Srinagar city is located in the heart of the Kashmir valley of the northwest Himalaya and is the largest urban center in the seismically active region. As yet, no direct deformation measurement or observation of any kind has been made in Srinagar and the surrounding areas using InSAR. We detect and quantify the ground deformation in the city's western flank using the InSAR time series. Stanford Method for Persistent Scatterer (StaMPS) is employed to process Sentinel-1A radar images acquired between 2015 and 2022 for ascending (161 scenes) and 2020 to 2022 for descending track (31 scenes). Generated velocity fields were decomposed into vertical rate maps, revealing a deformation of 17 mm year(-1) for ascending and 19 mm year(-1) for descending track. Time series analysis exhibits an identical deformation rate for both tracks on concurrent dates. Time-series GPS data was employed to validate the outcomes of our InSAR analysis. A field survey conducted in the main zone of deformation revealed extensive damage to structures in the form of wide cracks. Such cracks develop in older infrastructure (similar to 8 years) due to cumulative ground deformation over several years. Geotechnical investigation and strength calculation on a 30-m borehole of the subsiding region shows a vertical domination of high void, floodplain soils, with appreciable amounts of decomposed organic matter and lower shear strength parameters that are prone to volume reduction and particle rearrangement upon wetting and loading. The overall relevance of this study is in detecting and quantifying such subsidence in the Kashmir basin using SAR remote sensing. We also seek to establish a linkage of this deformation with the local stratum to allow for more consideration and efficient planning of civil infrastructure in the subsidence-prone regions of the citified zone and appropriate management of the subsidence-induced risk.

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

In this study, a methodology is proposed to use dual-polarimetric synthetic aperture radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5-6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: first the spatial distributions of soil liquefaction is estimated using a constant false alarm rate method applied to the SPAN metric, namely the total power associated with the measured polarimetric channels, which is ingested into a bitemporal approach to sort out dark areas not genuine. Second, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix-namely the degree of polarization. The latter is evaluated using the coseismic scenes and contrasted with the preseismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-the-art optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Turkiye-Syria earthquake, confirm the soundness of the proposed approach.

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions.

Landslides are mass movements of rock or soil down a slope, which may cause economic loss, damage to natural resources and frequent fatalities. To support risk management, landslide dating methods can provide useful knowledge about the date of the landslide and the frequency of occurrences, and thus potential triggers. Remote sensing techniques provide opportunities for landslide dating and are especially valuable in remote areas. However, the use of optical remote sensing is frequently hampered by cloud cover, decreasing the success rate and accuracy of dating. Here, we propose a landslide dating framework that combines the advantages of optical and SAR remote sensing satellites, because optical monitoring provides spectral changes on the ground and microwave observations provide information on surface changes due to loss of coherence. Our method combines Sentinel-1 and-2 satellite data, and is designed for cases wherein the landslide causes vegetation decrease and terrain deformation resulting in changing Normalized Difference Vegetation Index (NDVI) and SAR backscatter values. This landslide dating framework was tested and evaluated against 60 published landslides across the world. We show that the mean accuracy of landslide dating reaches 23 days when using combined Sentinel-1 and-2 imagery, which is a pronounced improvement compared to using only optical Sentinel-2 images resulting in an accuracy of 51 days. This study highlights that a combination of optical and SAR remote sensing monitoring is a promising technique for dating landslides, especially in remote areas where monitoring equipment is limited or which are frequently covered by clouds. Our method contributes to identifying failure mechanism by providing reliable date ranges of landslide occurrence, assessing landslide hazard and constructing landslide early warning systems.

Permafrost, known for its high sensitivity to climate change and human activities, plays a crucial role in comprehending the environmental sustainability of Arctic cities. Yakutsk, being the largest city situated in continuous permafrost, has suffered irreversible damage to surface buildings due to the widespread permafrost thaw. Therefore, it is imperative to conduct detailed monitoring and assessment of surface deformation to ensure the stable operation of building facilities and to plan urban development situated above the permafrost. This study utilized 138 scenes of Sentinel-1B images captured between 2017 and 2021 to process the surface deformation variations of Yakutsk using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method. The surface deformation rate map reflects that the study area is undergoing extensive surface subsidence. The cumulative deformation time series maps effectively trace the temporal and spatial evolution of surface deformation, with the highest surface subsidence rates exceeding 40 mm/year and maximum cumulative subsidence exceeding 250 mm. In-depth analysis of the obtained results indicated that the permafrost in Yakutsk is undergoing degradation, and the primary reason for surface subsidence is the thawing of foundation soils and the degradation of ice-bearing permafrost. This study provides basic data for the investigation of geohazards caused by surface subsidence in Yakutsk. Permafrost, due to its high sensitivity to climate change and human activities, plays a crucial role in understanding the environmental sustainability of Arctic cities. However, widespread global climate warming has caused irreversible damage to the urban surface situated on permafrost. Despite this, there still remains a significant amount of uncertainty in research pertaining to this issue. Therefore, this study is designed to investigate the surface deformation of Yakutsk, the largest city situated on permafrost, using times series remote sensing techniques.image

Glacier velocity is a crucial parameter in understanding glacier dynamics and mass balance, especially in response to climate change. Despite numerous studies on glaciers in the West Kunlun Mts., there is still insufficient knowledge about the details of inter- and intra-annual velocity changes under global warming. This study analyzed the glacier velocity changes in the West Kunlun Mts. using Sentinel-1A satellite data. Our results revealed that: (1) The velocity of glaciers across the region shows an increasing trend from 2014 to 2023. (2) Five glaciers were found to have been surged during the study period, among which two of them were not reported before. (3) The surges in the study region were potentially controlled through a combination of hydrological and thermal mechanisms. (4) The glacier N2, Duofeng Glacier, and b2 of Kunlun Glacier exhibit higher annual velocities (32.82 m a-1) compared to surging glaciers in quiescent phases (13.22 m a-1), and were speculated as advancing or fast-flowing glaciers.

To characterize the spatiotemporal variations of glacier surface speed on the Kenai Peninsula, Alaska (similar to 3,900 km(2)), we derived 92 surface speed fields between October 2014 and December 2019 using intensity offset tracking on Sentinel-1 data. On average, speeds are 50% greater in spring (March-May) than the annual mean (69 m a(-1)) while winter speeds are close to the annual mean. While marine-terminating glaciers have their maximum speed near the terminus, both land- and lake-terminating glaciers flow fastest around the median glacier elevation. On average, the lake-terminating and tidewater glaciers flow 1.7 and 2.3 times faster than the land-terminating glaciers, respectively. Monthly variations over the 5-year period are strikingly synchronous regardless of terminus type suggesting that regional-scale meteorological drivers govern the temporal variability. Mean annual speeds fluctuate roughly +/- 10% of the period mean without an apparent trend. At lake-terminating Bear Glacier, a short-term tripling in ice speed in fall 2019 over the area below an ice-dammed lake coincides with an observed glacier lake outburst flood (GLOF). An earlier GLOF caused a persistent breach of the beach barrier between the proglacial lake and ocean which likely led to overall speed-up of the lower glacier part throughout 2019. A significant speedup was also observed at the lower part of the lake-terminating Ellsworth Glacier and attributed to rapid glacier retreat and lake expansion, probably further amplified by the terminus area becoming buoyant and a large tabular iceberg breaking off. Our results highlight the impact of GLOFs and proglacial characteristics in spatial and temporal glacier speed variations.