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This study evaluates a temporally dense VV-polarized Sentinel-1 C-band backscatter time series (revisit time of 1.5 days) for wheat fields near Munich (Germany). A dense time series consisting of images from different orbits (varying acquisition) is analyzed, and Radiative Transfer (RT)-based model combinations are adapted and evaluated with the use of radar backscatter. The model shortcomings are related to scattering mechanism changes throughout the growth period with the use of polarimetric decomposition. Furthermore, changes in the RT modeled backscatter results with spatial aggregation from the pixel to field scales are quantified and related to the sensitivity of the RT models, and their soil moisture output are quantified and related to changes in backscatter. Therefore, various (sub)sets of the dense Sentinel-1 time series are analyzed to relate and quantify the impact of the abovementioned points on the modeling results. The results indicate that the incidence angle is the main driver for backscatter differences between consecutive acquisitions with various recording scenarios. The influence of changing azimuth angles was found to be negligible. Further analyses of polarimetric entropy and scattering alpha angle using a dual polarimetric eigen-based decomposition show that scattering mechanisms change over time. The patterns analyzed in the entropy-alpha space indicate that scattering mechanism changes are mainly driven by the incidence angle and not by the azimuth angle. Besides the analysis of differences within the Sentinel-1 data, we analyze the capability of RT model approaches to capture the observed Sentinel-1 backscatter changes due to various acquisition geometries. For this, the surface models “Oh92” or “IEM_B” (Baghdadi’s version of the Integral Equation Method) are coupled with the canopy model “SSRT” (Single Scattering Radiative Transfer). To resolve the shortcomings of the RT model setup in handling varying incidence angles and therefore the backscatter changes observed between consecutive time steps of a dense winter wheat time series, an empirical calibration parameter (coef) influencing the transmissivity (T) is introduced. The results show that shortcomings of simplified RT model architectures caused by handling time series consisting of images with varied incidence angles can be at least partially compensated by including a calibration coefficient to parameterize the modeled transmissivity for the varying incidence angle scenarios individually.

In this article a new method is introduced for the automatic estimation of all the relevant parameters of oil storage tanks using a single high-resolution synthetic aperture radar (SAR) image. For a given storage tank, this method will estimate its maximum capacity and determine whether it has a fixed or a floating roof. For tanks with a floating roof, the amount of oil stored will also be estimated. If a SAR time series is available all the images can be processed jointly, exploiting the available temporal information to provide more accurate and robust estimates than those obtained from each individual image. The dimensions of each storage tank are derived from its semicircular double reflections, which are detected using the coherent scatterers in the SAR image. The classification between tanks with fixed and floating roofs is performed with a simple machine learning classifier using just three features related to the detected semicircular double reflections. The performance of the proposed method using a single image and a time series is evaluated with three TerraSAR-X images of the port of Rotterdam, containing 167 oil storage tanks of different sizes, and with both fixed and floating roofs.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

A semianalytical approach based on an equivalent circuit representation is proposed to calculate the microwave transmission through metasheets consisting of two-dimensional periodic arrays of planar circular metal rings on a dielectric substrate. In the semianalytical approach, the impedances of the equivalent circuit are parameterized and fitted to match the values of transmission coefficients obtained by full-wave simulations at selected frequency points. As dimensional parameters, the outer radius and the width of the ring are considered. A metalens with four concentric zones is designed by using this semianalytical approach to correct the phase distortions due to a polypropylene hemispheric radome at frequencies around 28 GHz in the Ka band. It is shown that the designed metalens works well for 27 GHz, 28 GHz and 29.5 GHz, implying the bandwidth of approximately 2.5 GHz. The field transmitted through the metalens and the radome is calculated by Physical Optics (PO). The electrically large integration area is divided into small square facets to calculate the PO integral. The calculated and measured results are shown to agree well.

Special Issue Ten Years of Remote Sensing at Barcelona Expert Center.-- 19 pages, 9 figures, 6 tables, In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation, This research was funded by the Spanish Ministry of Science, Innovation and Universities, through the coordinated project L-Band (MCIU/AEI/FEDER, UE): Sobre la continuidad de las misiones satelitales de banda L. Nuevos paradigmas en productos y aplicaciones, grant numbers ESP2017-89463-C3-2-R (UPC part) and ESP2017-89463-C3-1-R (ICM part), and the Unidad de Excelencia María de Maeztu MDM-2016-0600. M. Piles is supported by a Ramón y Cajal contract and the project RTI2018-096765-A-100 (MCIU/AEI/FEDER, UE), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

This study investigates the sensitivity of L-band (1.41 GHz) polarimetric brightness temperature signatures to oriented permittivity patterns, which can occur for example in the case of row and interrow soil moisture differences in agricultural fields. A field experiment and model simulations are conducted to verify the effects of such patterns on all four Stokes parameters. We find that for an artificial target resembling idealized model conditions, permittivity patterns lead to systematic brightness temperature modulations in dependency of the azimuthal look angle. For the specific field setup, modulations reach amplitudes of ∼ 4 K and mostly affect h-polarized brightness temperatures as well as the first, second, and third Stokes parameters. Simulations of soil moisture patterns under idealized model conditions indicate even higher amplitudes (up to 60 K for extreme cases). However, the effects occur only for permittivity layer widths of up to 8 cm (given the observing wavelength of 21 cm), which is lower than the row and interrow widths typically observed in agricultural settings. For this reason, and due to the idealized model geometry investigated here, future studies are needed to transfer the findings of this study to potential applications such as the sensing of oriented soil moisture patterns. Particular interest might lie in radiometry and reflectometry in lower frequency ranges such as P-band, where according to the threshold established here (8/21 wavelengths), permittivity layer widths of up to ∼ 45 cm could be observed.

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). The algorithm is tested with the global active-passive microwave observations of the SMAP mission. The developed roughness retrieval algorithm shows independence of permittivity for e_s > 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of non-vegetated areas become evident on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined s and l confirm rather smooth to semi-rough surface roughness patterns with small vertical RMS heights and corresponding higher horizontal correlation lengths.

This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

A method of physical integration of scatteringmodels is presented here to estimate the backscattering coefficient(BSC) for L-band, brightness temperature (TB) for L- and C-Bandand the reflectance for visible (VIS) and near-infrared (NIR)region for dynamic vegetated terrain. The SPIN (SpectrumInvariant Interaction) model is obtained by solving vectorradiative transfer (VRT) equations kernel-based and therefore fordifferent wave interaction mechanisms. To demonstrate itsapplication for the microwave region, the measurements duringthe growing cycle of corn from the Eleventh Microwave, Water,and Energy Balance Experiment (MicroWEX-11) have been used.For the optical part the results are compared with the PROSAILmodel. By applying the SPIN model in the radar regime, it couldbe shown that the modeled backscattering coefficients (BSC)correlate strongly with the vertical polarization measurements(Pearson 0.83, R20.69) and are less correlated with the horizontalmeasurements (Pearson 0.45, R20.20). In addition, the modeledbrightness temperatures (L- and C-band) in both polarizationstates are also highly correlated with the MicroWEX-11measurements (L-band: Pearson 0.755, R2 0.57; C-band: Pearson0.73, R2 0.53). Finally, the optical results are consistent with theresults of other optical models (Pearson 0.99, R2 0.98).

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

Space debris nowadays is one of the main threats for satellite systems especially in low earth orbit (LEO). More than 700,000 debris objects with potential to destroy or damage a satellite are estimated. The effects of an impact often are not identifiable directly from ground. High-resolution radar images are helpful in analyzing a possible damage. Furthermore investigations on unknown space objects or satellites can be performed. Therefor DLR is currently developing a radar system called IoSiS (Imaging of Satellites in Space), being based on an existing steering antenna structure and our multipurpose high-performance radar system GigaRad for experimental investigations. GigaRad is a multi-channel system operating at X band and using a bandwidth of up to 4.4 GHz in the IoSiS configuration, providing fully separated transmit (TX) and receive (RX) channels, and separated antennas. For the observation of small satellites or space debris a high-power traveling-wave-tube amplifier (TWTA) is mounted close to the TX antenna feed. For the experimental phase IoSiS uses a 9 m TX and a 1.8 m RX antenna mounted on a common steerable positioner. High-resolution radar images are obtained by using Inverse Synthetic Aperture Radar (ISAR) techniques. The guided tracking of known objects during orbit pass allows here wide azimuth observation angles. Thus high azimuth resolution comparable to the range resolution can be achieved. This paper outlines technical main characteristics of the IoSiS radar system. It shows the main error sources and solutions as well as the calibration effort to generate the first centimeter-resolution radar image observed with IoSIS.

Many attempts have been made and are undertaken to model the electromagnetic (EM) interaction with vegetation-coveredsoils at different frequencies. The modelling concepts and architectures range from empirical correlations to simple andsophisticated physical models [1, 2]. However, a general understanding of the physical properties and interactions along frequencyand for a wider part of the EM spectrum is difficult if the different models are treating the single frequencies and acquisitioncharacteristics (e.g. active or passive recording) separately. Due to the increasing fleet of earth observation sensors operating inmultiple frequency bands, demand for a spectrum-overarching EM modelling is becoming increasingly important. An integrativemodeling approach offers the chance that the different insights into biophysical and biochemical processes can be consideredconsistently including the respective dielectric properties and structural characteristics. With this objective we developed a kernel-driven electromagnetic interaction model using vector radiative transfer (VRT), called SPIN (Spectrum Invariant Interaction)model, which represents an integrated model approach for active and passive microwaves and passive optical as well as thermalsignatures [3]. Preparatory research for this study was the development of the RadOptics Model [4]. The main advantage of a kernel-driven approach is its analytical invertibility and the representation of the VRT as sum of linear equations, which leads toan enlargement of the information by including multiple frequencies. In addition, the model does not require semi-empirical or statistical calibration (i.e. all model parameters have physical meaning and none just for model adjustment.).

A considerable amount of water is stored in vegetation, especially in regions with high precipitation rates. Knowledge of the vegetation water status is essential to monitor changes in ecosystem health and to assess the vegetation influence on the water budget. In this study, we develop and validate an approach to estimate the gravimetric vegetation water content (mg), defined as the amount of water [kg] per wet biomass [kg], based on the attenuation of microwave radiation through vegetation. mg is expected to be more closely related to the actual water status of a plant than the area-based vegetation water content (VWC), which expresses the amount of water [kg] per unit area [m2]. We conducted the study at the field scale over an entire growth cycle of a winter wheat field. Tower-based L-band microwave measurements together with in situ measurements of vegetation properties (i.e., vegetation height, and mg for validation) were performed. The results indicated a strong agreement between the in situ measured and retrieved mg (R2 of 0.89), with mean and standard deviation (STD) values of 0.55 and 0.26 for the in situ measured mg and 0.57 and 0.19 for the retrieved mg, respectively. Phenological changes in crop water content were captured, with the highest values of mg obtained during the growth phase of the vegetation (i.e., when the water content of the plants and the biomass were increasing) and the lowest values when the vegetation turned fully senescent (i.e., when the water content of the plant was the lowest). Comparing in situ measured mg and VWC, we found their highest agreement with an R2 of 0.95 after flowering (i.e., when the vegetation started to lose water) and their main differences with an R2 of 0.21 during the vegetative growth of the wheat vegetation (i.e., where the mg was constant and VWC increased due to structural changes in vegetation). In addition, we performed a sensitivity analysis on the vegetation volume fraction (&delta, ), an input parameter to the proposed approach which represents the volume percentage of solid plant material in air. This &delta, parameter is shown to have a distinct impact on the thermal emission at L-band, but keeping &delta, constant during the growth cycle of the winter wheat appeared to be valid for these mg retrievals.

Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time-domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP-observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave-based surface soil moisture retrievals in areas with high OM.

The Radar Vegetation Index (RVI) is a well-established microwave metric of vegetation cover. The index utilizes measured linear scattering intensities from co- and cross-polarization and is normalized to ideally range from 0 to 1, increasing with vegetation cover. At long wavelengths (L-band) microwave scattering does not only contain information coming from vegetation scattering, but also from soil scattering (moisture & roughness) and therefore the standard formulation of RVI needs to be revised. Using global level SMAP L-band radar data, we illustrate that RVI runs up to 1.2, due to the pre-factor in the standard formulation not being adjusted to the scattering mechanisms at these low frequencies. Improvements on the RVI are subsequently proposed to obtain a normalized value range, to remove soil scattering influences as well as to mask out regions with dominant soil scattering at L-band (sparse or no vegetation cover). Two purely vegetation-based RVIs (called RVII and RVIII), are obtained by subtracting a forward modeled, attenuated soil scattering contribution from the measured backscattering intensities. Active and passive microwave information is used jointly to obtain the scattering contribution of the soil, using a physics-based multi-sensor approach; simulations from a particle model for polarimetric vegetation backscattering are utilized to calculate vegetation-based RVI-values without any soil scattering contribution. Results show that, due to the pre-factor in the standard formulation of RVI the index runs up to 1.2, atypical for an index normally ranging between zero and one. Correlation analysis between the improved radar vegetation indices (standard RVI and the indices with potential improvements RVII and RVIII) are used to evaluate the degree of independence of the indices from surface roughness and soil moisture contributions. The improved indices RVII and RVIII show reduced dependence on soil roughness and soil moisture. All RVI-indices examined indicate a coupled correlation to vegetation water content (plant moisture) as well as leaf area index (plant structure) and no single dependency, as often assumed. These results might improve the use of polarimetric radar signatures for mapping global vegetation. Keywords: microwaves; radiometer; radar; vegetation index; soil scattering; roughness; soil moisture; SMAP; SMOS

Metasheets are ultra-thin sheets built from sub-wavelength resonators designed in order to achieve certain frequency-dependent transmission behaviour. A semi-analytical approach based on an equivalent circuit representation is proposed to calculate the microwave transmission through metasheets which consist of 2D periodic arrays of planar circular metal rings with and without substrate. The electromagnetic response of the metasheet can be controlled by changing the radius and the periodicity of the circular rings. In the semi-analytical approach, the equations for the impedances of the equivalent circuit are parametrized and fitted to match the values of transmission coefficients obtained by full-wave simulations at selected frequency points. Such an approach permits an optimization of the metasheet design with a very small number of full-wave numerical simulations. It is shown that the results of the semi-analytical approach match well with full-wave simulations and measurements within a reasonable range of radius and periodicity values.

In the past years DLR has developed and operates a very versatile and modular high-resolution radar system for manifold applications. The so called GigaRad instrument [1] is an experimental system operating in X and Ku band, and can provide spatial resolution of a few centimeters. The waveform generation and reception in the baseband is performed by IQ modulation and demodulation, based on full digitization of the baseband signals in transmit and receive path. The system concept providing two transmit and two receive channels allows quasi monostatic, bi-static, or MIMO (multiple input multiple output) operation. The normal transmit waveform is a chirp, but also any other waveforms like noise ore orthogonal coded signals are possible [6]. Based on coherent system architecture and the realized degree of automation the applications of the instrument vary from RCS measurements, UAV detection to the Imaging of Satellites in Space (IoSiS). In this paper the basic system concept, the calibration procedure, and some applications of the instrument are outlined.

Ultra-thin metamaterials, called meta-surfaces or meta-sheets, open up new opportunities in designing microwave radomes, including an improved transmission over a broader range of antenna scan angles, tailorable and reconfigurable frequency bands, polarization transformations, one-way transmission and switching ability. The smallness of the unit cells combined with the large electrical size of microwave radomes significantly complicates full-wave numerical simulations as a very fine sampling over an electrically large area is required. Physical optics (PO) can be used to approximately describe transmission through the radome in terms of the homogenized transmission coefficient of the radome wall. This paper presents the results of numerical simulations of electromagnetic transmission through planar meta-sheets (infinite and circularly shaped) obtained by using a full-wave electromagnetic field simulator and a PO-based solution.

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

The Microwaves and Radar Institute of DLR operates an experimental radar system called IoSiS (Imaging of Satellites in Space), to do basic research in imaging of objects in space and furthermore for the purpose of gath-ering high resolution radar images of objects in a low earth orbit. These images can be an appropriate tool for analyzing satellite structures and possible mechanical damages for example caused by space debris. Furthermore investigations on unspecified objects could be established. Considering the imaging process of objects in a low earth orbit, based on inverse synthetic aperture radar geometry (ISAR), several error sources have to be taken into account. Experimental measurements show the first imaging results and these challenges in the imaging process.

ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.

Space debris nowadays is one of the main threats for satellite systems especially in low earth orbit (LEO). More than 700,000 debris objects having potential to destroy or damage a satellite are estimated. The effects of an impact often are not identifiable directly from ground. Hence highresolution radar images are an appropriate tool for analyzing satellite structures. Therefor DLR is currently developing a radar system called IoSiS (Imaging of Satellites in Space), being presently based on an existing steering antenna facility and our multi-purpose high-performance radar system GigaRad [3], developed and constructed for multi-purpose experimental investigations. GigaRad is a multi-channel system operating at X band and using a bandwidth of up to 4.4 GHz in the IoSiS onfiguration, providing fully separated transmit (TX) and receive (RX) channels, and separated antennas. For the observation of small satellites or space debris a high-power traveling-wave-tube amplifier (TWTA) is mounted close to the TX antenna feed. For the experimental phase IoSiS uses a 9 m TX and a 1 m RX antenna mounted on a common steerable positioner. High-resolution radar images are obtained by using Inverse Synthetic Aperture Radar (ISAR) techniques. The guided tracking of known objects during overpass allows wide azimuth observation angles. Thus high azimuth resolution comparable to the range resolution can be achieved. This paper outlines technical main characteristics of the IoSiS radar system including the basic setup of the antenna, the radar instrument and the corresponding RF error correction methodology, and the measurement strategy. Also a short description about a simulation tool for characterizing the whole instrument and exploring the expected nature of the radar images are discussed.

In this study, polarimetric Synthetic Aperture Radar (PolSAR) data at X-, C- and L-Bands, acquired by the satellites: TerraSAR-X (2011), Radarsat-2 (2011), ALOS (2010) and ALOS-2 (2016), were used to characterize the tundra land cover of a test site located close to the town of Tuktoyaktuk, NWT, Canada. Using available in situ ground data collected in 2010 and 2012, we investigate PolSAR scattering characteristics of common tundra land cover classes at X-, C- and L-Bands. Several decomposition features of quad-, co-, and cross-polarized data were compared, the correlation between them was investigated, and the class separability offered by their different feature spaces was analyzed. Certain PolSAR features at each wavelength were sensitive to the land cover and exhibited distinct scattering characteristics. Use of shorter wavelength imagery (X and C) was beneficial for the characterization of wetland and tundra vegetation, while L-Band data highlighted differences of the bare ground classes better. The Kennaugh Matrix decomposition applied in this study provided a unified framework to store, process, and analyze all data consistently, and the matrix offered a favorable feature space for class separation. Of all elements of the quad-polarized Kennaugh Matrix, the intensity based elements K0, K1, K2, K3 and K4 were found to be most valuable for class discrimination. These elements contributed to better class separation as indicated by an increase of the separability metrics squared Jefferys Matusita Distance and Transformed Divergence. The increase in separability was up to 57% for Radarsat-2 and up to 18% for ALOS-2 data.

A radome is an integral part of almost every antenna system, protecting antennas and antenna electronics from hostile exterior conditions (humidity, heat, cold, etc.) and nearby personnel from rotating mechanical parts of antennas and streamlining antennas to reduce aerodynamic drag and to conceal antennas from public view. Metamaterials are artificial materials with a great potential for antenna design, and many studies explore applications of metamaterials to antennas but just a few to the design of radomes. This paper discusses the possibilities that metamaterials open up in the design of microwave radomes and introduces the concept of metaradomes. The use of metamaterials can improve or correct characteristics (gain, directivity, and bandwidth) of the enclosed antenna and add new features, like band-pass frequency behavior, polarization transformations, the ability to be switched on/off, and so forth. Examples of applications of metamaterials in the design of microwave radomes available in the literature as well as potential applications, advantages, drawbacks, and still open problems are described.

The combined use of high resolution SAR and optical images is of great interest, especially in complex scenes like urban areas. This requires accurate registration of images with big radiometric and geometric differences. This paper shows the accuracy that can be achieved by taking advantage of the geolocation accuracy of current sensors, instead of using more complex feature or intensity-based methods. An issue that can arise in layover areas is identified and a solution is shown. The approach is illustrated using very high resolution SAR and optical images.

The radiometric calibration of synthetic aperture radar (SAR) systems is typically based on the known backscatter of calibration point targets such as transponders. Before a SAR calibration campaign can begin it is therefore necessary to determine the backscatter (radar cross section or RCS) of the transponder itself. Known methods suffer from unnecessarily high uncertainty contributions and therefore also affect the radiometric uncertainty of the calibrated SAR system. In this paper we present a novel calibration approach which neither requires to disassemble the amplifier path nor necessitates a separate RCS calibration target such as a corner reflector or a circular disk. Significant sources of measurement uncertainties can therefore be outright avoided. The method is not applicable to all transponders though, and a certain type of transponder design is assumed. Nevertheless, direct metrological traceability to a realization of the SI base unit "meter" can be achieved for the first time. The reduced measurement uncertainties of the novel approach will subsequently allow to reduce the radiometric measurement uncertainties of air- and spaceborne SAR sensors in the future.

This study investigates a two component decomposition technique for HH/VV-polarized PolSAR (Polarimetric Synthetic Aperture Radar) data. The approach is a straight forward adaption of the Yamaguchi decomposition and decomposes the data into two scattering contributions: surface and double bounce under the assumption of a negligible vegetation scattering component in Tundra environments. The dependencies between the features of this two and the classical three component Yamaguchi decomposition were investigated for Radarsat-2 (quad) and TerraSAR-X (HH/VV) data for the Mackenzie Delta Region, Canada. In situ data on land cover were used to derive the scattering characteristics and to analyze the correlation among the PolSAR features. The double bounce and surface scattering features of the two and three component scattering model (derived from pseudo-HH/VV- and quad-polarized data) showed similar scattering characteristics and positively correlated-R2 values of 0.60 (double bounce) and 0.88 (surface scattering) were observed. The presence of volume scattering led to differences between the features and these were minimized for land cover classes of low vegetation height that showed little volume scattering contribution. In terms of separability, the quad-polarized Radarsat-2 data offered the best separation of the examined tundra land cover types and will be best suited for the classification. This is anticipated as it represents the largest feature space of all tested ones. However; the classes “wetland” and “bare ground” showed clear positions in the feature spaces of the C- and X-Band HH/VV-polarized data and an accurate classification of these land cover types is promising. Among the possible dual-polarization modes of Radarsat-2 the HH/VV was found to be the favorable mode for the characterization of the aforementioned tundra land cover classes due to the coherent acquisition and the preserved co-pol. phase. Contrary, HH/HV-polarized and VV/VH-polarized data were found to be best suited for the characterization of mixed and shrub dominated tundra.

Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estimated from the observations of an airborne P-band SAR instrument across nine different biomes in North America is presented. The goal is to apply a hybrid (model- and eigen- based) three component decomposition approach to separate the contributions of surface, double-bounce and vegetation volume scattering across a wide range of biome conditions. The decomposition makes no prior assumptions about vegetation structure. We characterize the dynamics of the decomposition across different North American biomes and assess their characteristic range. Impacts of vegetation cover seasonality and soil surface roughness on the contributions of each scattering mechanism are also investigated. Observations used here are part of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission and data have been collected between 2013 and 2015., Comment: 29 pages, 9 figures

Recently DLR has developed and constructed a new experimental radar instrument for various applications like radar signature collection, SAR/ISAR imaging, motion detection, tracking, etc., where high performance and high flexibility have been the key drivers for system design. Consequently a multi-purpose and multi-channel radar called GigaRad is operated in X band and allows an overall bandwidth of up to 6 GHz, resulting in a theoretical range resolution of up to 2.5 cm. Hence, primary obligation is a detailed analysis of various possible error sources, being of no or less relevance for low-resolution systems. A high degree of digital technology enables advanced signal processing and error correction to be applied. The paper outlines technical main features of the radar, the basic error correction strategy and illustrates some first imaging results.

Spaceborne synthetic aperture radars (SAR) are systems capable of acquiring high-resolution earth-observation data independent on time of day and weather. These SAR systems have to be calibrated before first use and at regular intervals. For these radiometric calibrations active targets with known backscatter, so called transponders, serve as an external reference. For determining the frequency-dependent radar cross section (RCS) of these transponders, different measurement methods are available. This paper gives an overview of four different strategies with a following uncertainty analysis according to the Guide to the expression of uncertainty in measurement (GUM). After comparison with respect to accuracy and feasibility, a recommendation for the best absolute radiometric transponder calibration method is given.

The progressive development of upcoming satellite missions using synthetic aperture radar (SAR) lead to novel challenges also for the reference targets. In order to provide a reference target for an accurate absolute radiometric calibration the knowledge of the backscattering characteristics is essential. Whereas usually one single measurement is used for the estimation of the radar cross section (RCS), this paper presents two conducted, independent methods to derive the RCS of three C-band transponder leading to cross-validated results based on a compatibility analysis.

In der vorhandenen Software des Instituts ist eine Visualisierung von Radarzielen integriert, welche jedoch nicht mehr auf dem neuesten Stand der Technik ist. Diese wird durch die in dieser Arbeit entwickelte leistungsfähige moderne Bibliothek ersetzt werden. Diese soll Funktionen für eine Visualisierung von 3D-Modellen, die Darstellung der Flugtrajektorie sowie die Visualisierung des gestreuten Radarsignals implementieren.

Known and unknown properties of Hansen Ideal coordinates are summarized. It is shown that the ideal space frame is a general and necessary component of basic celestial mechanics and astrodynamics, as well as of any theory of motion. A typical consequence is the intimate correlation of the Hansen frame with the Lagrange constraint within the method of the variation of the parameters. The use of observations in the ideal frame may allow conclusions on the intergalactic fundamental coordinate system.

This paper analyses the fuel consumption of interferometric radar missions employing small satellite formations like, e.g., Cross-track Pendulum, Cartwheel, CarPe, or Trinodal Pendulum. Individual analytic expressions are provided for each of the following contributions: separation from a simultaneously injected master satellite, formation set-up, orbit maintenance, formation maintenance, and distance maintenance. For this, a general system of equations is derived describing the relative motion of the small satellites in a co-rotating reference frame. The transformation into Keplerian elements is carried out. To evaluate fuel consumption, three master satellites are assumed in different orbital heights, which are typical for Earth observation missions. The size of the exemplarily analysed formations is defined by remote sensing aspects and their respective fuel requirements are estimated. Furthermore, a collision avoidance concept is introduced, which includes a formation separation and formation set-up after a desired time period.

The product quality with respect to radiometric accuracy of current and future space-borne synthetic aperture radar (SAR) systems depends on the grade of the utilized external calibration targets. This paper presents the first steps taken in designing and building a highly accurate active target (transponder), which exhibits high radar cross section (RCS) stability (in the order of a tenth of a decibel) over its lifetime even for future multiple mode SAR systems. Furthermore, the transponder allows the recording of the azimuth pattern and fully sampled pulses.

The realization of flat superlenses is a major application area of metamaterials. A slab of double negative (DNG) material is capable of imaging with a resolution below the diffraction limit. The focusing quality depends primarily on the amount by which the original spectrum of the source is restored behind the lens. Even a small deviation from the ideal case limits the spectrum of the transmitted field, which may result in a significant degradation of the focusing quality. In this work we determine the width of the transmitted spectrum as a function of the configuration parameters and establish a relation between the spectrum width and the imaging quality. Restrictions imposed on the focusing characteristics and difficulties arising in full wave simulations will be pointed out.

Zuverlässige, globale Aufklärung durch Fernfeld - erkundung erfordert die Fähigkeit zur Detektion, Erkennung und Identifikation interessierender Objekte unabhängig von Wetter und Tageszeit. Ein allgestütztes hochauflösendes Synthetisches Apertur Radar (SAR) System im Spotlight-Modus kann hierfür ein hilfreiches Instrument darstellen. Bei der Planung einer solchen Mission ist die Verfügbarkeit von vergleichbaren Radardaten wichtig. Diese sollten bei Messungen am Boden gewonnen werden können. Sie dienen z.B. der Analyse komplexer Objektsignaturen und dem Gewinn von Referenzsignaturen bspw. für die Simulation oder als Datenbank für Objekterkennungsaufgaben. Die Abteilung Aufklärung und Sicherheit des Instituts für Hochfrequenztechnik und Radarsysteme am Deutschen Zentrum für Luft- und Raumfahrt (DLR) unternahm Inverse SAR Messungen an einem Turm - drehstand um zwei- und dreidimensionale Signaturen zu erstellen.

Die Sicherheit von Personen und kritischen Infrastrukturen gewinnt zunehmend an Bedeutung. Passive Mikrowellensensoren erlauben eine tageszeitunabhängige und zerstörungsfreie Beobachtung und Untersuchung interessierender Objekte ohne künstliche Bestrahlung und bei fast allenWetterbedingungen. Das Eindringvermögen von Mikrowellen ermöglicht die Detektion verborgener Objekte.Verschiedenste Abbildungs- und Sondierungssysteme wurden beim Deutschen Zentrum für Luft- und Raumfahrt (DLR) entwickelt, welche nahezu das gesamte Spektrum zwischen 1 GHz und 150 GHz für eine Vielzahl von Anwendungen ausnutzen.

Over the last several years there has been a surge of interest in artificial materials because of their potential to expand the range of electromagnetic properties in materials. The so called metamaterials, also known as left-handed (LHM) or double-negative (DNG) materials with negative permittivity and permeability have attracted growing interest. An important application area is the realization of flat superlenses with imaging properties beyond that of conventional lenses. This work investigates the focusing properties of a lossless planar DNG slab with a relative permittivity and permeability both approaching the value -1. The relation between the imaging quality and the material parameters is examined both analytically and numerically. Results obtained from numerical simulations via the transmission line matrix method are compared to the analytical solution.

The Brazilian-German MAPSAR (Multi-Application Purpose SAR) mission is a proposal for a light and innovative L-band SAR sensor, based on INPE´s Multi-Mission Platform (500 kg class spacecraft). The main mission objectives are the assessment, management and monitoring of natural resources.

Satellite constellations for Earth observation are remarkably useful, powerful and exible tools, but their realisation and maintenance pose a big issue on costs. From a design engineering perspective building up a constellation with small and simple satellites is a key to contain or reduce costs, while from a mission operations engineering point of view, optimal constellation management is a key in cost reduction and an important performance driver. In this paper the problem of the optimisation of planning and scheduling the operations for a remote sensing satellite constellation is addressed.

Scientific small satellite missions for remote sensing with Synthetic Aperture Radar (SAR) payloads or high accuracy optical sensors, pose very strict requirements on the accuracy of the reconstructed satellite positions, velocities and accelerations. Today usual GPS receivers can fulfill the accuracy requirements of this missions in most cases, but for low-cost-missions the decision for a appropriate satellite hardware has to take into account not only the reachable quality of data but also the costs. In this paper an analysis is carried out in order to assess which on board and ground equipment, which type of GPS data and processing methods are most appropriate to minimize mission costs and full satisfying mission payload requirements focusing the attention on a SAR payload.

Scientific small satellite missions for remote sensing with Synthetic Aperture Radar (SAR) payloads or high accuracy optical sensors, pose very strict requirements on the accuracy of the reconstructed satellite positions, velocities and accelerations. Today usual GPS receivers can fulfill the accuracy requirements of this missions in most cases, but for low-costmissions the decision for a appropriate satellite hardware has to take into account not only the reachable quality of data but also the costs. An analysis is carried out in order to assess which on board and ground equipment, which type of GPS data and processing methods are most appropriate to minimize mission costs and full satisfying mission payload requirements focusing the attention on a SAR payload.

The next Generation of Global Satellite Navigation Systems (GNSS - GPS and Galileo) will provide enhanced performance with respect to signals, services, quality and quantity of measurements, availability, integrity and accuracy. Since in 2004, the European Commission (EC) and the USA signed an agreement about the compatibility and interoperability between GPS and Galileo, the combined use of both systems and respective measurements will have a significant impact on the design of future navigation systems in general. This paper will present results from simulations for a SAR formation flying mission. Applicability, advantages and limitations of GNSS based concepts for orbit- and attitude determination in the context of SAR formation missions in LEO will be outlined and discussed.

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.