Satellite-derived seasonal fluctuations in surface displacement and soil moisture: Implications for landslide activity

Permafrost is widely distributed in the Tibetan Plateau. Seasonal freeze–thaw cycles of permafrost result in upward and downward surface displacement. Multitemporal interferometric synthetic aperture radar (MT-InSAR) observations provide an effective method for monitoring permafrost displacement under difficult terrain and climatic conditions. In this study, a seasonal sinusoidal model-based new small baselines subset (NSBAS) chain was adopted to obtain a deformation time series. An experimental study was carried out using 33 scenes of Sentinel-1 data (S-1) from 28 November 2017 to 29 December 2018 with frequent revisit (12 days) observations. The spatial and temporal characteristics of the surface displacements variation combined with different types of surface land cover, elevation and surface temperature factors were analyzed. The results revealed that the seasonal changes observed in the time series of ground movements, induced by freeze–thaw cycles were observed on flat surfaces of sedimentary basins and mountainous areas with gentle slopes. The estimated seasonal oscillations ranged from 2 mm to 30 mm, which were smaller in Alpine deserts than in Alpine meadows. In particular, there were significant systematic differences in seasonal surface deformation between areas near mountains and sedimentary basins. It was also found that the time series of deformation was consistent with the variation of surface temperature. Based on soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data, the deformation analysis influenced by soil moisture factors was also carried out. The comprehensive analysis of deformation results and auxiliary data (elevation, soil moisture and surface temperature et al.) provides important insights for the monitoring of the seasonal freeze-thaw cycles in the Tibetan Plateau.

Velocity distribution and movement of multiple slow-moving landslides characterized by an optimized MTInSAR workflow

Evaluating satellite-derived soil moisture data for improving the internal consistency of process-based ecohydrological modelling

Increasing near-surface temperature over the Qinghai-Tibet Plateau (QTP) has led to permafrost degradation and increasing active layer thickness (ALT). In this study, the ALT was estimated based on ground subsidence monitored by multitemporal interferometric synthetic aperture radar (MT-InSAR) and temporal-spatial multilayer soil moisture data. For the ground subsidence monitoring, a modified Stefan piecewise elevation change model based on air temperature data was integrated into a new small baseline subset (NSBAS) chain. A total of 33 scenes of Sentinel-1 data (S-1) were collected over one year to build the MT-InSAR analysis network. Moreover, both soil moisture active/passive (SMAP) L4 surface and root zone soil moisture data and ERA-Interim reanalysis data were used to build an ALT retrieval model. In particular, the global-scaled soil moisture data (SMAP and ERA-Interim) fraction was separated based on the Sentinel-1 amplitude-based land cover classification results and in situ soil moisture data. A typical ALT estimation method based on the point scale groundwater information was also performed to evaluate the performance of the proposed method. Based on the validation of the ground-based ALT observations, the proposed method outperformed the traditional point scale groundwater information-based method, with a correlation coefficient of 0.67, RMSE of 0.70 and ubRMSE of 0.51, respectively. The ERA-Interim-based estimation results were underestimated due to the overestimation of the ERA-Interim soil moisture data. Obvious differences were observed between the ALT of the alpine meadow areas and alpine desert areas. Our results demonstrate that the combination of temporal-spatial multilayer soil moisture data and the MT-InSAR method with S-1 images is a promising approach for the large-scale characterization of ALT.

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future.

Preferential flow occurs in many soils and it is recognized to influence soil moisture distribution and hydrological fluxes at different scales. Preferential flow paths are formed for example by soil fauna, by plant roots or soil erosion. Water plays an important role in mass movement processes: rainwater or snow melt infiltrates into the soil and recharges the groundwater system. The unsaturated zone controls groundwater recharge allowing for the loss of soil moisture by evaporation and attenuation of percolation towards the groundwater system. An increase in pore water pressure results in a decrease in an effective stress and internal strength of slopes. The preferential fluxes may change the spatial and temporal hydrological response of a landslide and influence intensity, duration and differentiation of mass movement. The quantification of groundwater recharge, especially by means of preferential flow, is a research challenge for an advanced understanding of hydrological systems in hillslopes and landslides. The main difficulties stem from heterogeneity of landslide lithology and spatial and temporal variations of hydraulic properties. The complexity of preferential fissure flow processes, and their high spatial and temporal variability, makes it very difficult to measure the processes in the field and to include them in hydrological modeling. This thesis focuses on preferential fissure flow, where fissure is defined as geo-mechanically induced cracks commonly present in slow-moving landslides, and their influence on landslide hydrological behaviour. Research work included both extended field measurements and hydrological modelling. All experiments described in this thesis were done at the Super-Sauze landslide: a persistently active clay shale landslide that covers 0.17 km2 of surface with the average slope of 25°. The landslide kinematics of the Super-Sauze is controlled by hydrology. The mass movement occurs as a consequence of the rise of groundwater table and hence the development of positive pore pressure. In order to monitor and quantify preferential flow processes on site two methodologies were proposed: Distributed Temperature Sensing (DTS) and combined hydrological and hydrochemical analysis of small-scale sprinkling tests. Both methodologies allowed for qualitative analysis of preferential flow patterns and showed the potential for quantification of dominant hydrological processes observed across the landslide: - qualitative analysis of measured soil temperature variation allowed observing spatial differences in soil moisture state and estimating the location of surface and subsurface water flow paths; - quantitative analysis of measured soil temperature made it possible to detect the spatial and temporal variations in apparent soil thermal conductivity and correlated them with measured soil moisture content; promising empirical relationships were obtained when accounting for local heterogeneities in soil characteristics; - analyses of small scale sprinkling experiments, combining the hydrological and hydrochemical analysis of two consecutive days of sprinkling, were able to capture the dominant hydrological process occurring in the area and show the potential for their quantification; based on the analysis of all available field data, conceptual models of the hydrological responses were proposed. The literature review and the analyses of the extensive field data sets consisting of day-to-day monitoring as well as sprinkling experiments, resulted in the formulation of a conceptual model of the hydrological influence of fissures on landslide activity. Special attention was given to spatial and temporal variation in fissures connectivity, which makes fissures act both as preferential flow paths for deep vertical infiltration and as lateral groundwater drains. These dynamics were included in a spatially distributed hydrological and slope stability model and applied to a ’simple’ landslide. The results highlight that fissure connectivity and fissure permeability play an important role in distributing water within a landslide. Making the fissures connectivity a function of soil moisture content resulted in a strong seasonality of the hydrological response on infiltrating rainwater or snowmelt: increased soil moisture content leads to more lateral water drainage through the fissures towards the lower part of the landslide, while decreased soil moisture content increases the water storage in the fissures. Furthermore, an analysis was made of all available field monitoring data of the Super-Sauze landslide. Hereafter, the distributed hydrological and slope stability model was applied to the Super-Sauze case study. The main objective was to model the influence of fissures on the hydrological behaviour of slow moving landslide and the dynamic feedbacks between fissures, hydrology and slope stability. In addition to hydrological feedback (fissure connectivity being the function of soil moisture content), the mechanical feedback was implemented as a relationship between fissure volume and level of landslide activity. Overall, from this research it can be concluded that preferential fissure flow may significantly influence the timing and duration of the periods of elevated pore pressure conditions in landslides depending on fissure network characteristics, especially fissure volume and connectivity between them. The field measurements outline the spatial heterogeneity of soil hydraulic properties and dominant hydrological processes existing in slow-moving clay shale landslides. The analyses of field data together with presented modelling results confirms the importance of distributed approaches when modelling differential hydrological response of complex heterogeneous landslides and stresses the need for including spatio-temporal changes in soil hydraulic properties of both fast (i.e. fissures) and slow (i.e. matrix) responding domain.

This paper discusses the results of a comparative analysis of a multiyear time series for average seasonal and annual surface temperatures in Siberia. To analyze seasonal temperature fluctuations, we use the data on average seasonal temperatures which excludes the influence of higher-frequency (cyclonic and daily) fluctuations. We have obtained an empirical correlation between the seasonal fluctuations and average seasonal surface temperatures for two climatic zones (Irkutsk and Tobol'sk). For both zones, the seasonal fluctuations amplitude is, with a high determination coefficient (over 0.9), in linear correlation with the average seasonal winter temperature. We point out that it is promising to model and forecast the observed climatic change on the basis of empirical regularities.

The main aim of this study is to compare the two commonly used multi-temporal interferometric synthetic aperture radar (InSAR) techniques, i.e. permanent scatterers (PS) and small baseline subset (SBAS), in monitoring shallow landslides. PS and SBAS techniques have been applied to ascending and descending Sentinel-1 SAR data to measure the rate of surface deformation and the displacement time series in the Rovegliana area (NE Italian pre-Alps) from 2014 to 2019. As expected, PS results cover only urban areas, while those obtained by SBAS cover up to the 85% of the investigated area. Velocity maps obtained by the two techniques show that some sectors of the investigated slope are affected by active shallow landslides which threaten the stability of buildings, walls and road network. The comparison between ascending and descending velocity maps along the satellite line of sight reveals the presence of a horizontal component in the east–west direction which is consistent with the landslide kinematic. The analysis of the displacement time series shows that, in the case of linear deformation trends, PS and SBAS results are similar, whereas, in the case of high oscillations and non-linear behavior, SBAS technique can provide a better estimation of the displacements. Besides, SBAS provides smoother and less noisy displacement time series. However, both the techniques showed their high capability in monitoring the evolution of the landslides, which is crucial for the implementation of effective risk prevention and mitigation strategies. To deep investigate the differences between the two techniques, other geomatic methodologies, based on global navigation satellite system and terrestrial laser scanning, should be used.

Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R2 = 0.928 and RMSE = 0.020 m3m−3). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning.

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., European Space Agency Climate Change Initiative (ESA CCI), National Aeronautics and Space Administration Soil Moisture Active Passive (NASA SMAP)); however, there are regions where satellite-derived soil moisture cannot be estimated because of certain conditions such as high canopy density, frozen soil, or extremely dry soil. We compared and tested three approaches, ordinary kriging (OK), regression kriging (RK), and generalized linear models (GLMs), to model soil moisture and fill spatial data gaps from the ESA CCI product version 4.5 from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation (r = 0.558, RMSE = 0.069 m3m−3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation with NASMD data when using 100% (r = 0.579, RMSE = 0.067 m3m−3), 75% (r = 0.575, RMSE = 0.067 m3m−3), and 50% (r = 0.569, RMSE = 0.067 m3m−3) of available valid pixels for each month of the study period. RK showed comparable values to OK when using different percentages of available valid pixels, 100% (r = 0.582, RMSE = 0.067 m3m−3), 75% (r = 0.582, RMSE = 0.067 m3m−3), and 50% (r = 0.571, RMSE = 0.067 m3m−3). GLM had slightly lower correlation with NASMD data (average r = 0.475, RMSE = 0.070 m3m−3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for using geostatistical approaches (OK and RK) as alternative techniques to gap-fill missing spatial values of satellite-derived soil moisture.

<p>On 17 June 2020, a large debris flow triggered by continuous heavy precipitation hit the Danba County in southwest China, blocked the river and a barrier lake was formed. Meanwhile, on the other side of the river, a large-scale landslide was triggered due to the reactivation of the ancient landslide body. Then an evacuation of more than 20000 people leaving their home town was urgently conducted.<br>This study exploits multi-sensor remote sensing techniques to assess landslide deformation, precursory deformation and post-failure motion of Danba landslide. We start with optical remote sensing images using the cross correlation method to investigate the overall information about this collapse, such as magnitude and moving direction of the sliding. Two high-resolution remote sensing optical images from Planet are processed right before and after the failure.<br>Moreover, we apply the advanced Multi-temporal InSAR (MTI) techniques such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subsets (SBAS) to analyze the precursors of the landslide over the long term. Based on the results of optical remote sensing, the descending Sentinel-1 data in 2014-2020 are extensively exploited with a better geometry of satellite observation. The long-term and transient of the deformation are analyzed against variations of precipitation, and then the related early warning systems are further explored.<br>The last stage of the work is the monitoring of current movements in the collapse region after the failure. It is explored by using multiple SAR datasets including C-band Sentinel-1 and X-band TerraSAR-X (TSX) high-resolution SAR images. With the help of the field works by our collaborators, stable artificial corner reflectors (CR) are deployed on selected sites to evaluate their performance in deriving landslide kinematics. Different from the traditional Triangle CR (TCR), the new design of dihedral CR (DCR) are introduced and exploited on the scene. The performance of this new design towards MTI processing and sub-pixel offset-tracking processing is examed and tested in this study. Results are presented and further discussed for a better assessment of Danba landslide.<br>The results of this paper can provide new strategies for developing an early warning system in this landslide using remote sensing technologies. Besides, the post-failure results are compared with the pre-event analysis, which could give an associated and comprehensive understanding of the whole landslide kinematics.</p>

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

Slow-moving landslides can transform into catastrophic landslides at certain conditions. In recent decades, catastrophic landslides around the world have caused widespread damage to buildings and threatened human lives. Thus, the detection of surface displacement variation and the definition of precursory motion for extensive slow-moving landslides become a primary goal of current researches. This study dedicates to detect and monitor surface displacement of multiple slow-moving landslide areas in Taiwan by the multitemporal interferometric SAR (MTInSAR) technique. A new package was established to generate interferograms of multiple slow-moving landslide areas at the same time. Thus, the surface displacement information in large spatial coverage and a long period of time can be obtained efficiently. The landslide characteristics in space and time domain of those slow-moving landslides were investigated and analyzed. Especially, the significant acceleration movements of certain slow-moving landslides were observed and agreed with the in situ monitoring data. The results of this study provide important information of landslide hazard assessment and show the opportunity of detecting the landslide precursory motions by the MTInSAR technique in the future.

A field monitoring study was carried out to follow the changes of fine root morphology, biomass and nutrient status in relation to seasonal changes in soil solution chemistry and moisture regime in a mature Scots pine stand on acid soil. Seasonal and yearly fluctuations in soil moisture and soil solution chemistry have been observed. Changes in soil moisture accounted for some of the changes in the soil solution chemistry. The results showed that when natural acidification in the soil occurs with low pH (3.5–4.2) and high aluminium concentration in the soil solution (>3–10 mg l−1), fine root longevity and distribution could be affected. However, fine root growth of Scots pine may not be negatively influenced by adverse soil chemical conditions if soil moisture is not a limiting factor for root growth. In contrast, dry soil conditions increase Scots pine susceptibility to soil acidification and this could significantly reduce fine root growth and increase root mortality. It is therefore important to study seasonal fluctuations of the environmental variables when investigating and modelling cause-effect relationships.

Monitoring surface deformation of permafrost in Wudaoliang Region, Qinghai–Tibet Plateau with ENVISAT ASAR data