Data from the Sentinel-1 satellite have already proven useful for investigating seismic and volcanic events since its launch in April 2014. The requirement of ultrahigh coregistration accuracy and the current relatively short time of Sentinel-1 acquisitions make its application challenging for studying slow deformation processes, such as fault creep and land subsidence. Here we analyze a set of 14 SAR images over the San Francisco Bay Area spanning 1 year from early 2015 to 2016. We show that implementing an existing Enhanced Spectral Diversity algorithm or using precise orbits together with a reference digital elevation model both yield the required coregistration accuracy for making use of the phase measurements in time series analysis of ground deformation. Following a thorough validation test, we update our estimates of Hayward Fault creep rate and confirm uplift due to recharge of the Santa Clara Valley aquifer system during the final summer of 4 year drought.

Operating at L-band (24 cm wavelength) in wide-swath modes is one of the characteristics of the new and next generation satellite synthetic aperture radar (SAR) missions. After 3 years of operation, the Japan Aerospace Exploration Agency Advanced Land Observing Satellite-2 (ALOS-2) satellite has acquired a wealth of L-band wide-swath SAR data over many areas using its ScanSAR mode. We present interferometry SAR (InSAR) time series analysis results from ALOS-2 ScanSAR data. We analyze the possible error sources in the InSAR and correct them if possible. We present different time series analysis results including azimuth frequency modulation rate error, line of sight (LOS) ionospheric phase, azimuth shift caused by the ionosphere, and LOS displacement processed using both full-aperture and burst-by -burst workflows. The final InSAR LOS displacement time series result reveals both large-scale tectonic and small-scale anthropogenic deformation components. The results demonstrate the potential for measuring continental or even global-scale tectonic deformation and illustrate the promise of upcoming L-band wide-swath SAR missions, such as the NASA-ISRO SAR mission.

In order to measure the response of the Slumgullion landslide to hydraulic forcing, we utilize the unique capabilities of the NASA/JPL's UAVSAR airborne repeat-pass SAR interferometry system to provide surface geodetic measurements with 'landslide-wide' spatial coverage. Unlike traditional space-based INSAR we are not restricted to fixed flight tracks or fixed repeat times, allowing for optimal imaging geometries and timing. We combine four look directions chosen based on the landslide geometry and invert for the full 3-D landslide-wide surface deformation. These observations complement ongoing GPS measurements and in-situ observations of pore-pressure and atmospheric parameters acquired by the U.S. Geological Survey.

Catastrophic landslides are often preceded by slow, progressive, accelerating deformation that differs from the persistent motion of slow-moving landslides. Here, we investigate the motion of a landslide that damaged 12 homes in Rolling Hills Estates (RHE), Los Angeles, California on 8 July 2023, using satellite-based synthetic aperture radar interferometry (InSAR) and pixel tracking of satellite-based optical images. To better understand the precursory motion of the RHE landslide, we compared its behavior with local precipitation and with several slow-moving landslides nearby. Unlike the slow-moving landslides, we found that RHE was a first-time progressive failure that failed after one of the wettest years on record. We then applied a progressive failure model to interpret the failure mechanisms and further predict the failure time from the pre-failure movement of RHE. Our work highlights the importance of monitoring incipient slow motion of landslides, particularly where no discernible historical displacement has been observed.

Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ∼2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.

Slow-moving landslides are destabilized by precipitation. The continuous soil moisture monitoring aid the understanding of landslide processes. Here we study time-series soil moisture using NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) at 6-m resolution for a seasonally active grassland landslide in the northern California Coast Ranges, USA. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture. The forward model is developed for polarization HH and VV. The soil moisture retrieval using HH&VV shows unbiased RMSE (ubRMSE) of 0.058 m3/m3. From the Freeman-Durden decomposition for UAVSAR's time-series data, the surface scattering and double bounce dominate the landslide area which suggests the strong correlation with soil moisture for the data. The physical-model based algorithm can be applied to other grassland covered landslides in California to retrieve soil moisture.

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologie and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover. Copyright 1998 by the American Geophysical Union.

In order to provide surface geodetic measurements with “landslide-wide” spatial coverage, we develop and validate a method for the characterization of 3-D surface deformation using the unique capabilities of the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne repeat-pass radar interferometry system. We apply our method at the well-studied Slumgullion Landslide, which is 3.9 km long and moves persistently at rates up to ∼2 cm/day. A comparison with concurrent GPS measurements validates this method and shows that it provides reliable and accurate 3-D surface deformation measurements. The UAVSAR-derived vector velocity field measurements accurately capture the sharp boundaries defining previously identified kinematic units and geomorphic domains within the landslide. We acquired data across the landslide during spring and summer and identify that the landslide moves more slowly during summer except at its head, presumably in response to spatiotemporal variations in snowmelt infiltration. In order to constrain the mechanics controlling landslide motion from surface velocity measurements, we present an inversion framework for the extraction of slide thickness and basal geometry from dense 3-D surface velocity fields. We find that the average depth of the Slumgullion Landslide is 7.5 m, several meters less than previous depth estimates. We show that by considering a viscoplastic rheology, we can derive tighter theoretical bounds on the rheological parameter relating mean horizontal flow rate to surface velocity. Using inclinometer data for slow-moving, clay-rich landslides across the globe, we find a consistent value for the rheological parameter of 0.85 ± 0.08.

We present a detailed survey of the ongoing destabilization process of the Mosul dam. The dam is located on the Tigris river and is the biggest hydraulic structure in Iraq. From a geological point of view the dam foundation is poor due to a site geology formed by alternating strata of highly soluble materials including gypsum, anhydrite, marl and limestone. Here we present the first multi-sensor cumulative deformation map for the dam generated from space-based interferometric synthetic aperture radar measurements from the Italian constellation COSMO-SkyMed and the European sensor Sentinel-1a over the period 2014-2016 that we compare to an older dataset spanning 2004-2010 acquired with the European Envisat satellite. We found that deformation was rapid during 2004-2010, slowed in 2012-2014 and increased since August 2014 when grouting operations stopped due to the temporary capture of the dam by the self proclaimed Islamic State. We model the inferred deformation using a Markov chain Monte Carlo approach to solve for change in volume for simple tensile dislocations. Results from recent and historical geodetic datasets suggests that the volume dissolution rate remains constant when the equivalent volume of total concrete injected during re-grouting operations is included in the calculations.

The hazardous impact and erosive potential of slow-moving landslides depends on landslide properties including velocity, size, and frequency of occurrence. However, constraints on size, in particular, subsurface geometry, are lacking because these types of landslides rarely fully evacuate material to create measurable hillslope scars. Here, we use pixel offset tracking with data from the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar to measure the three-dimensional surface deformation of 134 slow-moving landslides in the northern California Coast Ranges. We apply volume conservation to infer the actively deforming thickness, volume, geometric scaling, and frictional strength of each landslide. These landslides move at average rates between ∼0.1–2 m/yr and have active areas of ∼6.10 × 103–2.35 × 106 m2, inferred mean thicknesses of ∼1.1–25 m, and volumes of ∼7.01 × 103–9.75 × 106 m3. The best fit volume-area geometric scaling exponent is γ ∼ 1.2–1.5, indicating that these landslides fall between typical soil and bedrock landslide scaling. A rollover in the scaling relationship suggests that the largest landslide complexes in our data set become large primarily by increasing in area rather than thickness. In addition, the slow-moving landslides display scale-dependent frictional strength, such that large landslide tend to be weaker than small landslides. This decrease in frictional strength with landslide size is likely because larger landslides are composed of higher proportions of weak material. Our work shows how state of the art remote sensing techniques can be used to better understand landslide processes and quantify their contribution to landscape evolution and hazards to human safety.