Ice-free areas in Antarctica contain fragile ecosystems and are hotspots of biodiversity due to a concentration of flora and fauna. As these areas are often isolated and with difficult access, using hyperspectral satellite-borne data and remote sensing techniques presents great advantages for characterizing and monitoring their extension and surface cover characteristics. The objective of this work was to map key soil properties related to the Adélie penguin colony and associated flora and fauna of Seabee Hook at Cape Hallett using EnMAP data. Initial mapping results of extractable phosphorous (P), total organic carbon (TOC) and total nitrogen (TN) were obtained, and the distribution could be related to the intense activities of the penguin colony that comes to breed in the study area.

The Environmental Mapping and Analysis Program (EnMAP) is a new spaceborne German hyperspectral satellite mission, whose primary goal is to generate accurate information on the state and evolution of the Earth´s ecosystems. The core themes of EnMAP are monitoring environmental changes, ecosystem responses to human activities, and management of natural resources such as soils and minerals. EnMAP started on 1stApril 2022 and is now in operational phase since over six months, with strong expectations regarding data quality and impact on soil research. In this paper, we aim to demonstrate in a few case studies the observed current capabilities for EnMAP with regard to soil mapping based on different test sites and methodologies. Key soil properties could be derived and spatially mapped in agricultural test sites in semi-arid and temperate zones such as Soil Organic Carbon (SOC) content important for soil health and carbon sequestration, texture (clay content) important for soil fertility, and carbonate content. Additionally, we test different standard and state-of-the art methodologies, including new scenarios for time-series of hyperspectral remote sensing data for improved soil products.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

Ice-free areas within the Antarctic Peninsula region are of particular interest due to their rich terrestrial biodiversity. They are often small extensions in isolated areas and the use of visible near infrared reflectance is an ideal tool for characterizing and monitoring their extension and surface covers. The objective of this work was to use reflectance spectroscopy to determine the distribution of terrestrial land surface covers at Cierva Point. A site specific spectral library containing 86 reference spectra was compiled. As a result, a selection of image-derived spectra from multispectral satellite data were easily labeled using the reference spectra. A preliminary distribution has distinguished seven different basic surface covers over the study area.

The German Environmental Mapping and Analysis Program (EnMAP) will provide hyperspectral spaceborne data to the global geoscientific community. Here we present EnGeoMAP the EnMAP Geological Mapper and EnSoMAP the EnMAP Soil Mapper which are two geomapping tool provided in the EnMAP Box. They offer geologists and soil scientists worldwide a unique toolset for the analysis of future EnMAP data. The structure, workflow and results of EnGeoMAP and EnSoMAP are shown and discussed together with a brief overview on further developments. Synergies between EnMAP and other sensors are furthermore demonstrated.

Ice- free areas within the Northern Antarctic Peninsula region are of interest for studying changes occurring to surface covers, including those related to glacial coverage, raised beach deposits and periglacial processes and permafrost. The objective of this work is to map the main surface covers within ice-free areas of King George Island, the largest island of the South Shetlands archipelago, using fully polarimetric RADARSAT-2 SAR data. Surface covers such as rock outcrops and glacial till, stone fields, patterned ground, and sand and gravel deposits form the most representative classes and account for 84 km2of the ice-free areas on the island. A distribution of complex geomorphological features and landforms was obtained, being some of them considered indicators of periglacial processes and presence of permafrost.

Deception Island is an active volcano in the Antarctic Peninsula region, affected by different geomorphological processes interacting to form a complex mosaic of landforms and deposits. The use of visible near infrared reflectance is an ideal tool for characterizing and monitoring surface covers and substrates. The objective of this work was to use reflectance spectroscopy to identify spectral characteristics of surface covers related to different volcanic deposits in ice-free areas of Deception Island, South-Shetland Islands. A site specific spectral library containing 220 reference spectra was compiled. Image-derived spectra from multispectral satellite data were easily labeled using the reference spectra. A preliminary distribution has distinguished five different deposit types over the entire area of Deception Island.

In this paper, we introduce two spatially adaptive filtering methods to improve the estimation of the covariance matrix (CM), which is required for the processing of tomographic SAR data. We evaluate their effect on scatterer separation and height estimation. We propose several criteria to evaluate such methods and introduce a spatial simulation procedure allowing generating a tomographic image stack from a 3-D building model, assuming a multitrack airborne configuration and a distributed target model incorporating multidimensional speckle. Inversion of such a model requires the estimation of a CM from the data. Consequently, we propose two nonlocal methods to improve the estimation of the CM. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between CMs. The second one is a new method extending the previous one to similarities between patches. We show the importance of spatial adaptivity in covariance estimation by comparing the 3-D reconstructions obtained with our filters and other methods. Further experiments on simulated and L-band experimental data show the ability of the nonlocal filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge-preserving properties.

In this paper, we introduce two spatially adaptive covariance filtering methods and evaluate their effect on scatterer separation and height estimation from tomographic SAR. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between covariance matrices. The second one is a new method extending the previous one to patch-based similarities. We show the importance of spatial adaptivity in covariance estimation by comparing the 3D reconstructions obtained with our nonlocal filters and the boxcar filter. Our experiments on simulated and L-band experimental data show the ability of the non-local filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge preserving properties.

In this paper, we propose to reconstruct, in 3D, an isolated building. We introduce a treatment chain, from SAR data to 3D rendering. We define the Tomographic Bilateral filter (TomoSAR-BLF) which improve drastically the estimation of the covariance matrix by preserving the edge and the nature of the different object. The rest of the processing consists mainly by generating the tomograms using spectral analysis, extracting point-cloud using the TomoSNI approach and then rendering the 3D building using a mesh obtained by triangulation and surface rendering method.

Active periglacial processes and landforms are common within the Northern Antarctic Peninsula region and are becoming the focus of interest for studying changes occurring to permafrost. The objective of this study was to identify and characterize glacial and periglacial surface features with fully polarimetric SAR C band RADARSAT-2 data in ice-free regions surrounding Maxwell Bay, South Shetland Islands. Extraction of polarimetric parameters, a selection of field based training sites and a supervised classification approach, were chosen to determine the spatial distribution of the different geomorphological surface features. These results show the identification of complex and relatively small scale geomorphological features such as periglacial landforms that are an important indicator of the presence of permafrost.

In this paper, we introduce an automatic parameter selection technique for the polarimetric bilateral filter. The method is inspired by the theory of unbiased risk estimation that allows to compare different estimators with respect to a loss function. Moreover, a local risk estimation allows spatially varying parameters. We demonstrate our approach on experimental data and show how the method improves the smoothing capabilities of the filter in homogeneous areas while retaining its edge preserving properties.

This paper presents an investigation to the estimation of the surface movements over the region of Yakutsk using TerraSAR-X data acquired during two months. The interferometric phase generated from the data stack presents a deterministic behavior over the most of the image, due to short temporal baseline and frozen surface context, and reveals fringes due to surface deformation over different geo-morphologic objects as alluvial deposits, alasses and other thermokarstic depressions, but also on the Lena river.

This paper describes the analysis of complex scenario of urban area by means of a reduced number of tomographic SAR data. First, the tomographic data are processed using the standard MUSIC algorithm, which requires an estimation of the data covariance matrix and an estimation of number of sources. We introduce the tomographic bilateral filter (To-moBLF) to improve the estimation of the covariance matrix and we propose a new model order selection scheme using the tomographic entropy parameters TomoH. Then point of interest are extracted using the Tomographic Signal-to-Noise Index (TomoSNI) algorithm.

Synthetic aperture radar interferometry (InSAR) is limited by temporal decorrelation and topographic errors, which can result in unwrapping errors in partially incoherent and mountainous areas. In this paper, we present an algorithm to estimate and remove local digital elevation model (DEM) errors from a series of wrapped interferograms. The method is designed to be included in a small baseline subset (SBAS) approach for InSAR time series analysis of ground deformation in natural environment. It is easy to implement and can be applied to all pixels of a radar scene. The algorithm is applied to a series of wrapped interferograms computed from ENVISAT radar images acquired across the Himalayan mountain range. The DEM error correction performance is quantified by the reduction of the local phase dispersion and of the number of residues computed during the unwrapping procedure. It thus improves the automation of the spatial unwrapping step.

In this paper we introduce a method that allows automatic extraction of planar features from tomographic SAR data. Our approach takes advantage of the spatial connectivity of pixels from tomographic height maps in order to retrieve planar patches from noise corrupted complex scenes. We demonstrate how our method outperforms the well-known RANSAC algorithm over synthetic and experimental data.

In this paper we propose a new speckle filter for polarimetric SAR data based on the bilateral filter. We also study the use of Riemannian metrics related to the Hermitian nature of the complex covariance matrices. The approach is validated over synthetic and experimental data. The method achieves a strong smoothing in homogeneous areas while preserving both polarimetric and spatial information.

In this paper we present a SAR tomographic post-processing technique to extract point of interest from SAR tomograms. We propose a signal-to-noise index adapted to tomogram generated by SP-MUSIC-1 (single polarization, assuming 1 scatterer) that quantifies the separation of scatterer peak from other artifacts (noise,...). This index allows to remove artifacts caused by speckle noise, low SNR, error in orbit position, etc. The domain of validity of this approach is also analyzed over two different regions: isolated buildings and building surrounded with vegetation. Experiment results are shown using a multibaseline dataset acquired in L-band by DLR's experimental SAR (E-SAR) on a test site near Oberpfaffenhofen/Germany.

Synthetic Aperture Radar interferometry allows to measure spatio-temporal patterns of deformation. However this geodetic technique is limited by unwrapping difficulties linked with temporal decorrelation and topographic errors in partially incoherent and mountainous areas. This paper presents a new algorithm to correct and remove DEM errors in order to improve the phase unwrapping step. The method consists in a mix approach between Small Baseline and Permanent Scatterers strategy using a series of wrapped interferograms. First we develop our methodology and then we apply it to a series of wrapped ENVISAT interferograms on the Tibetan plateau.

This paper describes an unsupervised classifier for polarimetric interferometric synthetic aperture radar (PolInSAR) data. Expectation maximization is used to estimate class parameters that maximize the likelihood of observations in an input data set for a given number of classes. Polarimetric information, in the form of coherency matrices, and interferometric information, in the form of complex coherences, are taken into account. Differences in interferometric phase across different polarization states are explicitly modeled to make the classifier sensitive to the vertical structure of the scene under observation, and the distribution over such phase differences is introduced. The classifier is self-initializing, in that it does not rely on decompositions or thresholds. Classification results obtained for real polarimetric interferometric data are presented and discussed.

This letter proposes an algorithm to improve the range resolution in airborne synthetic aperture radar (SAR) data by coherently combining an interferometric image pair, i.e., two images acquired with slightly different viewing angles. This algorithm is based on the wavenumber shift principle. In contrast to other methods, developed for application to spaceborne SAR data, the proposed algorithm takes the nonlinear effects due the strong variations in incidence angle in airborne SAR data into account. The proposed method is applied to SAR data of German Aerospace Center (DLR)'s E-SAR sensor. Quantitative verification results are obtained by measuring the resolution of several corner reflectors placed in the area under study, as well as the resolution of speckle of different areas. It is demonstrated that a resolution improvement of almost a factor of two can be achieved by incorporating a second interferometric image, which can be acquired easily with an airborne sensor.

This paper presents the first step to characterisescatterers using polarimetric SAR tomography (POLTOMSAR).It is a logical extension of the tomographic data processing, whichconsists to use the vectorial properties of electromagnetic waves.An introduction to the estimation of the polarisation state ofretrieved target is proposed. It is based on a modified signalmodel adapted to the MUSIC algorithm. This makes it possibleto determinate the physical nature of detected object, which is notpossible using classical approach, unless having the ground-truthof the scene under study. The experimental results are shownusing a multibaseline data set acquired in L-band by DLR’sexperimental SAR (E-SAR) on a test site near Oberpfaffenhofen/ Germany.

This paper presents a first evaluation of the ESPRIT approach in polarimetric interferometric SAR. This evaluation is carried out by using 3D images obtained by SAR tomographic like an alternative to the acquisition of ground-truth data, which is an extremely complex task in the case of volume areas. All parameters over a volumetric area are directly visible in a tomographic image and can, therefore, be employed to validate the ESPRIT approach by comparing parameters generated by ESPRIT and the SAR tomography approach. This allows to identify the principal deficiencies of the ESPRIT method, which occur over high vegetation areas, where there is a misinterpretation of the ESPRIT results. Whereas, the ESPRIT approach is useful for building characterisation, identifying a good applicability area. Airborne L-band repeat-pass interferometric data of the German Aerospace Center (DLR) experimental airborne SAR are used to perform this evaluation.

This letter proposes a building characterization technique for L-band polarimetric interferometric synthetic aperture radar (SAR) data. This characterization consists of building identification and height estimation. Initially, a polarimetric interferometric segmentation is performed to isolate buildings from their surroundings. This classification identifies three basic categories: single bounce, double bounce, and volume diffusion. In order to compensate for the misclassifications among the volume and the double-bounce classes, interferometric phases given by the high-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method are analyzed. Once buildings are localized, a phase-to-height procedure is applied to retrieve building height information. The method is validated using E-SAR, German Aerospace Center (DLR) fully polarimetric SAR data, at L-band, repeat-pass mode, over the Oberpfaffenhofen, Germany, test site, with a spatial resolution of 1.5 m in range and azimuth. More than 80% of buildings are retrieved with acceptably accurate height estimates.

Abstract—This paper describes the analysis of built-up areas using fully polarimetric interferometric SAR data at L-band. This approach uses a polarimetric interferometric segmentation to determine the number of dominant scattering mechanisms required by an interferometric phase estimation using ESPRIT method. I.