Synthetic Aperture Radar Acquisitions Research Articles

Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis.

SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition

Abstract. Automated sea ice charting from synthetic aperture radar (SAR) has been researched for more than a decade, and we are still not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a data set from 20 near-coincident x-band SAR acquisitions and as many airborne laser scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This data set is then used to assess the accuracy and robustness of five machine-learning-based approaches by deriving classes from the freeboard, surface roughness (standard deviation at 0.5 m correlation length) and reflectance. It is shown that there is only a weak correlation between the radar backscatter and the sea ice topography. Accuracies between 44 % and 66 % and robustness between 71 % and 83 % give a realistic insight into the performance of modern convolutional neural network architectures across a range of ice conditions over 8 months. It also marks the first time algorithms have been trained entirely with labels from coincident measurements, allowing for a probabilistic class retrieval. The results show that segmentation models able to learn from the class distribution perform significantly better than pixel-wise classification approaches by nearly 20 % accuracy on average.

Mass changes of the northern Antarctic Peninsula Ice Sheet derived from repeat bi-static synthetic aperture radar acquisitions for the period 2013–2017

Abstract. Some of the highest specific mass change rates in Antarctica are reported for the Antarctic Peninsula. However, the existing estimates for the northern Antarctic Peninsula (<70∘ S) are either spatially limited or are affected by considerable uncertainties. The complex topography, frequent cloud cover, limitations in ice thickness information, boundary effects, and uncertain glacial–isostatic adjustment estimates affect the ice sheet mass change estimates using altimetry, gravimetry, or the input-output method. Within this study, the first assessment of the geodetic mass balance throughout the ice sheet of the northern Antarctic Peninsula is carried out employing bi-static synthetic aperture radar (SAR) data from the TanDEM-X satellite mission. Repeat coverages from the austral winters of 2013 and 2017 are employed. Overall, coverage of 96.4 % of the study area by surface elevation change measurements and a total mass budget of -24.1±2.8 Gt a−1 are revealed. The spatial distribution of the surface elevation and mass changes points out that the former ice shelf tributary glaciers of the Prince Gustav Channel, Larsen A and B, and Wordie ice shelves are the hotspots of ice loss in the study area and highlights the long-lasting dynamic glacier adjustments after the ice shelf break-up events. The highest mass change rate is revealed for the Airy–Seller–Fleming glacier system at -4.9±0.6 Gt a−1, and the highest average surface elevation change rate of -2.30±0.03 m a−1 is observed at Drygalski Glacier. The comparison of the ice mass budget with anomalies in the climatic mass balance indicates, that for wide parts of the southern section of the study area, the mass changes can be partly attributed to changes in the climatic mass balance. However, imbalanced high ice discharge drives the overall ice loss. The previously reported connection between mid-ocean warming along the southern section of the west coast and increased frontal glacier recession does not repeat in the pattern of the observed glacier mass losses, excluding in Wordie Bay. The obtained results provide information on ice surface elevation and mass changes for the entire northern Antarctic Peninsula on unprecedented spatially detailed scales and with high precision and will be beneficial for subsequent analysis and modeling.

COSMO-SKYMED: A SATELLITE TOOL FOR MONITORING CULTURAL HERITAGE

Abstract. COSMO-SkyMed is the flagship Synthetic Aperture Radar (SAR) satellite constellation of the Italian Space Agency (ASI) that, among the many civilian applications for which it was originally conceived, is nowadays successfully exploited for cultural heritage applications. Current capabilities offered by both the First and Second Generations satellites are reviewed in light of the experience undertaken by ASI through data exploitation initiatives with scientific and commercial users, and more importantly in the framework of institutional projects and cooperation agreements such as those with the Colosseum Archaeological Park and the Italian Ministry of Culture (MiC). The interferometric nature of COSMO-SkyMed SAR acquisitions is the key feature exploited in structural and ground deformation monitoring of monuments and historical buildings, so as the constellation is currently the essential Earth Observation asset supporting MiC’s Extraordinary Plan of Monitoring and Conservation of Immovable Cultural Heritage. Further applications that could be enabled by COSMO-SkyMed data are explored through internal ASI research activity, such as the use of high resolution Digital Elevation Models (DEM) for topographic surveying of archaeological tells and condition assessment to estimate the impact due to natural and anthropogenic threats (e.g. looting, agriculture, destruction). Finally, current perspectives towards operational use and greater user uptake of COSMO-SkyMed for purposes of scientific downstream are opened by the new ASI programme Innovation for Downstream Preparation for Science (I4DP_SCIENCE).

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and “ASCENDING” orbits outperformed those involving copolarization, a single time period and “DESCENDING” orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS

Abstract. Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in the 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when a large number of SAR observations. In this study, with a limited number of SAR images, we have assessed the efficiency of the nonparametric spectral estimation methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) in the reconstruction of the third dimension of urban buildings. The experiments are conducted on both simulated and TerraSAR-X stripmap images to indicate the effectiveness of the LP proposed estimation algorithm. The analysis of the results proves that by minimizing the average output signal power over the antenna array elements, the LP spectral estimation achieves the discrimination of distinct scatterers inside an image pixel. In addition, this low computational estimator improves the sidelobe suppression and the height estimates of the scatterers in the complex multiple-scattering urban environment. Compared to SVD, maximum entropy, Capon, minimum norm, and beamforming, the height of the Eskan tower in Tehran, Iran, obtained with the LP technique, is considerably near to field-based measurement.

3-D Stereo Geolocation of Radar Reflectors Using Multiaspect SAR Acquisitions

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively.

A Generalized Geodesic Distance-Based Approach for Analysis of SAR Observations Across Polarimetric Modes

Present and future sensors are diversifying from traditional quad polarimetric mode of synthetic aperture radar acquisition. Thus, an approach that is interpretative in nature and applicable across polarimetric modes is required. In this context, the geodesic distance (GD)-based approach within the polarimetric synthetic aperture radar (PolSAR) literature is seen as an eigenvalue-decomposition free approach to interpret and analyze quad PolSAR data. This approach is highly adaptive toward applications due to its ability to compare the SAR observation with a known scatterer/model, or with another SAR observation in general providing a means for direct interpretation. In this work, we show that the GD (originally defined for the quad polarization mode) is generalizable across any arbitrary SAR polarimetric mode while retaining its simple form for ready computation. We show that the GD-based approach provides level ground for comparison of different polarimetric modes given a fixed application. We demonstrate it using change detection as the chosen application. In addition, we show how the behavior of the three roll-invariant GD-based parameters change under different polarimetric modes (e.g., quad, dual, and compact polarization modes). We also discuss how the GD-based approach can also be adapted to ground range detected (GRD) product data, which is presently available from Sentinel-1 and widely used in many applications. However, in this case, we show that only one of the three GD-derived parameters can be defined. We believe this work will make the GD-based approach important for present and future PolSAR applications cutting across sensors and its available polarimetric modes.

Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles.

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition's geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with and RMSE CV = 0.88 m2 m-2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloudprone agri-environments.

Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.

Quantifying Large‐Scale Surface Change Using SAR Amplitude Images: Crater Morphology Changes During the 2019–2020 Shishaldin Volcano Eruption

AbstractMorphological processes often induce meter‐scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote‐sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross‐section, we estimate meter‐scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high‐quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR‐X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co‐registered TerraSAR‐X high‐resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter‐scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.

Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events

Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition (Δt). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI ≤0.7 and NDVI >0.7). The main results showed that when the vegetation is moderately developed (NDVI ≤0.7), the C-band temporal variation remains sensitive to the soil moisture dynamics and the irrigation events could be detected. The C-VV signal decreases due to the drying out of the soil when the time difference between the S1 image and irrigation event increases. For well-developed vegetation cover (NDVI >0.7), the C-band sensitivity to irrigation events becomes dependent on the crop type. For well-developed Gramineae grass with longs stalks and seedheads, the C band shows no correlation with Δt due to the absence of the soil contribution in the backscattered signal, contrary to the legume grass type, where the C band shows a good correspondence between C-VV and Δt for NDVI > 0.7. In contrast, analysis of the L-band backscattering coefficient shows that the L band remains sensitive to the soil moisture regardless of the vegetation cover development and the vegetation characteristics, thus being more suitable for irrigation detection than the C band. The L-HH signal over Gramineae grass or legume grass types shows the same decreasing pattern with the increase in Δt, regardless of the NDVI-values, presenting a decrease in soil moisture with time and thus high sensitivity of the radar signal to soil parameters. Finally, the co-polarizations for both the C and L bands (L-HH and C-VV) tend to be more adequate for irrigation detection than the HV cross-polarization, as they show higher sensitivity to soil moisture values.

A method for structural monitoring of multispan bridges using satellite InSAR data with uncertainty quantification and its pre-collapse application to the Albiano-Magra Bridge in Italy

Synthetic Aperture Radar Interferometry (InSAR) using satellite data is revealing a promising tool for monitoring long-term deformation phenomena in critical infrastructural systems. Nevertheless, its use in structural engineering is still quite limited and a general understanding of its potential is still missing, especially when dealing with bridge structures for which specific methods of data processing and displacement assessment with error quantification need to be developed, accounting for the type of deformation phenomena and for the orientation of the bridge. In order to partly fill this research gap, this paper proposes a post-processing methodology to derive two-dimensional displacement configurations of multi-span bridges with properly defined error bounds, using both ascending and descending Synthetic Aperture Radar acquisitions. In order to obtain an engineering meaningful estimate of the uncertainties affecting the reconstructed bridge deformations, both random and systematic errors are quantified, accounting for the orientation of the bridge with respect to the Line-Of-Sights of the satellites, the hypothesized deformation plane and the accuracy of InSAR measurements. The proposed procedure has been applied to the illustrative case study of the Albiano-Magra Bridge in Italy, collapsed on 8 April 2020. The results, referred to the monitoring period 2015–2020, demonstrate the effectiveness of the proposed method in supporting engineering assessments. In particular, an initially temperature-induced stationary deformation phenomenon has been observed, with all spans moving upwards or downwards during summer and winter. Afterwards, displacements of increasing amplitude for two side spans have been observed during the three years preceding the failure, providing information on the possible cause of collapse.

Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds

AbstractAimThe increasing availability of remote sensing (RS) products from airborne laser scanning (ALS) surveys, synthetic aperture radar acquisitions and multispectral satellite imagery provides unprecedented opportunities for describing the physical structure and seasonal changes of vegetation. However, the added value of these RS products for predicting species distributions and animal habitats beyond land cover maps remains little explored. Here, we aim to assess how metrics derived from different types of high‐resolution (10 m) RS products predict the habitat suitability of wetland birds.LocationNorth‐eastern part of the Netherlands.MethodsWe built species distribution models (SDMs) with occurrence observations from territory mapping of two selected wetland bird species (great reed warbler and Savi's warbler) and metrics from a Dutch land cover map, country‐wide ALS and Sentinel‐1 and Sentinel‐2 RS products. We then compared model performance, relative variable importance and response curves of the SDMs to assess the contribution and ecological relevance of each RS product and metric.ResultsOur results showed that ALS and Sentinel metrics improve SDMs with only land cover metrics by 11% and 10% of the Area Under Curve (AUC) for the great reed warbler and the Savi's warbler respectively. Assessments of feature importance revealed that all types of RS products contributed substantially to predicting the habitat suitability of these wetland birds, but that the most important variables vary among species.Main conclusionsOur study demonstrates that metrics from different high‐resolution RS products capture complementary ecological information on animal habitats, including aspects such as the proportional cover of habitat types, vegetation density and the horizontal variability of vegetation height. Land cover maps with detailed spatial and thematic information can already achieve high model accuracies, but adding metrics derived from ALS point clouds and Sentinel imagery further improve model accuracy and enhance the understanding of animal–habitat relationships.

The comparative study of three nonparametric methods of SAR tomography for building reconstruction

Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods.

Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data

Vegetation cover significantly influences the hydrometeorological processes of land surfaces. The heterogeneity of vegetation cover makes these processes more complex and impacts the interaction between water held in the soil matrix and vegetation cover. The backscatter measured by Synthetic Aperture Radar (SAR) is sensitive to target dielectric and morphological properties. In addition, SAR acquisitions are weather-independent, providing a major advantage over optical imaging during periods of cloud cover. Most often, vegetation properties are measured using vegetation indices, including simple ratios of backscatter intensities. However, this approach can miss the scattered wave purity from vegetation targets. The scattered wave characteristic is an essential parameter as it is susceptible to the complex random structure of vegetation. In this study, we propose a novel dual-polarimetric SAR vegetation descriptor based on the co-pol purity component of the wave. We use this descriptor within a semi-empirical vegetation model to estimate soil moisture. It is noteworthy that this proposed method to retrieve soil moisture uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Therefore, the proposed method has the potential for operational scale monitoring applications at a global scale. This study validated different crop types (viz. canola, oats, forage, maize, soybeans, sugar beet, wheat, winter wheat, winter barley) over two test sites using airborne SAR data. The Root Mean Square Error (RMSE) values for soil moisture are within the range of 4.3% to 7.7% with the Pearson correlation coefficient r greater than 0.6. This novel descriptor will facilitate the operational use of dual-polarimetric GRD SAR data for vegetation monitoring and soil moisture estimation.The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

The Effects of Dry Snow on the SAR Impulse Response and Feasibility for Single Channel Snow Water Equivalent Estimation

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation.

Extraction of Absolute Water Level Using TanDEM-X Bistatic Observations With a Large Perpendicular Baseline

The application of Wetland synthetic aperture radar interferometry (InSAR) has often been restricted in practical hydrological monitoring because it is based on relative estimates of water level changes between two synthetic aperture radar acquisitions, as opposed to absolute water levels obtained by ground measurements. TanDEM-X bistatic observations can provide absolute water level estimates using simultaneous phase measurements by a two-satellite constellation with TerraSAR-X. We evaluated two datasets of TanDEM-X bistatic observations acquired during an experimental science phase on August 26 and 31, 2015, with a very large baseline configuration to extract absolute water levels of Everglades wetland in southern Florida, USA. The perpendicular baselines are 1.43 and 1.36 km, and the ambiguities of height were calculated as 3.61 and 3.90 m in each interferometric pair, respectively. Hourly water level measurements provided by the Everglades depth estimation network (EDEN) were used to verify the estimated absolute water levels. Several stage stations located in densely vegetated areas that showed incoherence were excluded from the verification as outliers. The verification results show an excellent agreement (degree of determination > 0.95) between the InSAR derived absolute water levels and the stage station measurements. The root mean square error (RMSE) between the TanDEM-X results and stage records was 0.77 and 0.66 m, respectively. Severe volume decorrelations over the vegetated area, owing to the large perpendicular baselines, were detected, despite near zero temporal baseline of the bistatic observations. The absolute water levels can be used as excellent constraints for wetland surface flow models.

Assessing the Potential of Fully Polarimetric Mono- and Bistatic SAR Acquisitions in L-Band for Crop and Soil Monitoring

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to the maize canopy row structure and biophysical variables, as well as to soil moisture and surface roughness in both vegetated and bare fields. The effect of the row structure of maize crops is assessed through the impact of the orientation of the planting rows relative to the sensor beam on microwave scattering measurements. The results of this analysis suggest that the row orientation of maize crops has a significant influence on both mono- and bistatic scattering measurements in both copolarizations, and especially, in HH, while the cross polarizations are not affected. Furthermore, the study also shows through a linear regression analysis that bistatic data, even with a very small bistatic baseline, can provide valuable additional information for maize crop biophysical variable retrieval, which however does not appear to be the case for soil moisture retrieval over bare soils.

Autofocus-Based Estimation of Penetration Depth and Permittivity of Ice Volumes and Snow Using Single SAR Images

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site.