetric SAR data acquired by the JAXA/NICT airborne Pi-SAR, including a variety of natural and artificial targets, are calibrated introducing noise imbalance of cross-polarized channels into the Quegan algorithm and deriving the cross-talk parameters from areas with low correlation between like and cross-polarized observed echoes. The achieved enhancement in stability of cross-talk parameter estimation and removal over is demonstrated in terms of CR response analysis and polarimetric classification result

Polarimetric SAR interferometry (Pol-InSAR) permits an accurate forest height extraction by inverting the SAR data with the Random Volume over Ground-model (RVoG). These forest heights were subsequently converted to forest biomass through forest height-biomass relation (allometry). Both the height extraction (L-band) and the allometric biomass conversion proved a good performance over dense forest stands. In open forests and stands of variable canopy height (here referred to as heterogeneous stands), the behaviour of the height extracting algorithm is unknown, and height-biomass allometry cannot easily be applied. To facilitate a direct biomass comparison of the Pol-InSAR-height extraction with ground measurements, the effective height heffwas introduced as a direct biomass equivalent. Examples from spruce dominated sites of variable stand densities showed a sensitivity of the height extraction to the stand density, though not in correlation with the heff. It might be promising to exploit additional information from the shape of the extracted heights-histogram for an accurate biomass estimation even over dense and heterogeneous forests

Forest biomass (wood volume) is the most integrative forest structural parameter. Since no remote sensing technique can measure wood volume or biomass directly, a direct biomass determination from air- or spaceborne images always has a regression character. A different approach is to estimate forest biomass indirectly from a forest parameter that can be extracted more accurately than biomass, of which forest height is the closest related one. This makes it possible to utilize remote sensing methods that extract forest heights for biomass assessments: stereoscopic aerial photography, Lidar and Pol-InSAR. In this paper, forest heights were extracted from polarimetric interferometric SAR data (Pol-InSAR) over the spruce dominated test site `Fichtelgebirge'. The extracted heights proved a good correlation with the upper canopy height (h100, top height), and could be converted into forest biomass by means of height-biomass allometry. On this basis, the potential of Pol-InSAR systems for forest biomass estimation is critically discussed, and directions towards a performance optimisation are pointed out

TanDEM-X is a mission proposal for a TerraSAR-X add-on satellite for high-resolution single-pass SAR interferometry. This mission proposal has been selected for a Phase A study within the scope of a Call for Proposals for a next German Earth Observation Mission to be launched in 2008/2009. The mission has the goal of generating a global Digital Elevation Model (DEM) with an accuracy corresponding to the DTED-3 specifications (12 m posting, 2 m relative height accuracy for flat terrain). This goal will be achieved by means of a second, TerraSAR-X like satellite (TanDEM-X) flying in a close orbit configuration with TerraSAR-X. This paper describes the mission concept and requirements, including several innovative aspects like operation modes, orbit selection and maintenance as well as PRF and phase synchronization. Results from a detailed performance estimation show the achievable DEM accuracy. Finally, an overview of the potential of the TanDEM-X mission for several scientific applications is presented.

In this work an attempt is made to extract information about the subsurface soil layers by means of multi-frequency fully polarimetric SAR data. The model of scattering from two-layer structure is proposed and analysis of experimental SAR data at L- and P-band is carried out.

In this overview, reasons are provided on why we do need to place multi-modal, multi-band single and multiple pass POLinSAR monitoring platforms into air and space. The questions "on what POLinSAR monitoring can provide that POL-SAR and IN-SAR by themselves cannot accomplish" is assessed; whereupon facts and justifications on placing POL-IN-BISAR satellite clusters into space are presented. Reasons for this technology becoming a basic requirement for current, near-future and much more so for future all day & night year-round monitoring of the terrestrial covers are analyzed in view of the un-abating and uncontrollable terrestrial population explosion, which has, does and for ever will result in unavoidable conflicts deteriorating unfortunately at times into terrorism. The pertinent questions on how to reduce the exorbitant cost for initiating this "home-globe security protection" technology are therefore also broached, and the expected benefits are laid out. The pertinent National and International airborne and space borne multi-modal, multi-band SAR remote sensing and security conflict surveillance support agencies are herewith invited for co-sponsoring our proposal, which is timely and fleets of orbiting multi-band POLinSAR platforms are urgently required to be placed into space.

The performance difference between full polarimetry (fp) and partial polarimetry (pp) systems is still an important open question according to Imbo and Souyris (2000) and Lee et al. (1995), especially in the frame of future low-cost spaceborne SAR studies. The aim of this paper is to briefly present a fully polarimetric model, the recent F-Bragg model (Breuer et al., (2002, 2003)), and compare it to its restriction to partial polarimetry. In a second step the inversion possibilities of fp and pp F-Bragg are investigated.

In this work the potential of using the interferometric coherence at different polarization over surface scatterers to extract information about surface parameters is investigated. The sensitivity of the individual coherence contributions to surface roughness and moisture conditions is discussed and simulated using a novel hybrid polarimetric surface scattering model. The model itself consists of a coherent part obtained from the extended Bragg model and incoherent part obtained from the integral equation model. Finally, the experimental airborne SAR data are used to validate the proposed model at different polarizations.

Various remote sensing techniques do not derive forest biomass directly but forest parameters that are related to biomass such as forest height or LAI (leaf area index). This article focuses on the accuracy of the allometric relation relating forest biomass to forest height. The main error of a height-to-biomass allometry is the missing information on stand density (basal area). Yet, the relation derived from standard forestry tables (1) showed less than 15 % variance for a large range of temperate forest types. Comparing the table-derived height-biomass relation to data from standard field inventory, it was found that the table-based rela- tion overestimates biomass. A modified allometric relation is proposed that compensates the systematic overestimation.

A two-step consolidation process is presented that reduces the effect of speckle in the classification result. The degree of consolidation or smoothing can be adjusted using a classification confidence measure and a threshold for the amount of neighborhood agreement needed. Results from AIRSAR ice data shown are compared to a classification result form pre-filtered data.

Proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.

The aim of this paper is to analyse the information contained in multi-frequency and polarimetric SAR data for an accurate retrieval of surface geophysical parameters. A two-scale surface scattering model is presented with the aim to develop an inversion algorithm. This model is represented by projection into a three-dimensional space defined by the H-A-/spl alpha/_parameters, which permits, for each observation frequency value, to obtain distinct curves with respect to the soil moisture content and large-scale roughness. Then, a comparison between multi-frequency and polarimetric data from the JRC laboratory and the two-scale surface polarimetric response is carried out. At last, two different multi-frequency parameter inversion methods based on artificial neural networks schemes are proposed.

In this work the potential of using the interferometric coherence at different polarisations over surface scatterers in order to extract information about surface parameters is investigated. The sensitivity of the individual coherence contributions to surface roughness and moisture conditions is discussed and simulated using a polarimetric surface scattering model. Finally, experimental airborne SAR data are used to demonstrate the discussed effects.

Forest biomass is one of the most important and most unknown parameters for accurate global carbon stock modelling. In this paper we address an alternative methodology for estimating above ground forest biomass from radar remote sensing data, based on forest height estimates from single frequency polarimetric-interferometric SAR data.

Recently, several passive interferometric configurations consisting of two or more receiving only micro-satellites operating in a fully polarimetric mode positioned ahead or behind a master satellite have been proposed as a cost efficient option for future single-pass interferometric spaceborne SAR systems. In this paper the potential performance of such a passive polarimetric and interferometric micro-satellite concept for the estimation of forest structure parameters in terms of polarimetric interferometry is investigated.

This paper discusses the role of scattering decomposition models in the classification of polarimetric SAR sea ice data. The iterative Wishart classifier was applied to 3-frequency airborne SAR data acquired in the Beaufort Sea, and the scattering models were found to be helpful in interpreting the assigned classes. In addition to using the full data set, reduced data sets based on an eigenvector decomposition were investigated for their potential for classification, as the eigenvectors provided a separation of scattering mechanisms. The surface scattering component was found to be the dominant one for this data set, and yielded a classification similar to the full data set.

This paper discusses the capability of the complex Wishart classifier for sea ice and classification using multifrequency, fully polarimetric SAR data. C-, L-, and P-band data acquired by the JPL AIRSAR in the Beaufort Sea was used. Classification using the unsupervised Wishart classifier is a two-stage process. An initial classification is required to seed the algorithm and can be derived using other classification methods. The Wishart classifier then used in iterations where the class means are updated after every step. The convergence of this approach is investigated. The Wishart classifier was found to be extremely dominant so that the classification result after a few iterations depends not necessarily on the initial classification used to derive the first class means. Even an initial classification derived with a random number generator leads to a good result after a few iterations.