Environmental Satellite Advanced Synthetic Aperture Research Articles

Land Subsidence Evolution and Simulation in the Western Coastal Area of Bohai Bay, China

Groundwater overexploitation and loading of buildings have been the main factors triggering land subsidence along the west coast of Bohai Bay, China, since the 2000s. Uneven subsidence has been causing damage to buildings and civil facilities, loss of elevation, increasing the risk of flood and seawater intrusion, and threatening the safety of people’s lives and property. This paper analyzed the spatial and temporal features of land subsidence along the coastal area from 2003 to 2010 and from 2015 to 2020, respectively. The relations between the initiating factors and land subsidence were explored. Then, the simulation model of land subsidence was constructed through a deep learning method. During the process, multiple data were collected, including land satellite (Landsat), environmental satellite advanced synthetic aperture radar (ENVISAT ASAR) and Sentinel-1 images, leveling data, lithological data, and groundwater level data. The area occupied by buildings and vertical displacement were extracted by using supervised classification, small baseline subset (SBAS), and persistent scatterer interferometry (PSI) technologies. The gated recurrent unit (GRU) neural network was adopted to simulate the evolution of land subsidence. Results showed that the maximum annual vertical displacement rate decreased from −94 mm/yr during 2003–2010 to −87 mm/yr during 2015–2020. The correlation efficiency between the groundwater level of the third confined aquifer group and land subsidence was larger than the area occupied by buildings and the compressible layer thickness with subsidence. The constructed GRU neural network model can simulate subsidence from September 2019 to December 2019, with the overall RMSE and MAE being 3.16 mm and 2.19 mm, respectively. This work can facilitate an understanding of the evolution and prevention of land subsidence along the west coast of Bohai Bay, which will provide information for policy decisions and flood-fighting plans of the worldwide coastal cities.

Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS

The sub-Saharan African coast is experiencing fast-growing urbanization, particularly around major cities. This threatens the equilibrium of the socio-ecosystems where they are located and on which they depend: underground water resources are exploited with a disregard for sustainability; land is reclaimed from wetlands or lagoons; built-up areas, both formal and informal, grow without adequate urban planning. Together, all these forces can result in land surface deformation, subsidence or even uplift, which can increase risk within these already fragile socio-ecosystems. In particular, in the case of land subsidence, the risk of urban flooding can increase significantly, also considering the contribution of sea level rise driven by climate change. Monitoring such fast-changing environments is crucial to be able to identify key risks and plan adaptation responses to mitigate current and future flood risks. Persistent scatterer interferometry (PSI) with synthetic aperture radar (SAR) is a powerful tool to monitor land deformation with high precision using relatively low-cost technology, also thanks to the open access data of Sentinel-1, which provides global observations every 6 days at 20-m ground resolution. In this paper, we demonstrate how it is possible to monitor land subsidence in urban coastal areas by means of permanent scatterer interferometry and Sentinel-1, exploiting an automatic procedure based on an integration of the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). We present the results of PSI analysis over the cities of Banjul (the Gambia) and Lagos (Nigeria) showing a comparison of results obtained with TerraSAR-X, Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO-SkyMed) and Environmental Satellite advanced synthetic aperture radar (Envisat-ASAR) data. The methodology allows us to highlight areas of high land deformation, information that is useful for urban development, disaster risk management and climate adaptation planning.

Modeling Orbital Error in InSAR Interferogram Using Frequency and Spatial Domain Based Methods

Synthetic Aperture Radar Interferometry (SAR, InSAR) is increasingly being used for deformation monitoring. Uncertainty in satellite state vectors is considered to be one of the main sources of errors in applications such as this. In this paper, we present frequency and spatial domain based algorithms to model orbital errors in InSAR interferograms. The main advantage of this method, when applied to the spatial domain, is that the order of the polynomial coefficient is automatically determined according to the features of the orbital errors, using K-cross validation. In the frequency domain, a maximum likelihood fringe rate estimate is deployed to resolve linear orbital patterns in strong noise interferograms, where spatial-domain-based algorithms are unworkable. Both methods were tested and compared with synthetic data and applied to historical Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) sensor and modern instruments such as Gaofen-3 (GF-3) and Sentinel-1. The validation from the simulation demonstrated that an accuracy of ~1mm can be obtained under optimal conditions. Using an independent GPS measurement that is discontinuous from the InSAR measurement over the Tohoku-Oki area, we found a 31.45% and 73.22% reduction in uncertainty after applying our method for ASAR tracks 347 and 74, respectively.

A-Differential Synthetic Aperture Radar Interferometry analysis of a Deep Seated Gravitational Slope Deformation occurring at Bisaccia (Italy)

This paper presents the results of an investigation on a Deep Seated Gravitational Slope Deformation (DSGSD), previously only hypothesized by some authors, affecting Bisaccia, a small town located in Campania region, Italy. The study was conducted through the integration of conventional methods (geological-geomorphological field survey, air-photo interpretation) and an Advanced-Differential Interferometry Synthetic Aperture Radar (A-DInSAR) technique. The DSGSD involves a brittle lithotype (conglomerates of the Ariano Irpino Supersynthem) resting over a Structurally Complex Formation (Varycoloured Clays of Calaggio Formation). At Bisaccia, probably as a consequence of post-cyclic recompression phenomena triggered by reiterated seismic actions, the rigid plate made up of conglomeratic sediments resulted to be split in five portions, showing different rates of displacements, whose deformations are in the order of some centimeter/year, thus inducing severe damage to the urban settlement.A-DInSAR techniques confirmed to be a reliable tool in monitoring slow-moving landslides. In this case 96 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, in ascending and descending orbits, have been processed using SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politècnica de Catalunya (UPC). The DInSAR results, coupled with field survey, supported the analysis of the instability mechanism and confirmed the historical record of the movements already available for the town.

Estimation of internal wave velocity in the shallow South China Sea using single and multiple satellite images

This paper presents results on the estimation of the phase velocity of internal waves (IWs) in the shallow waters of the South China Sea in terms of the water depth using satellite data. To calculate the phase velocity, two methods are considered. The first is based on the different positions of IW images acquired by Environmental Satellite-advanced synthetic aperture radar (ASAR) and Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) in a very short time interval, and the second method uses the images of several packets of IWs associated with semidiurnal tidal period in either a single ASAR or MODIS image. The former is termed as the multitemporal images (MTI) method, and the latter is referred to as the tidal period images (TPI) method. The results show that the wave velocities estimated by the MTI method have strong correlation with those derived from a depth-dependent IW propagation theory, while those by the TPI method have moderate correlation. Both the methods can be used to estimate the IW phase velocity but with reduced accuracy by the TPI method. The main reason of this reduced accuracy is the long tidal period during which the characteristics of IWs change as they propagate over long distance in the water of variable currents and/or bottom topography.

Surface soil moisture estimation over dense crop using Envisat ASAR and Landsat TM imagery: an approach

This study focuses on developing a new method of surface soil moisture estimation over wheat fields using Environmental Satellite Advanced Synthetic Aperture Radar (Envisat ASAR) and Landsat Thematic Mapper (TM) data. The Michigan Microwave Canopy Scattering (MIMICS) model was used to simulate wheat canopy backscattering coefficients from experiment plots at incidence angles of 23° (IS2) and 43.9° (IS7). Based on simulated data, the scattering characteristics of a wheat canopy were first investigated in order to derive an optimal configuration of polarization (HH) and incidence angle (IS2) for soil moisture estimation. Then a parametric model was developed to describe wheat canopy backscattering at the optimal configuration. In addition, direct backscattering and two-way transmissivity of wheat crowns were derived from the TM normalized difference vegetation index (NDVI); direct ground backscattering was derived from surface soil moisture and TM NDVI; and backscattering from double scattering was derived from total backscattering. A semi-empirical model for soil moisture estimation was derived from the parametric model. Coefficients in the semi-empirical model were obtained using a calibration approach based on measured soil moisture, ASAR, and TM data. A validation of the model was performed over the experimental area. In this study, the root mean square error (RMSE) for the estimated soil moisture was 0.041 m3 m−3, and the correlation coefficient between the measured and estimated soil moisture was 0.84. The experimental results indicate that the semi-empirical model could improve soil moisture estimation compared to an empirical model.

Comparison between Differential SAR interferometry and ground measurements data in the displacement monitoring of the earth-dam of Conza della Campania (Italy)

This paper presents the application of Differential Synthetic Aperture Radar (SAR) interferometry (DInSAR) algorithms for the precise monitoring of earth dams ground deformation. The test site selected for this study is the Conza Dam, located in the southern Apennines (Italy), very close to the epicenter of the big earthquake (Mw=6.9) which took place on November 23th, 1980, striking the Irpinia region. Among the multiple advanced DInSAR techniques developed by the SAR community during the last decade, this work uses the so-called SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politècnica de Catalunya (UPC), which is based on the use of Coherent Pixels Technique (CPT) algorithm. The analysis is carried out using 51 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, corresponding to the period from 29th of November 2002 to the 30th of July 2010. In this framework, ground displacements recorded by a network of conventional ground-based sensors are also available for the same temporal span. Indeed, the embankment dam is well instrumented to measure internal settlements by means of cross-arms placed in six different cross-sections, and superficial displacements by means of targets for precise leveling. A statistical analysis has been performed to carry out a better comparison between the measurements obtained with the conventional field sensors and the interferometric data. The high agreement between final DInSAR displacements and in-situ instrumental data, demonstrates the reliability of such technique for the precise monitoring of civil infrastructures, and concretely, in dams with a high exposure factor and its consequent risk.

Capability Assessment of Fully Polarimetric ALOS–PALSAR Data for Discriminating Wet Snow From Other Scattering Types in Mountainous Regions

This paper examines the capability assessment of fully polarimetric L-band data for the snow and nonsnow-area classifications. The data sets used are the fully polarimetric Advanced Land Observation Satellite-Phased Array-Type L-Band Synthetic Aperture Radar data, optical Advanced Land Observing Satellite (ALOS)-advanced visible and near-infrared radiometer-2 data close to the radar acquisition, and environmental satellite-advanced synthetic aperture radar data. Several parameters are used to discriminate the snow-covered areas from nonsnow-covered areas in the Indian Himalayan region, including backscattering coefficients, the ratio of cross/copolarized backscattering power and polarization fraction (PF) value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart-supervised classification. Among various parameters and methods, it was found that the alternative newly proposed PF scheme, based on the implementation of fully polarimetric synthetic aperture radar data, yielded the best classification result in the absence of the training samples. The PF value has been effective for discrimination of the snow-covered areas from nonsnow-covered areas, debris-covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow-laden mountainous scenarios, which may require fully polarimetric S-band or C-band PolSAR measurements.

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR), and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) radar signals at horizontally emitted and received polarization (HH) for pasture monitoring at the paddock scale in order to guide farmers for better management. The pasture site is selected, in Otway, Victoria, Australia. The biomass, water content of grass, and soil moisture over this site were analyzed with these three bands of SAR images, through linear relationship between SAR backscattering coefficient, and vegetation indices Normalized Differential Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI)), together with soil moisture index (MI). NDVI, NDWI, and MI are considered as proxy of pasture biomass, plant water content, and soil moisture, respectively, and computed from optical images and climate data. SAR backscattering coefficient and vegetation indices are computed within a grass zone, defined by classification with MODIS data. The grass condition and grazing activities for specific paddocks are detectable, based on SAR backscatter, with all three wavelengths datasets. Both temporal and spatial analysis results show that the X-band SAR has the highest correlation to the vegetation indices. However, its accuracy can be affected by wet weather due to its sensitivity to the water on leaves. The C-band HH backscattering coefficient showed moderate reliability to evaluate biomass and water content of grass, with limited influence from rainfall in the dry season. The L-band SAR is the less accurate one for grass biomass measurement due to stronger penetration.

A regional investigation of urban land-use change for potential landslide hazard assessment in the Three Gorges Reservoir Area, People's Republic of China: Zigui to Wanzhou

The near-completion of the Three Gorges Dam has led to the creation of a narrow reservoir that, when completed in 2009, will stretch over 660 km upstream and result in the displacement of approximately 1.2 million people. This reservoir will drown more than 100 towns, some of which have already been lost due to the rising waters, and result in a significant change in land use. New urban areas have been constructed at higher elevations to avoid the rising water but it is feared that some of these settlements may now be exposed to a greater risk of landslides due to slope failure. A geographic information system (GIS) consisting of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat Thematic Mapper (TM), and Environmental Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) data has been created and used to monitor the urban changes from before the Dam's construction to the present day as well as changes in landslide susceptibility. To perform this analysis, a new 30 m high-resolution digital elevation model (DEM) was derived by combining an ASTER and Shuttle Radar Topography Mission (SRTM) DEM. Fieldwork was carried out along the Yangtze River, where the urban boundaries were field-checked using GPS to navigate to the satellite-derived checkpoints. The results show that a majority of the new urban areas are in fact located on shallow slopes, but are often positioned below steep slopes, which could pose a future threat of landslide risk to the inhabitants of the new towns.

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.

Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data

In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.

Multipolarization Microwave Scattering Model for Sahelian Grassland

A coherent scattering formulation is developed for radar remote sensing of Sahelian grassland. This African vegetation is mainly composed of annual grass and shrubs. In the proposed procedure, first, a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal</i> model for generation of grass and shrub structures, which includes important realistic botanical information, is implemented. Because we develop a coherent scattering model, preserving the relative position of plant elements in a statistical manner as accurately as possible is very important. Shrubs are reproduced using cylindrical elements which represent trunks, branches, and thin green stems that function as leaves for these shrubs. Their crown shape is highly irregular, but for the most part can be encompassed in an ellipsoidal or cylindrical volume; on the other hand, the grass is represented as a set of cylindrical stalks and blade leaves. The scattered power from each grass element is added because multiple scattering among adjacent elements can be neglected at microwave frequencies. We calculate the soil scattering using the Integral Equation Method and neglect the soil volume scattering which may become significant for dry soil condition at high incidence angles. Backscatter statistics are acquired via a Monte Carlo simulation over a large number of realizations. The accuracy of the model is verified using measured data acquired by the C-band environmental satellite advanced synthetic aperture radar instrument at different incident angles.

Interferometry With ENVISAT Wide Swath ScanSAR Data

The possibility to get efficient topographic mapping and monitoring of large-scale motions with ScanSAR interferometry has been demonstrated with the Shuttle Radar Topography Mission and RADARSAT mission. The Environmental Satellite Advanced Synthetic Aperture Radar (ASAR) sensor has been designed to provide enhanced capabilities for interferometric applications. Different types of interferometric products can be obtained by combining the various ASAR modes as stripmap synthetic aperture radar [image mode (IM)] and ScanSAR [wide swath (WS) mode]. This letter deals with the possibility to use WS data to get either mixed-mode (IM/WS) or ScanSAR mode (WS/WS) differential interferograms. The impact of digital elevation model localization errors on IM/WS interferograms and of scan pattern synchronization on WS/WS interferograms is investigated. Experimental results are encouraging and show that ASAR ScanSAR data can be routinely used for interferometric applications in both cases

Engineering geology in Canada

This paper presents the results of an investigation on a Deep Seated Gravitational Slope Deformation (DSGSD), previously only hypothesized by some authors, affecting Bisaccia, a small town located in Campania region, Italy. The study was conducted through the integration of conventional methods (geological-geomorphological field survey, air-photo interpretation) and an Advanced-Differential Interferometry Synthetic Aperture Radar (A-DInSAR) technique. The DSGSD involves a brittle lithotype (conglomerates of the Ariano Irpino Supersynthem) resting over a Structurally Complex Formation (Varycoloured Clays of Calaggio Formation). At Bisaccia, probably as a consequence of post-cyclic recompression phenomena triggered by reiterated seismic actions, the rigid plate made up of conglomeratic sediments resulted to be split in five portions, showing different rates of displacements, whose deformations are in the order of some centimeter/year, thus inducing severe damage to the urban settlement.A-DInSAR techniques confirmed to be a reliable tool in monitoring slow-moving landslides. In this case 96 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, in ascending and descending orbits, have been processed using SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politècnica de Catalunya (UPC). The DInSAR results, coupled with field survey, supported the analysis of the instability mechanism and confirmed the historical record of the movements already available for the town.