Synthetic Aperture Radar Series Research Articles

Coupling effect of impoundment and irrigation on landslide movement in Maoergai Reservoir area revealed by multi-platform InSAR observations

The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai Reservoir (MEGR) is located in the middle reaches of the Heishui River in Sichuan Province, China. Following impoundment, numerous active slopes have arisen in the reservoir area, with the Xierguazi-Mawo landslide complex (XMLC) potentially being the most threatening. In this paper, we first acquired the surface displacement of the MEGR area, before and after impoundment, using time series Synthetic Aperture Radar Interferometry (InSAR) analysis of the ALOS-1/PALSAR-1, ALOS-2/PALSAR-2 and Sentinel-1 datasets. Then, the three-dimensional (3D) deformation field was inverted by combining multi-track InSAR observations and a topography-constrained model. Results show the presence of 47 active landslides within the 675 km2 reservoir area, a significant increase compared to the seven landslides identified prior to impoundment. Based on the derived 3D velocity vectors, we found that the motion of the XMLC exhibits heterogeneity in terms of spatial and temporal patterns. Further, the displacement time series of all detected active landslides suggests that irrigation and water level fluctuations are two major factors influencing the kinematic behaviors of landslides. Irrigation accelerates landslide movement and thus permanently alters the deformation trend, while water level fluctuations cause seasonal oscillations in the displacement time series with a time lag of approximately 100 days. The above results can contribute to landslide management and prevention in the MEGR area and provide a representation for numerous water reservoirs in southwest China.

Mapping seasonal glacier melt across the Hindu Kush Himalaya with time series synthetic aperture radar (SAR)

Abstract. Current observational data on Hindu Kush Himalayas (HKH) glaciers are sparse, and characterizations of seasonal melt dynamics are limited. Time series synthetic aperture radar (SAR) imagery enables detection of reach-scale glacier melt characteristics across continents. We analyze C-band Sentinel-1 A/B SAR time series data, comprised of 32 741 Sentinel-1 A/B SAR images, and determine the duration of seasonal glacier melting for 105 432 mapped glaciers (83 102 km2 glacierized area, defined using optical observations) in the HKH across the calendar years 2017–2019. Melt onset and duration are recorded at 90 m spatial resolution and 12 d temporal repeat. All glacier areas within the HKH exhibit some degree of melt. Melt signals persist for over two-thirds of the year at elevations below 4000 m a.s.l. and for nearly half of the calendar year at elevations exceeding 7000 m a.s.l. Retrievals of seasonal melting span all elevation ranges of glacierized area in the HKH region, extending greater than 1 km above the maximum elevation of an interpolated 0 ∘C summer isotherm and at the top of Mount Everest, where in situ data and surface energy balance models indicate that the Khumbu Glacier is melting at surface air temperatures below −10 ∘C. Sentinel-1 melt retrievals reflect broad-scale trends in glacier mass balance across the region, where the duration of melt retrieved in the Karakoram is on average 16 d less than in the eastern Himalaya sub-region. Furthermore, percolation zones are apparent from meltwater retention indicated by delayed refreeze. Time series SAR datasets are suitable to support operational monitoring of glacier surface melt and the development and assessment of surface energy balance models of melt-driven ablation across the global cryosphere.

Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland.

Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives

The research and improvement of methods to be used for deformation measurements from space is a challenge. From the previous 20 years, time series Synthetic Aperture Radar (SAR) interferometry techniques have proved for their ability to provide millimeter-scale deformation measurements over time. This paper aims to provide a review of such techniques developed in the last twenty years. We first recall the background of interferometric SAR (InSAR). We then provide an overview of the InSAR time series methods developed in the literature, describing their principles and advancements. Finally, we highlight challenges and future perspectives of the InSAR in the Big Data era.

Mapping Land Cover Based on Time Series Synthetic Aperture Radar (SAR) Data in Klaten, Indonesia

<p><em>Information </em><em>on </em><em>the existing land cover is important for land management and planning because it can represent the intensity, location, and pattern of human activities. However, mapping land cover in tropical regions is not easy when using optical remote sensing due to the scarcity of cloud-free images. Therefore, the objective of this study is to map the land cover in Klaten Regency using a time-series Sentinel-1 data. Sentinel-1 data is one of remote sensing image</em><em>s</em><em> with Synthetic Aperture Radar (SAR) system which is well known by its capabilit</em><em>y</em><em> of cloud penetration and all-weather observation. A time-series Sentinel-1 data of both polarisations, VV and VH were automatically classified using an unsupervised classification technique, ISODATA. The results show that the land cover classifications obtained overall accuracies of 79</em><em>.</em><em>26% and 73</em><em>.</em><em>79</em><em>% </em><em>for VV and VH respectively. It is also found that Klaten is still dominated by the vegetated land (agriculture and non-agricultural land).</em><em> T</em><em>hese results suggest the opportunity of mapping land cover using SAR multi temporal data. </em></p><p><strong><em> </em></strong></p><p><strong><em> Keywords</em></strong><em>: </em><em>Land cover; Synthetic Aperture Radar; Time series; Sentinel-1; Klaten</em><em></em></p>

Semi-automatic selection of optimum image pairs based on the interferometric coherence for time series SAR interferometry

ABSTRACTTime series synthetic aperture radar (SAR) interferometry (TS-InSAR) has been widely used for monitoring ground deformation. The selection of interferometric image pairs directly affects the quality of the interferograms and the accuracy of deformation monitoring. The Small BAseline Subset (SBAS) method has been widely adopted to form interferograms in TS-InSAR. However, the selection of low-quality interferograms or missed selection of good interferograms due to temporal and/or spatial baselines exceeding the thresholds sometimes occurs in the traditional SBAS method. This letter proposes a new method, namely, the semi-automatic selection of optimum image pairs (SASOIP), based on the interferometric coherence. By quantitatively evaluating the coherence of point targets in a small feature region, the SASOIP results in high-quality interferometric image pairs selected from all possible interferometric combinations for any two SAR images. The SASOIP method can effectively reduce the probability of poor selection and missed selection of quality image pairs. Twenty-nine COSMO-SkyMed SAR images acquired from 27 August 2011, to 29 July 2015, over Tianjin, China, are used to test the proposed algorithm. Validation with 102 levelling data points demonstrates that the accuracy of ground deformation derived from the interferograms by the SASOIP method is 23.13% higher than that based on the SBAS method.

Mining change patterns in time series high‐resolution SAR images

Here, the authors use the K-Shape algorithm, which is based on a z-normalised version of cross-correlation distance measure, to extract change patterns from time series synthetic aperture radar (SAR) images. These change patterns focus only on the shape of time series, rather than amplitude and phase. Besides, they employ a simple and elegant method to choose the optimal number of clusters. Experimental results on multi-temporal SAR images of Shanghai indicate the effectiveness of their approach.

Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry (DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas.

Time Series Synthetic Aperture Radar Interferometry for Ground Deformation Monitoring over a Small Scale Tectonically Active Deltaic Environment (Mornos, Central Greece)

ABSTRACT Parcharidis, I.; Kourkouli, P.; Karymbalis, E.; Foumelis, M., and Karathanassi, V., 2013, Time series synthetic aperture radar interferometry for ground deformation monitoring over a small scale tectonically active deltaic environment (Mornos, Central Greece). This study deals with the estimation of subtle ground deformation at millimetric accuracy over the broader area of the Mornos River delta in Central Greece and its spatio-temporal distribution for the period between 1992 and 2009 through Persistent Scatterers Interferometry (PSI). The results showed that the majority of the scatterers, which show subsidence, are located within the delta plain with mean subsidence rates throughout the delta varying between −7.2 and +0.7 mm/y. An attempt is made to highlight the geographic distribution, the amplitude, and the causes of the observed delta plain subsidence. The positive correlation between the thickness of the fine-grained Holocene deltaic deposits and the subsidence rates reveals that the main...