Interferometric Coherence Images Research Articles

Monitoring erosion in tropical savannas from C-band radar coherence

Increased erosion related to climate and/or land cover change has adverse impacts on terrestrial and aquatic ecosystems. Mapping of erosion hotspots improves our ability to identify and potentially remediate the most active erosion sources. The topical savannas of the Great Barrier Reef catchments, in northeast Australia, are generating excessive sediment yields, primarily from gully erosion. To reduce the adverse impacts on marine ecosystems, there is an urgent need to identify priority erosion hotspots and implement mitigation measures. While repeat airborne LiDAR surveys allow subtle topographic change detection, they are cost-intensive for catchment-scale applications and their applicability is constrained by data avalability and detection treshold of ground level change. In contrast, satellite-based radar imagery can allow large scale tracking of geomorphic change at high temporal resolution. Here we apply a new method based on Sentinel-1 C-band radar images and Coherence Change Detection (CCD), where large stacks of interferometric coherence images are subdivided with rain gauge time-series for separation of erosion-rich and erosion-free coherence information. After correcting the former with the latter, the resulting corrected coherence maps are compared with differential elevation models derived from multitemporal LiDAR, regional scale gully delineation maps, maps of gullying potential and in-situ field verification. Our results demonstrate the promising potential of this technique in detecting gully erosion hotspots. The coherence loss indicating erosion/deposition is well detected in wide gully morphologies, however, the line-of-sight angle does not allow penetration into narrow linear gullies. Further, CCD detects sheetwash or rill erosion occurring in areas identified as at risk of gully expansion, which is commonly below detection threshold for multitemporal LiDAR datasets. When used with LiDAR-derived geomorphic change mapping and gullying potential maps, CCD allows identification of gully erosion dynamics and forecasting gully evolution and creation, which is critical for supporting mitigation measures.

Vertical autofocus for the phase screen in a turbulent ionosphere

The performance of spaceborne synthetic aperture radars (SARs) is affected by the Earth’s ionosphere. In particular, the ionospheric turbulence causes phase perturbations of the SAR signals, which may lead to image distortions. A convenient way to model those phase perturbations is by means of a phase screen. The latter is an infinitesimally thin layer positioned at a certain elevation above the Earth’s surface. The radar signal acquires an instant perturbation once its trajectory intersects the screen. The trajectory is a ray between the antenna and the target, and the magnitude of the perturbation is equal to the screen density at the intersection point. The density is a bivariate function of the coordinates along the screen. The coordinates of a specific intersection point are determined by the ray itself, as well as the screen elevation. Thus, the magnitude of the phase perturbation explicitly depends on the screen elevation. Accordingly, to compensate for the resulting image distortions one should be able to determine the elevation of the screen. In the paper, we develop an algorithm of vertical autofocus that derives this elevation from the received SAR data, given a pair of point scatterers in the target area. The proposed algorithm exploits a modification of the coherent interferometric imaging that was previously employed to reduce the effect of phase errors due to the trajectory uncertainty. In our analysis, we highlight the differences between this case and transionospheric propagation.

Coherent interferometric imaging in a random flow

This paper is concerned with the development of imaging methods to localize sources or reflectors in inhomogeneous moving media with acoustic waves that have travelled through them. A typical example is the localization of broadband acoustic sources in a turbulent jet flow for aeroacoustic applications. The proposed algorithms are extensions of Kirchhoff migration (KM) and coherent interferometry (CINT) which have been considered for smooth and randomly inhomogeneous quiescent media so far. They are constructed starting from the linearized Euler equations for the acoustic perturbations about a stationary ambient flow. A model problem for the propagation of acoustic waves generated by a fixed point source in an ambient flow with constant velocity is addressed. Based on this result imaging functions are proposed to modify the existing KM and CINT functions to account for the ambient flow velocity. They are subsequently tested and compared by numerical simulations in various configurations, including a synthetic turbulent jet representative of the main features encountered in actual jet flows.

Resolving and Analyzing Landfast Ice Deformation by InSAR Technology Combined with Sentinel-1A Ascending and Descending Orbits Data.

Detailed mapping of landfast ice deformation can be used to characterize the rheological behavior of landfast ice effectively and to improve sea ice modeling subsequently. In order to analyze the characteristics, trends and causes of deformation comprehensively and accurately, the Sentinel-1A ascending and descending orbits data were used to detect the horizontal and vertical deformation of the fast ice in the Baltic Sea. Firstly, the fast ice edge lines were acquired through feature extraction with interferometric coherence images and SAR amplitude images. Then, the deformation transformed model was constructed according to the geometric relationship of multi-orbits deformation measurements. Finally, the landfast ice deformations were resolved and the horizontal and vertical deformations were obtained. The results showed that the maximum deformation was—44 cm in horizontal direction and 16 cm in vertical direction within the fast ice region of 960 km2 during the time from 2 to 16 February 2018. The southwest wind was the principal reason for the deformation, which made the deformation mainly occur in the horizontal direction from east to west. Moreover, the inner fast ice kept stable due to the protection of outer consolidated ice. The results showed that the deformation trend and characteristics can be better understood by using InSAR technology that was combined with multi-orbits SAR data to resolve and analyze the landfast ice deformation.

THE EFFECTS OF COHERENCE CALCULATION ON FOREST HEIGHT ESTIMATION USING SINC MODEL

Abstract. In this paper, the effects of coherence on forest height estimation by SINC model based on Tandem-X InSAR data were explored. First, different coherence calculation methods and different window sizes were used to obtain interferometric coherence images. Then, the forest heights were obtained based on SINC model. Finally, the estimated forest heights were validated against reference data from airborne LIDAR CHM (Canopy height model, CHM). The results showed that the coherence calculation algorithm affect the forest height inversion results with SINC model. The algorithm using only phase information for coherence calculation show better performance than the other one using both magnitude and phase information. Meanwhile, window size selecting for coherence calculation also affect the forest height estimation results. In this study, window size with 9 × 9 shows best agreement with the forest height extracted from LiDAR CHM. The R2 and RMSE are 0.656 and 3.54 m, respectively.

Applications of SAR Interferometric Coherence Time Series: Spatiotemporal Dynamics of Geomorphic Transitions in the South‐Central Andes

AbstractSediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel‐1 data, we analyze sediment transport regimes for the south‐central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope‐to‐fluvial and (b) a fluvial‐to‐alluvial transition. While transitions within a given basin can be well‐constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment‐wide erosion rates and to topographic metrics used to indicate long‐term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation‐free settings at event to decadal timescales.

Sentinel-1 SAR interferometry for agriculture: description of an experiment in Oryol, Russia

In this work we describe an experiment to be carried out in the basin of Suhaya Orlitsa river (Oryol region, central part of European Russia) to compare in-situ measurements of soil moisture with estimates obtained using Synthetic Aperture Radar (SAR) interferometry. The Sentinel-1 mission of the European Space Agency (ESA), acquiring C-band SAR images regularly over all Earth regions since 2014 with a mean revisiting time of 6 days, is used. In-situ measurements of soil moisture are planned in a time interval of 3 hours in coincidence of each Sentinel-1 passage, using a temporal sampling of 15 minutes. Test measurements are planned at the end of the month of April, when the soil accumulates water. The aim of the experiment is to demonstrate the feasibility of using Sentinel-1 images to densify the network of in-situ measurements of soil moisture on the territory of Russia. The application of SAR interferometry is investigated as it requires less in-situ measurements than methods based on the use of radar cross-section and the inversion of models of electromagnetic scattering from natural surfaces. Examples of interferometric coherence and phase images obtained by processing Sentinel-1 images acquired on 20th September 2019 and 2nd October 2019 over the study area are shown.

Flood Area Detection Using PALSAR-2 Amplitude and Coherence Data: The Case of the 2015 Heavy Rainfall in Japan

Rapid detection of flood areas in all weather conditions is needed for monitoring and mitigating flood disasters. In this paper, we investigated flood area detection using L -band synthetic aperture radar data acquired by the Phased-Array-type L -band Synthetic Aperture Radar-2 (PALSAR-2) aboard the Advanced Land Observing Satellite-2 during the 2015 heavy rainfall disaster in Kanto and Tohoku areas of Japan. This paper comprehensively compared various observation conditions of PALSAR-2, such as incidence angles, resolutions, and polarizations. We successfully detected open-water flood and built-up area flood by thresholding amplitude and interferometric coherence images. On the basis of our results, we conclude that incidence angles of approximately 20–40°, linear polarization or surface scattering component (HH, VV, and HH + VV), and high resolution are the preferable conditions for flood area detection by PALSAR-2. The optimum threshold resulted in the following flood area detection accuracies: Kappa coefficient ( κ ) = 0.583 in open water and κ = 0.429 in built-up area. These results successfully demonstrate the feasibility of rapid flood monitoring using PALSAR-2 data.

Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

Snow cover in mountain areas is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/α , Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P vol from Pauli, λ3 from H/A/α , and Y vol from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.

Second-harmonic imaging in random media

We consider the problem of optical imaging of small nonlinear scatterers in random media. We propose an extension of coherent interferometric imaging (CINT) that applies to scatterers that emit second-harmonic light. We compare this method to a nonlinear version of migration imaging and find that the images obtained by CINT are more robust to statistical fluctuations. This finding is supported by a resolution analysis that is carried out in the setting of geometrical optics in random media. It is also consistent with numerical simulations for which the assumptions of the geometrical optics model do not hold.

Adaptive Pulse Compression for Range Focusing in SAR Imagery

Synthetic aperture radar (SAR) images are focused via range and azimuth compression. Typically, a matched filter is used for range compression. However, its inherent operation introduces target masking due to sidelobes, reducing the recognition and interpretability of the underlying targets. To optimize the focusing quality, adaptive pulse compression (APC) can be applied. In this paper, APC is assessed for range processing in SAR image focusing. The performance of the method is evaluated with airborne SAR imagery and compared with those of traditional schemes. We show that by applying APC under single-channel interferometric and stepped frequency scenarios, the quality of the SAR images, the corresponding phase maps, and interferometric coherence images can be significantly improved, and that APC provides both better amplitude and phase estimates of the range profiles. APC is shown to preserve the characteristics of the azimuth signals and can be applied without restricting the azimuth compression scheme.

Imaging in Random Media with Convex Optimization

We study an inverse problem for the wave equation where localized wave sources in random scattering media are to be determined from time resolved measurements of the waves at an array of receivers. The sources are far from the array, so the measurements are affected by cumulative scattering in the medium, but they are not further than a transport mean free path, which is the length scale characteristic of the onset of wave diffusion that prohibits coherent imaging. The inversion is based on the coherent interferometric (CINT) imaging method, which mitigates the scattering effects by introducing an appropriate smoothing operation in the image formation. This smoothing stabilizes the images statistically, at the expense of their resolution. We complement the CINT method with a convex ($l_1$) optimization in order to improve the source localization and obtain quantitative estimates of the source intensities. We analyze the method in a regime where scattering can be modeled by large random wavefront distortio...

Extracting Snow Cover in Mountain Areas Based on SAR and Optical Data

Snow cover in cold and arid regions is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Interferometric synthetic aperture radar (InSAR) technology offers the ability to monitor snow cover in all weather. In this letter, a support vector machine (SVM) method for extracting snow cover based on SAR and optical data in rugged mountain terrain is introduced. In this method, RadarSat-2 InSAR interferometric coherence images are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” The analysis results indicate that the coherence in copolarizations is clearly correlated with the underlying surface type and local incidence angle. These two factors, combined with training samples from GF-1 wide field viewer data, were used to build an SVM to classify coherence images in HH polarization. The classification results demonstrate that snow cover extraction using this method can achieve mean accuracies of 83.8% and 77.5% in areas with low and high vegetation coverage, respectively. These accuracies are significantly higher than those achieved by the typical thresholding algorithm (72.7% and 69.2%, respectively).

Analysis of L-band SAR backscatter and coherence for delineation of land-use/land-cover

In this study, we investigated the potential improvement of land-use/land-cover (LU/LC) classification using multidate backscatter intensity as well as interferometric coherence images derived from Advanced Land Observing Satellite phased array L-band synthetic aperture radar data. Four interferometric synthetic aperture radar data pairs in horizontal–horizontal polarizations were processed to obtain backscatter intensity and coherence images. From the analysis of these images, it was observed that backscatter values alone are not sufficient to separate certain LU/LC classes, e.g. forest and mining areas, due to similarities in the associated scattering mechanisms producing similar backscatter values. However, the temporal coherence values from these LU/LC features were found to be distinctly different. Supervised classifications using maximum-likelihood distance were performed with various combinations of data (three-date backscatter intensity and two-date backscatter intensity with corresponding coherence data) to generate LU/LC maps of the study area. The comparison of classification accuracies obtained for different combinations of data indicates that the classification accuracy is improved by adding coherence information to the backscatter intensity data compared to using the multidate backscatter intensity data alone. Thus, the analysis of backscatter intensity along with coherence is a better alternative than using backscatter intensity alone to improve the accuracy in LU/LC classification.

Paraxial Coupling of Propagating Modes in Three-Dimensional Waveguides with Random Boundaries

We analyze long range wave propagation in three-dimensional random waveguides. The waves are trapped by top and bottom boundaries, but the medium is unbounded in the two remaining directions. We consider scalar waves, and motivated by applications in underwater acoustics, we take a pressure release boundary condition at the top surface and a rigid bottom boundary. The wave speed in the waveguide is known, but the top boundary has small random fluctuations that cause significant cumulative scattering of the waves over long distances of propagation. To quantify the scattering effects, we study the evolution of the random amplitudes of the waveguide modes. We obtain that in the long range limit they satisfy a system of paraxial equations driven by a Brownian field. We use this system to estimate three important mode-dependent scales: the scattering mean free path, the cross-range decoherence length, and the decoherence frequency. Understanding these scales is important in imaging and communication problems, because they encode the cumulative scattering effects in the wave field measured by remote sensors. As an application of the theory, we analyze time reversal and coherent interferometric imaging in strong cumulative scattering regimes.

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.

Enhanced statistical stability in coherent interferometric imaging

We analyze the resolution and statistical fluctuations of images when the ambient medium is random and scattering can be modeled primarily by wavefront distortion. We compare the coherent interferometric imaging method to the widely used Kirchhoff migration and show how the latter loses statistical stability at an exponential rate with the distance of propagation. In Kirchhoff migration we form images by superposing the array data back-propagated to the image domain. In coherent interferometry, we back-propagate local cross-correlations of the array data. This is a denoising process that enhances the signal-to-noise ratio of images but also reduces the resolution. We quantify analytically the trade-off between enhanced stability and reduced resolution in coherent interferometric imaging.

Coherent interferometric imaging, time gating and beamforming

Coherent interferometric imaging is based on the backpropagation of local spacetime cross-correlations of array data and was introduced in order to improve images when the medium between the array and the object to be imaged is inhomogeneous and unknown (Borcea et al 2005 Inverse Problems 21 1419). Although this method has been shown to be effective and is well founded theoretically, the coherent interferometric imaging function is computationally expensive and therefore difficult to use. In this paper, we show that this function is equivalent to a windowed beamformer energy function, that is, a quadratic function that involves only time gating and time delaying signals in emission and in reception. In this form the coherent interferometric imaging can be implemented efficiently both in hardware and software, that is, at a computational cost that is comparable to the usual beamforming and migration imaging methods. We also revisit the trade-off between enhanced image stability and loss of resolution in coherent interferometry from the point of view of its equivalence to a windowed beamformer energy imaging function.

Monitoring a boreal wildfire using multi-temporal Radarsat-1 intensity and coherence images

Twenty-five C-band Radarsat-1 synthetic aperture radar (SAR) images acquired from the summer of 2002 to the summer of 2005 are used to map a 2003 boreal wildfire (B346) in the Yukon Flats National Wildlife Refuge, Alaska under conditions of near-persistent cloud cover. Our analysis is primarily based on the 15 SAR scenes acquired during arctic growing seasons. The Radarsat-1 intensity data are used to map the onset and progression of the fire, and interferometric coherence images are used to qualify burn severity and monitor post-fire recovery. We base our analysis of the fire on three test sites, two from within the fire and one unburned site. The B346 fire increased backscattered intensity values for the two burn study sites by approximately 5–6 dB and substantially reduced coherence from background levels of approximately 0.8 in unburned background forested areas to approximately 0.2 in the burned area. Using ancillary vegetation information from the National Land Cover Database (NLCD) and information on burn severity from Normalized Burn Ratio (NBR) data, we conclude that burn site 2 was more severely burned than burn site 1 and that C-band interferometric coherence data are useful for mapping landscape changes due to fire. Differences in burn severity and topography are determined to be the likely reasons for the observed differences in post-fire intensity and coherence trends between burn sites.

Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data

In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.