The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies. Full article

Sentinel-1A and -1B are twin spaceborne synthetic aperture radar (SAR) sensors developed and operated by the European Space Agency under the auspices of the Copernicus Earth observation programme. Launched in April 2014 and April 2016, Sentinel-1A and -1B are currently operating in tandem, in a common orbital configuration to provide an increased revisit frequency. In-orbit commissioning was completed for each unit within months of their respective launches, and level-1 SAR products generated by the operational SAR processor have been geometrically calibrated. In order to compare and monitor the geometric characteristics of the level-1 products from both units, as well as to investigate potential improvements, products from both satellites have been monitored since their respective commissioning phases. In this study, we present geolocation accuracy estimates for both Sentinel-1 units based on the time series of level-1 products collected thus far. While both units were demonstrated to be performing consistently, and providing SAR data products according to the nominal product specifications, a subtle beam- and mode-dependent azimuth bias common to the data from both units was identified. A method for removing the bias is proposed, and the corresponding improvement to the geometric accuracies is demonstrated and quantified. Full article

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I²EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm. Full article

High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical Model Functions (GMF) used for SAR wind retrieval are not fully validated here. Ground based scanning light detection and ranging (LiDAR) offer high horizontal resolution wind velocity measurements with high accuracy, also in the coastal zone. This study, for the first time, examines accuracies of SAR wind retrievals at 10 m height with respect to the distance to shore by validation against scanning LiDARs. Comparison of 15 Sentinel-1A wind retrievals using the GMF called C-band model 5.N (CMOD5.N) versus LiDARs show good agreement. It is found, when nondimenionalising with a reference point, that wind speed reductions are between 4% and 8% from 3 km to 1 km from shore. Findings indicate that SAR wind retrievals give reliable wind speed measurements as close as 1 km to the shore. Comparisons of SAR winds versus two different LiDAR configurations yield root mean square error (RMSE) of 1.31 ${\mathrm{ms}}^{-1}$ and 1.42 ${\mathrm{ms}}^{-1}$ for spatially averaged wind speeds. Full article

Sentinel-1B is the second of two C-Band Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 mission, launched in April 2016—two years after the launch of the first satellite, Sentinel-1A. In addition to the commissioning of Sentinel-1B executed by the European Space Agency (ESA), an independent system calibration was performed by the German Aerospace Center (DLR) on behalf of ESA. Based on an efficient calibration strategy and the different calibration procedures already developed and applied for Sentinel-1A, extensive measurement campaigns were executed by initializing and aligning DLR’s reference targets deployed on the ground. This paper describes the different activities performed by DLR during the Sentinel-1B commissioning phase and presents the results derived from the analysis and the evaluation of a multitude of data takes and measurements. Full article

This paper discusses the potential and limitations of high-resolution P-band polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) in underlying topography estimation over forest areas. Time-frequency (TF) analysis in the azimuth direction is utilized to separate the ground scattering contribution from the total PolInSAR signal, without the use of any physical model, because the P-band PolInSAR data have a significant penetration depth and sufficient observation angle interval. To achieve this goal, a one-dimensional polynomial fitting (PF) method is proposed for correcting the residual motion error (RME). The Krycklan catchment test site, which is covered with pine forest, was selected to test the performance of the digital elevation model (DEM) inversion. The results show that the PF method can correct the RMEs for the sub-look interferograms well. When compared to the existing line-fit method, the TF+PF method can provide a more accurate DEM (the accuracy is improved by 26.9%). Moreover, the performance of the DEM inversion is free from the random-volume-over-ground assumption. Full article

A polarimetric ground-based radar (GB-radar) system operated in the L-band has been developed. The frequency range of GB-radar is 1.215 to 1.3 GHz, which is the same as that of Japanese satellite-borne SAR, PALSAR-2 (Phased Array type L-band Synthetic Aperture Radar), and Japanese L-band air-borne radar, i.e., Pi-SAR-L2 (Polarimetric and Interferometric Airborne Synthetic Aperture Radar L2). Polarimetric calibration was carried out twice in the field to calibrate and validate the GB-radar data. Cross-talk and channel imbalance are improved for the both experiments, and are from −13.3 dB to −30.7 dB, and from 1.06 to 1.00, respectively for the first experiment, after calibration. The calibrated cross-talk and channel imbalance values were comparable to −31.7 dB and 1.013, which were obtained using PALSAR. Radiometric calibration and antenna pattern correction were also carried out in the second experiment. Forest observations were also carried out simultaneously by GB-radar and Pi-SAR-L2 in the second experiment. The range profiles obtained by GB-radar and Pi-SAR-L2 were compared for several polarimetric parameters, namely, radar backscattering coefficient, polarimetric coherence, eigenvalue-decomposition parameters, and four-component-decomposition parameters. Both range profiles matched moderately well and showed good performance that could compensate for the limited possibility of satellite/air-borne SAR observation. Full article

This paper reports the validation results of burst misalignment for ScanSAR-ScanSAR interferometry of the Phased Array-type L-band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”). After the internal software modification on 8 February 2015, ALOS-2 ScanSAR observation mode archives have been available for use in interferometric analysis, as it was planned. However, it has not been reported whether its burst misalignment satisfies the mission requirements: 90% or higher burst overlap ratio. The validation results in this paper confirm that the expected observation misalignment satisfies the required range, including minimal seasonal and orbital dependencies. The results in this paper are obtained directly from the azimuth offset of the single look complex (SLC) data and are not derived from the orbit records. Nine identical orbits and frame numbers are chosen and evaluated to investigate the average burst misalignment and orbital dependency. Full article

The spatial separation of the transmitter and receiver in Bistatic Synthetic Aperture Radar (BiSAR) makes it a promising and useful supplement to a classical Monostatic SAR system (MonoSAR). This paper proposes a novel integrated time and phase synchronization strategy for a multichannel spaceborne-stationary BiSAR system. Firstly, the time synchronization strategy is proposed, which includes Pulse Repetition Frequency (PRF) generation under noisy conditions, multichannel calibration and the alignment of the recorded data with the orbital data. Furthermore, the phase synchronization strategy, which fully considers the deteriorative factors in the BiSAR configuration, is well studied. The contribution of the phase synchronization strategy includes two aspects: it not only compensates the phase error, but also improves the Signal to Noise Ratio (SNR) of the obtained signals. Specifically, all direct signals on different PRF time can be reconstructed with the shift and phase compensation operation using a reference signal. Besides, since the parameters of the reference signal can be estimated only once using the selected practical direct signal and a priori information, the processing complexity is well reduced. Final imaging results with and without compensation for real data are presented to validate the proposed synchronization strategy. Full article

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce–sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of ~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (>10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies. Full article