Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude | | and phase � are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves–upon calibration with airborne L band data–median correlations � at HH polarization of 0.77 for the phase �, of 0.50 for | |, and for the phase triplets � of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture

Topological Characterization and Advanced Noise-Filtering Techniques for Phase Unwrapping of Interferometric Data Stacks

This chapter addresses the problem of phase unwrapping interferometric data stacks, obtained by multiple SAR acquisitions over the same area on the ground, with a twofold objective. First, a rigorous gradient-based formulation for the multichannel phase unwrapping (MCh-PhU) problem is systematically established, thus capturing the intrinsic topological character of the problem. The presented mathematical formulation is consistent with the theoretical foundation of the discrete calculus. Then within the considered theoretical framework, we formally describe an innovative procedure for the noise filtering of time-redundant multichannel multilook interferograms. The strategy underlying the adopted multichannel noise filtering (MCh-NF) procedure arises from the key observation that multilook interferograms are not fully time consistent due to multilook operations independently applied on each single interferogram. Accordingly, the presented MCh-NF procedure suitably exploits the temporal mutual relationships of the interferograms. Finally, we present some experimental results on real data and show the effectiveness of our approach applied within the well-known small baseline subset (SBAS) processing chain, thus finally retrieving the relevant Earth’s surface deformation time series for geospatial phenomena analysis and understanding

A-DInSAR Monitoring of Landslide and Subsidence Activity: A Case of Urban Damage in Arcos de la Frontera, Spain

Terrain surface displacements at a site can be induced by more than one geological process. In this work, we use advanced differential interferometry SAR (A-DInSAR) to measure ground deformation in Arcos de la Frontera (SW Spain), where severe damages related to landslide activity and subsidence have occurred in recent years. The damages are concentrated in two residential neighborhoods constructed between 2001 and 2006. One of the neighborhoods, called La Verbena, is located at the head of an active retrogressive landslide that has an extension of around 0.17 × 106 m2 and developed in weathered clayey soils. Landslide motion has caused building deterioration since they were constructed. After a heavy rainfall period in winter 2009–2010, the movement was accelerated, worsening the situation. The other neighborhood, Pueblos Blancos, was built over a poorly compacted artificial filling undergoing a spatially variable consolidation process which has also led to severe damage to buildings. For both cases, a short set of C-band data from the “ENVISAT 2010+” project has been used to monitor surface displacement for the period spanning April 2011–January 2012. In this work we characterize the mechanism of both ground deformation processes using in situ and remote sensing techniques along with a detailed geological interpretation and urban damage distribution

Experimental Synthetic Aperture Radar with Dynamic Metasurfaces

We investigate the use of a dynamic metasurface as the transmitting antenna for a synthetic aperture radar (SAR) imaging system. The dynamic metasurface consists of a one-dimensional microstrip waveguide with complementary electric resonator (cELC) elements patterned into the upper conductor. Integrated into each of the cELCs are two diodes that can be used to shift each cELC resonance out of band with an applied voltage. The aperture is designed to operate at K band frequencies (17.5 to 20.3 GHz), with a bandwidth of 2.8 GHz. We experimentally demonstrate imaging with a fabricated metasurface aperture using existing SAR modalities, showing image quality comparable to traditional antennas. The agility of this aperture allows it to operate in spotlight and stripmap SAR modes, as well as in a third modality inspired by computational imaging strategies. We describe its operation in detail, demonstrate high-quality imaging in both 2D and 3D, and examine various trade-offs governing the integration of dynamic metasurfaces in future SAR imaging platforms

An intensity triplet for the prediction of systematic InSAR closure phases

Thesis (M.S.) University of Alaska Fairbanks, 2023Synthetic Aperture Radar (SAR), a microwave-based active remote sensing technique, has had a rich and contemporary history. Because such platforms can measure both the phase and intensity of the reflected signal, interferometric SAR (InSAR) has proliferated and allowed geodesists to measure topography and millimeter-to-centimeter scale deformations of the Earth's surface from space. Applications of InSAR range from measuring the inflation of volcanoes caused by magma movement to measuring the subsidence in permafrost environments caused by the thawing of ground ice. Advancements in InSAR time series algorithms and speckle models have allowed us to image such movements at increasingly high precision. However, analysis of closure phases (or phase triplets), a quantification of inconsistencies thought to be caused by speckle, reveal systematic behaviors across many environments. Systematic closure phases have been linked to changes in the dielectric constant of the soil (generally thought to be a result of soil moisture changes), but existing models require strong constraints on structure and sensitivity to moisture content. To overcome this obstacle and decompose the closure phase into a systematic and stochastic part, we present a data-driven approach based on the SAR intensities. Intensity observations are also sensitive to surface dielectric changes. Thus, we have constructed an intensity triplet that mimics the algebraic structure of the closure phase. A regression between such triplets allows us to predict the systematic part of the closure phase, which is associated with dielectric changes. We estimate the corresponding phase errors using a minimum-norm inversion of the systematic closure phases to inspect the impact of such systematic closure phases on deformation measurements. Correction of these systematic closure phases that correlate with our intensity triplet can account for millimeter-scale fluctuations of the deformation time series. In permafrost environments, they can also account for displacement rate biases up to a millimeter a month. In semi-arid environments, these differences are generally an order of magnitude smaller and are less likely to lead to displacement rate biases. From nearby meteorological stations, we attribute these errors to snowfall, freeze-thaw, as well as seasonal moisture trends. This kind of analysis shows great potential for correcting the temporal inconsistencies in InSAR phases related to dielectric changes and enabling even finer deformation measurements, particularly in permafrost tundra.Chapter 1. Introduction. Chapter 2. InSAR theory -- 2.1. Forming an interferogram -- 2.2. Time series estimation -- 2.3. Closure phases. Chapter 3. Predicting and removing systematic phase closures -- 3.1. An intensity triplet -- 3.2. Predicting systematic closure phases -- 3.2.1. Model -- 3.2.2. Parameter estimation -- 3.3. Significance testing -- 3.4. Inversion. Chapter 4. Data and preprocessing -- 4.1. Las Vegas, NV -- 4.2. Dalton Highway, AK -- 4.3. Ancillary processing. Chapter 5. Results -- 5.1. Overview -- 5.2. Coefficient of determination -- 5.3. Slope estimates -- 5.4. Intercept estimates -- 5.5. Impacts on deformation estimates. Chapter 6. Discussion -- 6.1. Variability in R2 and slope estimates -- 6.2. Implications for deformation estimates -- 6.3. Implications for observations of land surface properties -- 6.4. Unexplained systematic closure phases -- 6.5. Model improvements. Chapter 7. Conclusion -- References -- Appendices

Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM)

https://www.mdpi.com/2072-4292/12/21/348

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