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Iran is located in a semi-arid to arid environment, and in many parts of it, groundwater is the primary or sole resource of water for domestic, industrial, and agricultural purposes. Over-exploitation of groundwater in the past few decades has caused widespread land subsidence and increased sinkhole occurrence in the country. Ground-water related subsidence on the one hand poses a significant hazard to infrastructure and urban areas and on the other hand reduces the storage capability of aquifers and threatens long-term developments of the country. In a few studies, researches have investigated groundwater depletion and its associated land subsidence for some of the important aquifers across the country. However, in many areas, information on land subsidence is still missing. In the past two decades, the abundance of SAR data provided by missions like Envisat and ALOS helped boost development of InSAR methods and their applications for land subsidence studies. Although regular data acquisition from those satellites was rare, their archive still helps to study the long term behavior of land subsidence in many areas where SAR data is available. The launch of the Sentinel-1 mission in 2014 revolutionized the availability of SAR data, providing a unique opportunity for near-real time data mining in order to extract homogeneous information over large areas up to country-scale. In this study, we present the results of our InSAR survey to study land subsidence in Iran. All Sentine-1 SAR images acquired in descending frames between 2014 and 2017 (3500 images) are analyzed across the country to determine for the first time the exact extent and number of areas affected by land subsidence. Existing information of groundwater levels as well as results from previous surveys using older SAR sensors are then used to examine both inelastic and elastic behavior of the aquifers. Our nation-wide subsidence map that we present here provides the first complete picture of the extent of water problem in Iran that we can achieve thanks to recent advancement in satellite remote sensing . We detect approximately 90 subsidence areas, some of them show significant inelastic deformation with the ratio between elastic and inelastic deformation < 0.4. Our analysis shows that land subsidence mostly happens in agricultural areas where groundwater extraction in more severe. It appears to be in qualitative agreement with previous estimations that agricultural section consumes 90% of the existing water resources in the country. Such information can help decision makers, local authorities, and communities to increase awareness of the problem and prepare measures to mitigate the effects of land subsidence.

In the semi-arid climate of Iran groundwater has played an important role as a major water resource for agricultural, industrial, and municipality purposes. As a result of withdrawal of this vital resource over the last 3 decades, many aquifers across the country have been depleted, causing land subsidence in many areas of Iran. The subsidence has damaged buildings and infrastructures and caused km-long earth fissures and sinkhole collapse inside the plain aquifers. In this presentation we use data from radar remote sensing, gravity and field surveys to provide a quantitative mapping of groundwater depletion at the national scale. The general pattern of gravity variations from GRACE mission indicates the long-term depletion of groundwater budget across the country from 2002 until 2016. However, the coarse resolution of GRACE (approximately 500 km) limits its ability in identifying the exact extent of depleted aquifers. To better address this we use SAR data from Sentinel-1 mission and process them using advanced InSAR time-series analysis to identify changes in temporal and spatial pattern of subsidence anomalies linked to groundwater extraction. Our results suggest that aquifer depletion is a widespread problem across the country, resulting in land subsidence from a few cm/yr to up to 40 cm/yr in approximately 100 basins. However, the rate of displacement is highly variable in different areas. Furthermore, the magnitude of seasonal variations, which indicates the response of the aquifer to discharge/recharge of aquifers, is variable in different aquifers. In several basins, the small magnitude of seasonal variations combined with a high rate of long-term trend subsidence, suggest a significant threat to the health of aquifers. We evaluate the obtained results by extensive field surveys in selected basins and discuss how our findings can be used for implementing proper groundwater measures in areas vulnerable to high levels of groundwater stress.

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique frequently used formeasuring Earth’s surface deformation. InSAR time series approaches provide detailed informationabout the evolution of displacement in time and also overcome the limitations of conventional InSAR.Both InSAR and InSAR time series methods have been used for measuring tectonic and non-tectonicsurface deformations caused by natural and anthropogenic processes in local to regional scales. Inthe past, the availability of SAR data suitable for InSAR purpose was a major constraint for InSARapplications in many areas of the world. The launch of the Sentinel-1 mission in 2014 guaranteed aregular long-term acquisition plan of SAR data available all over the globe that enables us to increasethe scale of deformation mapping from regional to national. However, there are challenges in large-scale InSAR processing that needs to be addressed. In this thesis, InSAR time series approaches areused to obtain detailed maps of the time-varying surface displacement fields caused by non-tectonicprocesses at a local to regional scales and find the link between surface displacement and its drivers.Then, a workflow for large-scale InSAR time series analysis is suggested that benefits from extensivecollections of data provided by Sentinel-1 to identify and monitor displacement fields over extensiveareas.In the first part of this thesis, InSAR time series approaches are used at local to regional scales inthree independent case studies. In the first case study, a localized displacement of as much as 1 cm/yrattributed to slope movement of a landslide in Taihape, New Zealand is observed. Along with the long-term trend of displacement caused by slope movement, the analysis of time series reveals an accelerationin surface displacement due to a rainstorm as well as the seasonal variations of displacement linkedto groundwater variations. In the second study area, land subsidence in Tehran, Iran is analyzedat a regional scale. Using an extensive collection of SAR data, land subsidence areas with a peakexceeding 25 cm/yr are detected. After combining time series from different datasets, both long-termsubsidence trend due to the constant decline of groundwater level, and seasonal variations of surfacedisplacement linked to the discharge/recharge cycle of groundwater are investigated. In the third casestudy, localized displacement of a natural gas reservoir in Berlin is analyzed and a long-term upliftof approximately 0.2 cm/yr and 2 cm of seasonal variations linked to the injection of gas in summerand extraction in winter are observed. Furthermore, mining-induced displacements in an area nearLeipzig are measured and approximately 4 cm/yr of subsidence coupled with 5 cm/yr of east-westdisplacement are observed.To extend the InSAR measurement of non-tectonic displacement fields from local and regional tonational scale, a workflow is developed in this thesis that benefits from the vast amount of data col- 4lected by Sentinel-1 mission. The proposed workflow notably reduces the broad-scale and topography-dependent components of tropospheric errors in an adaptive approach and provides a quick, but robustdisplacement field map from large stacks of Sentinel-1 data. The proposed method is applied on twotracks of Sentinel-1 data across Iran and Germany and successfully obtained the displacement fieldsin large scales. In the first case study, more than 20 displacement areas mostly attributed to landsubsidence induced by over-extraction of groundwater are identified. In the second case study, approx-imately 10 displacement areas mainly related to anthropogenic activities including mining and gas/oilinjection/extraction are detected.

The combined effects of climate change and human land use activities are increasing the occurrence of extreme events, such as floods and landslides worldwide. Between mid-March and the beginning of April 2019, almost the whole Iran was affected by intense record rainfall leading to widespread flash flooding. Starting in the northern province of Golestan and moving to the South and the West of the country, heavy rains have poured on 28 out of the 31 provinces. Approximately, 1900 cities and villages across the country were damaged with half a million people displaced at the peak of the disaster. The intense rainfall not only caused more than 140 rivers to burst their banks, but also triggered thousands of landslides. The presented work makes use of high resolution - spatial and temporal - optical and radar satellite remote sensing with the goal of performing large-area and at the same time detailed spatiotemporal analysis of landslide occurrence related to the main hydro-meteorological triggering events. Besides detection of rainfall and flood triggered landslides, pre- and post-failure landslide evolution has been analyzed including the first observed onset of landslide activation in February 2019 resulting from already increased precipitation in late fall and winter of 2018/19. Large-area landslide detection has been performed in a semi-automated way using time series of optical Planet Scope and Sentinel-2A/B data. For assessing the kinematics of pre- and post-failure slope motions, radar remote sensing based image correlation and differential interferometric synthetic aperture radar (DInSAR) methods have been applied using Sentinel-1A/B data. The obtained satellite remote sensing based results have been evaluated by field surveys conducted in cooperation between the GFZ Potsdam and the Forest, Range and Watershed Management Organization of Iran (FRWM) being responsible for landslide hazard and risk assessment as well as the design and implementation of mitigation measures. In this context, emphasis has been put on the potential of multi-sensor satellite remote sensing for improving large-area process monitoring and understanding, especially in relation to extreme triggering events of so far unprecedented magnitude which are likely to occur more frequently in the future because of ongoing climate change.

The C-band synthetic aperture radar (SAR) satellites of Sentinel-1 with capability to obtain acquisition in Terrain Observation with Progressive Scan (TOPS) mode have brought new opportunities for large-scale monitoring of the ground surface deformation using interferometric SAR (InSAR) technique. However, despite the potential to generate large-scale interferograms, the highly spatiotemporal variability in troposphere, limits InSAR measurements accuracy. In addition, the measurement accuracy might be degraded by the signals due to the ionosphere, which is not negligible even at C-band data. One way for the atmospheric delay correction in interferograms is using external sources such as the global navigation satellite system (GNSS). The total electron content (TEC) and the zenith total delay (ZTD) values measured from a dense network of the GNSS receivers can be used to retrieve the ionospheric and tropospheric contributions to the interferometric phase, respectively. However, interpolation of the data is a big challenge, as we need to find a suitable function to predict the delay for the whole interferogram, which is challenging for large-scale Sentine-1 interferograms. In this study, we propose a new technique based on machine learning (ML) regression approach using the combination of small-baseline interferograms and the GNSS derived TEC and ZTD values to mitigate the atmospheric contributions. The technique produces the differential atmospheric (using the TEC and ZTD values) map for short-interval intergerograms based on the phase-atmosphere relation by this assumption that the deformation contribution to the interferometric phase is negligible in the short intervals. It then estimates the differential atmospheric maps for the longer-interval interferograms using the atmospheric maps with short intervals. The technique facilitates the corrections, as we do not need to deal with finding a suitable function for interpolation of distributed external observations. We implement our method on 12 concatenated frames of Sentinel-1 images acquired between May-October 2016 along a track over Norway to correct the interferograms from the atmospheric effects. Then, we apply the small baseline subset (SBAS) approach on the atmospherically corrected interferograms. The results on the stack of large-scale Sentinel-1 interferograms show that the ML-based method largely removes the ionospheric and tropospheric effects and thus improves the InSAR time series analysis results. To validate the results we compare the displacement time-series derived by small-baseline interferograms corrected by our method with the displacement time-series observed by GNSS receivers.

The availability of regular SAR data acquired by the Sentinel-1 mission provides enormous opportunities for deformation mapping over extensive areas. However, it brings significant technical challenges that need to be properly addressed to extract high-quality deformation products. One of the major challenges in this regard is the tropospheric contribution in the interferometric phase. In this study, we investigate three different approaches for reducing the tropospheric effect from large stacks of Sentinel-1 interferograms and apply them for large-scale displacement mapping in three study areas in Iran, Germany, and Chile. In the first approach, we use the tropospheric phase delay estimated from a network of GNSS data to correct the interferograms. This approach is evaluated for Germany, where a dense network of GNSS is available. With the current distribution of GNSS stations, we can reduce the tropospheric phase from the interferograms by approximately 60%. For the other two study areas in Iran and Chile, such dense GNSS as in Germany is not available. Therefore, we use the ECMWF weather model data provided by Generic Atmospheric Correction Online Service for InSAR (GACOS) to correct the individual interferograms for the tropospheric phase. Although this method appears to be successful in many cases, it has difficulties in correcting interferograms with data acquisition times a few hours separated from the temporal intervals of ECMWF data. Therefore, we develop an adaptive method that uses the interferometric phase itself to filter out the topography-correlated phase and broad-scale phase changes as unwanted phases from the interferograms. Because in large-scale interferograms, the spatial variations of tropospheric delay are significant, we split the interferograms into smaller patches to adaptively estimate and remove the unwanted phases. Then, the remaining tropospheric contribution, mainly from the turbulent atmosphere, are reduced through time series analysis. In each study area, three years of Sentinel-1 data are analyzed using Small Baseline Subset (SBAS) approach. We compare the effectiveness of the three methods in reducing the tropospheric contribution and discuss the potentials and limitations of them in retrieving anthropogenic and tectonic deformation in different study areas.

On November 12, 2017, a large earthquake (Mw = 7.3) hit Kermanshah province of Iran near the border of Iran and Iraq. At least 600 people were killed, 9000 injured and thousands left homeless. In this study we integrated observations from radar and optical remote sensing, seismology and field mapping to investigate source parameters, coseismically triggered slope failures and secondary faulting related to the Mw 7.3, Sarpol-e Zahab earthquake in Iran. Coseismic surface deformation was constrained by interferometric synthetic aperture radar (InSAR) analysis from SAR images obtained by L-band ALOS-2 and C-band Sentinel-1 satellites. ALOS-2 wide-swath (WS) interferograms were derived from ascending and descending orbits covering 09.08.2016-14.11.2017 and 04.10.2017-15.11.2017 time periods, respectively. Sentinel-1 TOPS coseismic interferograms were derived from ascending and descending orbits covering 11.11.2017-17.11.2017 and 12.11.2017-18.11.2017, respectively. Source parameters and slip models of the earthquake were then obtained by the Bayesian inversion of interferometric results using elastic dislocation modeling, considering the seismic parameters as a priori information. Preliminary results of geodetic source modeling suggests that the 12 November 2017 Sarpol-e Zahab earthquake was generated by blind ENE oblique thrust faulting with an average slip of approximately 4 m at a depth between 17 and 22 km. The 2017 Sarpol-e Zahab event also triggered a lot of coseismic slope failures including landslides and rock falls, and secondary faulting. Their potential locations were initially assessed by identifying local phase changes and areas affected by loss of coherence in the interferograms. Moreover, large-scale analysis of horizontal surface displacements was performed by pixel-offset tracking using Sentinel-1 SAR images. The results were evaluated by expert interpretation based on optical Sentinel-2 data in combination with geological information and field investigations. Largest horizontal deformation was observed for a co-seismically triggered landslide which was further analyzed using optical PlanetScope data acquired one day apart right before and after the earthquake (12th and 13th of November). Pixel-based change detection allowed exact spatial delineation of the displaced block covering an area of about 4 km2. Cross-correlation analysis resulted in a spatially detailed derivation of horizontal movement vectors indicating displacements in the range from about 5 to 30 m. The results of this study have shown that a combination of various remote sensing methods with modelling and field investigations is required for gaining an improved understanding of surface processes caused by the Mw 7.3, Sarpol-e Zahab earthquake. Complementary analysis of radar and optical remote sensing has led to a more comprehensive assessment of various types of co-seismically induced surface changes occurring at different spatial scales. Such information can contribute to an improved spatial characterization of co-seismically activated geological structures. This is especially important in the context of seismic hazard assessment for scarcely investigated areas like the one affected by the Sarpol-e Zahab earthquake in the border region between Iran and Iraq.

The detection and monitoring of mass movement of susceptible slopes plays a key role in mitigating hazards and potential damage associated with creeping slopes and landslides. In this paper, we use observations from both Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) to assess the slope stability of the Sarcheshmeh ancient landslide in the North Khorasan province of northeast Iran. InSAR observations were obtained by the time-series analysis of Envisat SAR images covering 2004–2006, whereas repeated GPS observations were conducted by campaign measurements during 2010–2012. Surface displacement maps of the Sarcheshmeh landslide obtained from InSAR and GPS are both indicative of slope stability. Hydrogeological analysis suggests that the multi-year drought and lower than average precipitation levels over the last decade might have contributed to the current dormancy of the Sarcheshmeh landslide.

In this study, gradual elevation change due to summer thawing of active layer in tundra permafrost landscape of Barrow, Alaska is investigated using SAR interferometry (InSAR) technique. We used a variety of SAR sensors including TerraSAR-X, ALOS, and Sentinel-1 images to assess elevation changes in summer season. Preliminary result, obtained by TerraSAR-X InSAR analysis, clearly delineates subsidence during the summer by identifying thousands of coherent pixels on the ground. InSAR time-series analysis from June to September 2013 show the progressive thaw-season subsidence on the Barrow coastal plain with maximum magnitude of 4 cm in satellite line of sight. The spatial pattern of InSAR elevation change reflects different thaw-related landscape features of permafrost. As a general pattern, detected elevation change is higher in wet thermokarst basins than tundra uplands. Barrow is one of the Global Terrestrial Network for Permafrost (GTN-P) sites. A lot of ancillary data that have been acquired in the last decades will help us in interpretation of InSAR results. We also use airborne hyperspectral data to derive characteristics like wetness and vegetation cover of the landscape to better understand the process and the link between estimated subsidence and landscape features.