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This work proposes the development of a software module for studying historical and cultural heritage objects using remote and non-invasive research data. The module aims to integrate modern technologies such as image processing, data analysis, and visualization to provide access to heritage information for researchers, conservators, and the general public. Utilizing non-invasive data, such as geophysical surveys, enables the collection of information about heritage objects without causing damage. The module facilitates the analysis and visualization of this data as well as the creation of interactive object models, thereby promoting further research, preservation, and popularization of cultural heritage. The module consists of three blocks: defining areas for ground-based research using interferograms; constructing a comprehensive 3D model based on ground and underground research data; and refining the boundaries of historical-cultural heritage objects and establishing protective zones. The program is developed in the object-oriented programming language VisualBasic with additional modules. This developed module could become a significant tool for studying and preserving historical and cultural objects in the modern world. [ABSTRACT FROM AUTHOR]

The first MW 7.7 and second MW 7.6 Kahramanmaraş earthquakes were the strongest tectonic events in Eastern Türkiye since the MW 7.9 Erzincan earthquake in 1939. The first and second events occurred on the Pazarcık and Çardak segments of the East Anatolian Fault System, respectively. A subsequent significant earthquake of MW 6.4 occurred in the Antakya fault zone. We summarize preliminary earthquake data and share our previous field observations to better understand the mechanism and provide scientific consequences for future investigations and layout planning. After combining the field observations, kinematic analysis of fault surfaces, investigations of orthophotos, characteristics of mainshocks, and differential interferometry analysis of Sentinel-1A synthetic aperture radar images are used to evaluate the surface deformation from an earth sciences point of view. As a result, field data, paleostress analysis, and deformation zones are found to be completely consistent with the focal mechanism solutions of recent earthquakes. Furthermore, the first MW 7.7 event would have been caused by motion along the Pazarcık and Amanos segments at the same time as a single seismic segment. [ABSTRACT FROM AUTHOR]

The beginning of radars goes back to the 1930s where its main boost was related to the second world war. Nowadays, the techniques associated with radars are focused around a vast variety of civil, geodetic, and military applications. The development of the synthetic aperture principle, in the 1950s and 1960s, gave birth to a lot of new applications, and together with the technological progress of the last decades, the technique of interferometry with synthetic aperture radar (SAR) data became one of the most powerful ones for sensing remotely, with high quality and a vast spatial coverage. We used Sentinel-1 data and the differential interferometry SAR (DinSAR) technique to map and measure the surface deformation related to the 2015 Mw 8.3 Illapel earthquake (Chile). We also validated the results, by analysing the temporal variation of coordinates acquired from global navigation satellite system observations and projecting them in the geometry of the SAR system. Using this application we prove the DinSAR technique to be useful and powerful for the observation and analysis of surface deformation caused by the release of stress during the Mw 8.3 Illapel earthquake. It proved to be an efficient tool to detect and map the surface deformation with high spatial resolution in an approximate area of 20,000 km2.

This work presents a new approach for the estimation of snow extent, height, and density in complex orography regions, which combines differential interferometric synthetic-aperture-radar (DInSAR) data and snowpack numerical model data through artificial neural networks (ANNs). The estimation method, subdivided into classification and estimation, is based on two ANNs trained by a DInSAR response model coupled with Alpine3D snow cover numerical model outputs. Auxiliary satellite training data from satellite visible-infrared MODIS imager as well as digital elevation and land cover models are used to discriminate wet and dry snow areas. For snow cover classification the ANN-based estimation methodology is combined with fuzzy-logic and compared with a consolidated decision threshold approach using C-band SAR backscattering information. For snow height (SH) and density estimation, the proposed methodology is compared with an analytical inverse method and two model-based statistical techniques (linear regression and maximum likelihood). The validation is carried out in Central Apennines, a mountainous area in Italy with an extension of about 104 km2 and peaks up to 2912 m, using in situ data collected between December 2018 and February 2019. Results show that the ANN-based technique has a snow cover area classification accuracy of more than 80% when compared MODIS maps. Estimation bias and root mean square error are equal to about 0.5 cm and 20 cm for SH and to 5 kg/m3 and 80 kg/m3 for snow density. As expected, worse results are associated with low DInSAR coherence between two repeat passes and snow melting periods.

To visualize the microscopic internal structure of cells, some conventional methods exist in the literature, such as irradiating fluorescent substances with light that maintains activation and light that suppresses activation to reduce the effective spot diameter or irradiating samples with moiré stripes of irradiating light and analyzing the pattern of the moiré stripes. This study proposes a high-resolution method to detect the intensity and optical path difference (OPD), representing the phase distribution reflecting the internal structure of the object, in a transmission image without using pigmentation for observing near-transparent objects such as cells or soft materials in liquids. First, the object is scanned using a laser beam. Second, the intensity owing to the interference between the 0th- and 1st-order diffracted light is detected using two photodetectors as electrical signals. The signals are separated into DC and AC components, with the intensity and phase separated through signal processing using their ratio. Two tilting optics are inclined toward each other at a symmetrical position with respect to the optical axis of the objective lens. The arrangement ensures that the incident diffracted light becomes part of the 0th-order diffracted light and ± 1st-order diffracted light at high spatial frequency. Thus, in contrast to conventional optical systems, high-resolution transmitted images can be obtained by acquiring high spatial frequency diffracted light. Moreover, the lens is placed close to the glass slide to inject the diffracted light with high spatial frequency into the lens of the oblique optical system. The proposed method obtains images of OPDs with a resolution higher than the reciprocal of the cutoff frequency of the numerical aperture of the objective lens used in the actual optical system. [ABSTRACT FROM AUTHOR]

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing'an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas. [ABSTRACT FROM AUTHOR]

Conventional deformation monitoring means are limited by data accuracy, monitoring range, applicable environment and other factors, which seriously restrict the early warning and forecasting of landslide and collapse disasters.The microwave remote sensing method based on differential interference technology is an advanced technology for high precision monitoring of small displacements on non-contact surfaces nowadays.The self-researched ground-based real aperture radar system was applied in the research.This paper, based on the analysis of the signal model, summarized the process of target echo data processing with strong scattering characteristics and proposed a method applicable to verify the deformation monitoring accuracy of this type of radar system.Point target stationary and displacement experiments based on triangular plate angle reflector were carried out accordingly.Through the radar echo amplitude analysis and point cloud data fitting, the pre-defined target space position was discriminated and the target displacement standard value was determined, which proved the effectiveness of the method and the 0.1mm monitoring accuracy of the system.A slope monitoring experiment was carried out in Heidaigou open-pit coal mine in Inner Mongolia which combined with some point target deformation data illustrated the good practicality of the system.

Landslide susceptibility assessment was adopted for the Idukki region using 6 machine learning models viz., Adaptive Boosting (AdaBoost), Naïve Bayes (NB), Neural Network (NNET), Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) models. Due to the severe calamity caused by landslides in the Idukki district during the August 2018 monsoon, the district was listed as one of the top hotspots for landslides, which is why this study focuses on them. Eighteen conditioning factors that influenced the occurrence of the landslides in the Idukki region were considered primarily in this study. However, the Recursive Feature Elimination (RFE) approach was adopted to eliminate the variables with low importance. The research focuses on the sensitivity of the causative factors and explores the conditioning parameters that are considered necessary by each machine learning algorithm. Furthermore, the Accuracy, Kappa index, ROC-AUC, Sensitivity, Specificity were computed to estimate the performance of the models. The RF model portrayed the best prediction accuracy in training and testing cases, followed by the AdaBoost and XGBoost models. However, while determining the consistency of the model based on its practical applicability by generating the landslide susceptibility maps, the AdaBoost and NNET model showed better consistency, and the XGBoost model displayed inconsistency. Thus, the AdaBoost model was the best in accuracy and practical consistency compared to the other five models. [ABSTRACT FROM AUTHOR]

Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

The stability of large-scale landslide in the reservoir area of hydropower station has an important impact on the safety of dam structure and surrounding people's life and property. Large-scale real-time accurate observation of the stability and deformation trend of this type of landslide can provide reliable early warning and control information for the landslide, which is of great significance. The traditional monitoring methods of landslide in the reservoir area of hydropower station are mainly GNSS monitoring and total station monitoring. In this study, the advanced GB-InSAR system LKR-05-KU-S100 is applied to the monitoring of Cangjiangqiao-Yingpan landslide and Dahua landslide of Dahuaqiao hydropower station on Lancang River. Field monitoring tests show that the system has high precision, and it can carry out long-distance, all-day, all-weather, and large-scale monitoring. It has unique advantages and broad application prospects for the monitoring of large and super-large landslides.

Surface subsidence is a dominant component of the displacement vector triggered by underground mining. Over the last few decades, Differential Interferometry Synthetic Aperture Radar (DInSAR) has been used to efficiently monitor this phenomenon with great spatial and temporal coverage. More advanced multi-temporal DInSAR (MTInSAR) algorithms have been proposed to overcome some of the limitations of conventional DInSAR. However, advanced MTInSAR approaches are also not perfect in terms of measuring mining subsidence (e.g., temporal decorrelation, ambiguity, nonlinearity). For this reason, we propose a fusion of the Persistent Scatterer Interferometry (PSInSAR) and DInSAR results. By combining these complementary techniques, the atmospheric errors in PSInSAR data are reduced and larger deformation rates could have been detected more accurately (thanks to DInSAR) than by an approach solely based on PS-InSAR. This allows to measure areas with fast-moving subsidence (1 m/year) due to ongoing underground coal exploitation. Data from ascending and descending orbits of Sentinel-1A\B were used to obtain the vertical deformation component. The resulting integrated vertical deformation map was compared with the results from levelling benchmarks. The Root Mean Square Error (RMSE) calculated based on this comparison was 22 mm. Moreover, the maximal vertical cumulative subsidence detected in the study area was 1.05 m/year.

The parameters, including diameter at breast height and tree height of tamarix chinensis forest in the study area, were inversed on basis of backscatter coefficient, polarimetric SAR, differential interferometry with DEM, and differential interferometry with polarization. Finally, main conclusions are shown in the following: (1) The backscatter coefficient of SAR image was influenced by stand structure, phenological change and polarization way. Especially, the correlation was as high as 0.82 between the backscatter coefficient of VV polarization and vegetation fractional coverage. (2) The inversed tree height by differential interferometry with DEM and differential interferometry with polarization were three quarters and a half of the measured tree height. The reason of underestimation by these two methods was the uncertain interferometric phase of canopy surface and land surface during inversion. (3) After the calibration by the compensation model with sinc function, the average difference was 0.56 m between the calibrated tree height and the measured tree height, and relative error was 17.17%, so the inversed results were closer to the measured tree height. Therefore, the research results of this article can provide scientific basis and technical guidance for the protection, survey statistics, and ecological renovation of the tamarix chinensis forest in protection zone. [ABSTRACT FROM AUTHOR]

The phenomenon of the subsidence of the Earth is called the downward movement of the Earth's surface, which is not easily identifiable and quantifiable due to its slow evolution. The most appropriate method is to use radar data and differential interferometry techniques. Since physical compression and pressure drop are the main causes of subsidence in the places where water is extracted, D-InSAR's integrated zoning and its simultaneous investigation with subsurface factors reflect the anomalous deformation pattern created by subsurface factors. It shows well and demonstrates that the use of D-InSAR has been helpful in subsidence navigation. For this purpose, D InSAR radar data interferometry method was applied to ENVISAT and PALSAR radar satellite data from ASAR and ALOS sensors in 2006-2008, respectively. Ambient drought moderated the effect of phase change caused by atmospheric compounds, and the lack of challenge vegetation minimized the coherence of the phase radar images and made it possible to measure changes in the C and L band radar data annually. The maximum displacement was estimated by the C band of 0.14 m and by the L band of 0.12 m. Finally, we modeled the statistical results of the groundwater of the region and the amount of subsidence estimated by the mentioned radar images by linear regression method. There is groundwater, in relation to ASAR image per meter change in groundwater level we will have 0.025 meter change in ground level elevation and PALSAR (HH) image per meter change in groundwater level, 0.021 meter change in height we will have.

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas.

Ground-Based Differential Interferometric Radars (GB-DInRads) have been widely applied to measure deformations. In this paper, the main types of GB-DInRads are summarized, including ground-based real aperture radar and Ground-Based Synthetic Aperture Radar (GB-SAR). The working principles and important parameters of some representative systems are introduced. Then, taking the GB-SAR as an example, the current key processing techniques are introduced, which mainly include differential interferometry, permanent scatterer selection, and atmospheric phase compensation. Lastly, three examples are presented to show the applications of GB-DInRad in deformation measurements. A Multiple-Input Multiple-Output (MIMO) radar was utilized to monitor an open-pit mine for about 11 days, and two regions with obvious deformation were found. In addition, a linear-scanning GB-SAR was utilized to monitor a mountain slope after severe landslide. The analysis result proved that rainfall could accelerate deformation. The feasibility of vibration measurement with the MIMO radar was also discussed in this paper.

Land subsidence analysis using satellite imagery is a consequential subject. Earth scientists have begun utilizing satellite imagery as an alternative to in-situ measurements and conceptual models. Synthetic aperture radar (SAR) images, moreover, utilize the reformer approach more than traditional satellite imagery with the use of high-resolution radar images. As a natural hazard, land subsidence is mostly attributed to excessive groundwater extraction, which is also the main reason for choosing the Konya Plain in Turkey as the study area for the present work. Since the Konya region is an agricultural and industrial land, groundwater extraction has been a challenging circumstance for the last few years. Change in groundwater level is also correlated with land subsidence rates through hydrogeological conceptualization. In this study, SAR images of the Sentinel 1 satellite are utilized for land subsidence rate calculation with the European Space Agency's SNAP software. Differential SAR interferometry (DInSAR) technique was used, which makes possible to detect deformation on the ground surface of the same portion of the Earth's surface using SAR images. The different acquisitions with DInSAR method allow to create differential interferograms that provide information ground motion with accuracy in cm. Three periods were utilized as 2016–2017, 2017–2018 and 2018–2019 the mean land subsidence rates were calculated for each period as 2.2, 1.4 and 1.7 cm/year, respectively. In the sum of the 3-year period, the maximum subsidence value went up to 16 cm. [ABSTRACT FROM AUTHOR]

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The use of an inventory map of past landslide events in the derivation of susceptibility models is considered common practice. However, evidence of landslide activity may be lost due to various degrees of modification by subsequent landslides, erosional processes, vegetation growth and anthropic influences. The timely detection of active landslides can form an effective supplement to landslide records for improving the accuracy of landslide susceptibility maps. In this paper, we present a landslide susceptibility assessment carried out in a southwestern region of Cyprus using a synergy of differential interferometry and evidential statistics. A measurement of the vertical and horizontal displacements for the period 2016–2018 was done using the Small BAseline Subset multi-pass Differential Interferometric Synthetic Aperture Radar technique. Based on the results, a total of 8859 raster cells/pixels were classified as active landslides. The weight-of-evidence technique was applied to determine the weights of seven geomorphological and hydrological factors to landslide occurrence and compile a susceptibility model. The success and prediction rates of the derived model were calculated as 79.6% and 78.9%, respectively. The validation of the results against the existing landslide inventory indicates an 84% agreement with respect to the moderate and high landslide susceptibility zones. The proposed methodology can complement existing conventional landslide inventories as a means of providing updated landslide activity at frequent intervals and can provide valuable information regarding the distribution of landslides to support a detailed landslide assessment. [ABSTRACT FROM AUTHOR]

Surface subsidence is a dominant component of the displacement vector triggered by underground mining. Over the last few decades, Differential Interferometry Synthetic Aperture Radar (DInSAR) has been used to efficiently monitor this phenomenon with great spatial and temporal coverage. More advanced multi-temporal DInSAR (MTInSAR) algorithms have been proposed to overcome some of the limitations of conventional DInSAR. However, advanced MTInSAR approaches are also not perfect in terms of measuring mining subsidence (e.g., temporal decorrelation, ambiguity, nonlinearity). For this reason, we propose a fusion of the Persistent Scatterer Interferometry (PSInSAR) and DInSAR results. By combining these complementary techniques, the atmospheric errors in PSInSAR data are reduced and larger deformation rates could have been detected more accurately (thanks to DInSAR) than by an approach solely based on PS-InSAR. This allows to measure areas with fast-moving subsidence (1 m/year) due to ongoing underground coal exploitation. Data from ascending and descending orbits of Sentinel-1A\B were used to obtain the vertical deformation component. The resulting integrated vertical deformation map was compared with the results from levelling benchmarks. The Root Mean Square Error (RMSE) calculated based on this comparison was 22 mm. Moreover, the maximal vertical cumulative subsidence detected in the study area was 1.05 m/year. [ABSTRACT FROM AUTHOR]

On November 12, 2017, a strong Mw7.3 earthquake occurred in the Sarpol-e Zahab region on the Iranian-Iraqi border, killing more than 500 people. Earthquakes cause serious damage, but do not cause rupture of seismogenic faults on the earth's surface. In order to study the focal mechanism of earthquakes and the crustal deformation caused by earthquakes, this paper uses ALOS-2 and Sentinel-1 satellite data, obtains the seismic co-seismic deformation field by over-differential interferometry, and then uses the reso- lution constrained quadtree sampling method to observe it. The data are sampled for down sampling. On this basis, a two-step inversion algorithm is used to subdivide the fault plane, and the precise geometric pa- rameters and the optimal fault slip distribution are obtained by inversion. The inversion results show that the seismogenic fault is a fault with thrust and dextral strike slip, and the fault strike is determined 347° . The inclination angle is 15°. The distribution of seismic slip is mainly distributed in16 - 19 km. The depth range and the maximum tilting slip are 4.5 m. Based on the inversion of the fault model, the 3D seismic de- formation field is obtained. The results of the 3D seismic deformation field show that the deformation caused by the earthquake is mainly in the vertical direction, and the vertical uplift deformation reaches 78.8 cm. The inversion results of the focal mechanism and the three-dimensional deformation field results are in good agreement with the seismological research results. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar interferometry (GB-InSAR) enables the continuous monitoring of areal deformation and can thus provide near-real-time control of the overall deformation state of dam surfaces. In the continuous small-scale deformation monitoring of a reservoir dam structure by GB-InSAR, the ground-based synthetic aperture radar (GB-SAR) image acquisition may be interrupted by multiple interfering factors, such as severe changes in the meteorological conditions of the monitoring area and radar equipment failures. As a result, the observed phases before and after the interruption cannot be directly connected, and the original spatiotemporal datum for the deformation measurement is lost, making the follow-up monitoring results unreliable. In this study, a multi-threshold strategy was first adopted to select coherent point targets (CPTs) by using successive GB-SAR image sequences. Then, we developed differential GB-InSAR with image subsets based on the CPTs to solve the dam surface deformation before and after aberrant interruptions. Finally, a deformation monitoring experiment was performed on an actual large reservoir dam. The effectiveness and accuracy of the abovementioned method were verified by comparing the results with measurements by a reversed pendulum monitoring system. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters’ variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system. [ABSTRACT FROM AUTHOR]

Phase analysis based on high-quality pixel (HQP) is crucial to ensure the measurement accuracy of ground-based SAR (GB-SAR). The amplitude dispersion (ADI) criterion has been widely applied to identify pixels with high amplitude stability, i.e., permanent scatterers (PSs), which typically are point-wise scatterers such as stones or man-made structures. However, the PS number in natural scenes is few and limits the GB-SAR applications. This paper proposes an improved method to take HQP selection applied for natural scenes in GB-SAR interferometry. In order to increase the spatial density of HQP for phase measurement, three types of HQPs including PS, quasi-permanent scatter (QPS), and distributed scatter (DS), are selected with different criteria. The ADI method is firstly utilized to take PS selection. To select those pixels with high phase stability but moderate amplitude stability, the temporal phase coherence (TPC) is defined. Those pixels with moderate ADI values and high TPC are selected as QPSs. Then the feasibility of the DS technique is explored. To validate the feasibility of the proposed method, 2370 GB-SAR images of a natural slope are processed. Experimental results prove that the HQP number could be significantly increased while slightly sacrificing phase quality.

The paper presents the validation results of the InSAR method for determining the forest canopy height, based on TanDEM-X and ALOS PALSAR data. The research conducted on the territory of the Baikal-Kudara forest area of the Republic of Buryatia (52°10'N, 106°48'E). Forest vegetation is represented mainly by conifers – pine, and spruce, with a small admixture of deciduous trees – aspen, birch, etc. The forest vegetation height was determined by subtracting the digital elevation model (DEM) of the digital terrain model (DTM). DEM is built according to the L-band (wavelength of 23.5 cm) ALOS PALSAR satellite with horizontal co-polarization mode. In the investigation it was assumed that a radar signal of ALOS PALSAR passes all forest thickness and reflected from the underlying surface, made it possible to recover terrain under forest canopy. DTM has been built using the TanDEM-X data (wavelength 3 cm). In this case, it was assumed that the radar echoes scattered from a some virtual phase centers of scattering surface, which characterizes the upper limit of the continuous forest canopy. To check the accuracy of satellite definitions of forest height in study area were made high-precision geodetic measurement of trees heights using electronic total station and the coordinates of geographic control points using differential GPS receivers. The discrepancy between the satellite and ground-based measurements at 11 test sites did not exceed 2 m, which is mainly due to the difference in measurement techniques: height of individual trees by ground methods and continuous forest canopy height using radar interferometry.

A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.

The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR) Interferometry (InSAR) has been a widely used geodetic technique for observing the Earth's surface, especially for mapping the Earth's topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth's atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere) are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR). To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorología (AEMET) and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC) images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate height and deformation measurements. [ABSTRACT FROM AUTHOR]

Arctic landscapes undergo seasonal and long-term changes as the active layer thaws and freezes, which can result in localized or irregular subsidence leading to the formation of thermokarst terrain. Differential Interferometric Synthetic Aperture Radar (DInSAR) is a technique capable of measuring ground surface displacements resulting from thawing permafrost at centimetre precision and is quickly gaining acceptance as a means of measuring ground displacement in permafrost regions. Using RADARSAT-2 stacked DInSAR data from 2013 and 2015 we determined the magnitude and patterns of land surface change in a continuous permafrost environment. At our study site situated in the Canadian High Arctic, DInSAR seasonal ground displacement patterns were consistent with field observations of permafrost degradation. As expected, many DInSAR values are close to the detection threshold (i.e., 1 cm) and therefore do not indicate significant change; however, DInSAR seasonal ground displacement patterns aligned well with climatological and soil conditions and offer geomorphological insight into subsurface processes in permafrost environments. While our dataset is limited to two years of data representing a three-year time period, the displacements derived from DInSAR provide insight into permafrost change in a High Arctic environment and demonstrate that DInSAR is an applicable tool for understanding environmental change in remote permafrost regions. [ABSTRACT FROM AUTHOR]

This Special Issue hosts papers related to deformation monitoring in urban areas based on two main techniques: Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions highlight the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. In this Special Issue, a wide range of InSAR and PSI applications are addressed. Some contributions show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This issue includes a contribution that compares PSI and TomoSAR and another one that uses polarimetric data for TomoSAR.

Airborne differential synthetic aperture radar interferometry (D-InSAR) is a kind of potential technology to survey the surface deformation. In this paper, the errors of two usually used modes of this technology, namely, two-pass and three-pass approaches, are compared and analyzed. In the analysis, all the errors are decomposed and sorted according to the principle of independence, and the coupling between motion error and other errors is considered. Based on the analysis, analytical expressions are derived for the deformation measurement errors in the two modes. The results demonstrate that when the amplitude of the motion error is small, the three-pass approach can reduce the requirement on the precision of the external topography data, so it distinctly outperforms the two-pass approach; when the amplitude of the motion error is large, high-precision topography data are needed in the three-pass approach, and as a result, its advantage over the two-pass approach is not distinct any more.

In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements.

The estimation of deformation parameters using persistent scatterer interferometry (PSI) is limited to single dominant coherent scatterers. As such, it rejects layovers wherein multiple scatterers are interfering in the same range-azimuth resolution cell. Differential synthetic aperture radar (SAR) tomography can improve deformation sampling as it has the ability to resolve layovers by separating the interfering scatterers. In this way, both PSI and tomography inevitably require a means to detect coherent scatterers, i.e., to perform hypothesis testing to decide whether a given candidate scatterer is coherent. This paper reports the application of a detection strategy in the context of “tomography as an add-on to PSI”. As the performance of a detector is typically linked to the statistical description of the underlying mathematical model, we investigate how the statistics of the phase instabilities in the PSI analysis are carried forward to the subsequent tomographic analysis. While phase instabilities in PSI are generally modeled as an additive noise term in the interferometric phase model, their impact in SAR tomography manifests as a multiplicative disturbance. The detection strategy proposed in this paper allows extending the same quality considerations as used in the prior PSI processing (in terms of the dispersion of the residual phase) to the subsequent tomographic analysis. In particular, the hypothesis testing for the detection of coherent scatterers is implemented such that the expected probability of false alarm is consistent between PSI and tomography. The investigation is supported with empirical analyses on an interferometric data stack comprising 50 TerraSAR-X acquisitions in stripmap mode, over the city of Barcelona, Spain, from 2007–2012.

Monitoring of active faults in urban areas is of great importance, providing useful information to assess seismic hazards and risks. The present study concerns the monitoring of the potential ground deformation caused by the active tectonism in the cities of Patras and Pyrgos in Western Greece. A PS interferometric analysis technique was applied using a rich data–set of ERS–1 & 2 SLC images. The results of the interferometric analysis were compared with the tectonic maps of the two cities. Patras show clearer uplift–subsidence results due to the more distinct fault pattern and intense deformation compared to the Pyrgos area, where more diffused deformation is observed, with no significant displacements on the surface.

This paper presents a comprehensive overview of the surface deformation retrieval capability of the Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm, referred to as Small BAseline Subset (SBAS) technique, in the context of active volcanic areas. In particular, after a brief description of the algorithm some experiments relevant to three selected case-study areas are presented. First, we concentrate on the application of the SBAS algorithm to a single-orbit scenario, thus considering a set of SAR data composed by images acquired on descending orbits by the European Remote Sensing (ERS) radar sensors and relevant to the Long Valley caldera (eastern California) area. Subsequently, we address the capability of the SBAS technique in a multipleorbit context by referring to Mt. Etna volcano (southern Italy) test site, with respect to which two different ERS data set, composed by images acquired both on ascending and descending orbits, are available. Finally, we take advantage of the capability of the algorithm to work in a multi-platform scenario by jointly exploiting two different sets of SAR images collected by the ERS and the Environment Satellite (ENVISAT) radar sensors in the Campi Flegrei caldera (southern Italy) area. The presented results demonstrate the effectiveness of the algorithm to investigate the deformation field in active volcanic areas and the potential of the DInSAR methodologies within routine surveillance scenario.

In the recent years radar interferometry (InSAR) has become an important tool in various studies. It can be used to produce accurate digital elevation models and observe small surface displacements. Differential interferometry (DInSAR) can detect movements in the radar look direction that are in the order of wavelength used, i.e. less than one centimetre with ERS data. In the presented study DInSAR has been used to observe surface movements in western Slovenia. Three ERS radar images have been supplemented with an external digital elevation model to produce three differential interferograms that temporally covered the Posočje earthquake, which happened on April 12, 1998. For the area around Bovec a land subsidence of approximately 0.5 cm has been observed; the largest movements detected exceeded 2 cm. DInSAR has been compared to the permanent scatterers interferometry (PSInSAR). Both methods are complementary and both have individual advantages and disadvantages.

Differential SAR interferometry, a popular technique for measuring displacements of the Earth's surface, is potentially influenced by changes in soil moisture. Different mechanisms for this impact have been proposed, but its magnitude, sign and even presence remain poorly understood. In this study the dependence of the phase, the coherence magnitude as well as the phase triplets on soil moisture was inferred empirically with regression techniques: this was done for two airborne data sets at L-band. The phase dependence was significant (at a significance level of 0.05) for more than 70% of the fields at HH polarization, its sign corresponding to an increase in optical path upon wetting, and the magnitude of the associated deformation commonly exceeding 2 cm for a change in soil moisture of 20%. This trend was similar in both campaigns, whereas the prevalence of soil moisture-related decorrelation differs. These results are only consistent with a dielectric origin of the soil moisture effects, and not with soil swelling or the penetration depth hypothesis. Changes in vegetation impact the phase depending on the crop and polarization, with the vegetation influence at VV being more pronounced for the agricultural crops present in the study area. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Differential Interferometry (DInSAR) technique is an effective tool with large coverage and high spatial accuracy for subsidence monitoring. Nevertheless, the temporal resolution is usually poor so that rapid deformation cannot be measured due to the long revisit time of radar satellites. Bistatic SAR Differential Interferometry technique using Global Navigation Satellite System (GNSS) as illuminator has a shorter revisit time, whereas the measurement accuracy is constrained by low signal power, narrow bandwidth and atmospheric delay error. To cope with these problems, in this paper, we propose a novel subsidence monitoring technique based on Space-Surface bistatic Differential Interferometry (SS-DI) with GNSS transmitters, where two stations consist of a reference one and a measurement one that are deployed on the ground. First, we applied a space differential processing between two stations to cancel identical errors such as tropospheric and ionospheric errors etc. Then we used a long time coherent integration to improve the signal noise ratio. Subsequently, we also utilized a time differential processing to construct double differential equations with respect to unknown deformation variables. Finally, we solved the equations to obtain a highly accurate estimation of three dimension deformation. Furthermore, an SS-DI experiment using Beidou-2 as transmitters was carried out to validate the proposed method, where a high accuracy (0.01 mm) device was utilized to simulate subsidence deformation. The experimental results reveal that the proposed method has better performance of spatial measurement accuracy of 0.53 mm compared with differential GPS method. [ABSTRACT FROM AUTHOR]