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To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN.
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This estimation of AGB from remote sensing data is now supported by the availability of the freely available dual-polarization Sentinel 1 SAR data.
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The present study has been performed using GMTSAR software with Sentinel 1 SAR data of C band for the duration of 2017–2019 (January to April) and focused particularly over the area of Jagadhri city which is situated 100 km away from Chandigarh, which has been identified under the potential threat of land subsidence.
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The research was conducted to investigate the capability of Sentinel 1 SAR multi temporal data to detect the growth phase of paddy crop.
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The research was conducted to investigate the capability of Sentinel 1 SAR multi temporal data to detect paddy field based on growth phenology of rice crop.
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First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area.
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All the synthetic interferogram based on Sentinel-1 SAR Image acquisition dates over Seoul, Korea.
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<p>The Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) mission consists of two satellites A and B launched in April 2014 and April 2016, respectively.
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We combined Sentinel-1 SAR and Landsat 8 optical imagery to classify marshes and open water in both regions, with user’s and producer’s accuracies exceeding 89%.
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Sentinel-1 SAR and Sentinel-2 multispectral data were exploited to detect the films around three aquaculture sites.
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We used 2222/2223 Sentinel-1 SAR images (wind speed ranges from 5 to 20 m/s) to fit/validate the algorithm.
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In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.
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We analysed the temporal evolution of ice cover using satellite images from multiple satellite missions – MODIS on Terra and Aqua, Sentinel-1 SAR, Sentinel 2 MSI, Landsat-8, PlanetScope, satellite photography from International Space Station, and radar altimetry data from Jason-3.
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The velocity information is derived from archived and new Sentinel-1 SAR acquisitions by applying feature and speckle tracking.
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Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India.
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Subsequently, in the cloud-covered region, an analysis of dual-polarization RGB false color composites images and backscattering coefficient differences of Sentinel-1 SAR data were found an apparent response to ground roughness’s changes caused by the flood.
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This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014.
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This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM).
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In this study, more than 280 Sentinel-1 SAR images are used to derive significant wave heights (Hs) of the sea surface using a polarization-enhanced methodology.
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It is of great significance to extract building density using dual-polarimetric Sentinel-1 SAR data which provides a short revisit period and a wide coverage.
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Analysis of ground movement rates along the coastline and upper sections of the Ventnor landslide complex was carried out utilizing Persistent Scatterer Interferometric Synthetic Aperture Radar methods using Sentinel-1 SAR data from 2015 to 2019 (four years).
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Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium.
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Using the time-series Sentinel-1 SAR images acquired since 2016, we develop a burst-based, phase-gradient stacking algorithm to sum up phase gradients along the azimuth and range directions of short-temporal-baseline interferograms.
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Apart from this, the utility of cloud computing platforms, such as the Google Earth Engine (GEE) and Amazon Web Services (AWS), is explored to generate crop inventory maps from operational Sentinel-1 SAR data sets.
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This study examined a methodology for urban flood mapping based on discrimination analysis of pre- and co-event interferometric coherences obtained from multitemporal Sentinel-1 SAR images.
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Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape.
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This study was carried out using the entire archive of ERS, ENVISAT and Copernicus Sentinel-1 SAR data.
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C-band military radars cause interferences to Sentinel-1 SAR satellite when it passes over their application area.
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</p><p>In this study Copernicus Sentinel-1 SAR and Sentinel-2 optical images acquired on a mid-term time period between 2017 April and 2020 December were used to generate a median composite.
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We also compared the spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 SAR data.
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The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates.
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This study aims to map deforestation in Permanent Forest Reserve (HSK) Yong in Pahang between 2017 and 2020 using satellite images of Sentinel-1 SAR.
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Those techniques use Sentinel-1 SAR scattering characteristics and field observations with principal component analysis (PCA), support vector machine (SVM), and logistic regression (LR) in the western range of the Tianshan Mountains of Xinjiang, China.
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Land motion obtained by integration of 2 tracks of Sentinel-1 SAR images and 19 CGPS stations shows that the recent land subsidence in Tianjin downtown is less than 8 mm/yr, which has significantly decreased with respect to the last 50 years (up to 110 mm/yr in the 1980s).
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Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm.
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The Sentinel-1 SAR wind is ingested into LAPS, a numerical system developed at NOAA, specifically designed for data analysis and nowcasting issues, since it has the advantage of being faster and less computational demanding than advanced data assimilation methods.
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The latter was acquired by analyzing the ground deformation with multi-temporal Sentinel-1 SAR datasets and covered between 2015 and 2017.
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The main purpose of the study is to monitor the actual condition of the urbanization on unsuitable and important sites and to guide in determining pioneer areas for gentrification process using freely available remote sensing images, in particular Sentinel-1 SAR images.
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Utilization of Sentinel-1 SAR data to monitor illegal oil spills is expected to reduce violations that occur in Indonesian territorial waters.
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This study shows the high potential offered by Sentinel-1 SAR C-band time series for the detection of forest phenology for the first time, thus overcoming the limitations caused by cloud cover in optical remote sensing of vegetation phenology.
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Free availability of SAR data through the European Space Agency’s (ESA) Sentinel-1 SAR mission created a major opportunity for flood extent monitoring.
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In this study, land subsidence in Semarang was mapped using time-series analysis based on Stanford Methods for Persistent Scatterer (StaMPS) on the Sentinel-1 SAR datasets from March 2017 to May 2020 in both ascending and descending tracks.
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Here we investigated subsidence rates in Wuhan city with 2015–2019 Sentinel-1 SAR images.
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A case study near Sydney is included here based on Sentinel-1 SAR and Sentinel-2 optical satellite data collected on 10 and 11 October 2020, respectively.
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We used Landsat images to examine changes in wetland areas and Sentinel-1 SAR images to investigate water level and vegetation structure.
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For this case study, the Sentinel-1 SAR Band data obtained from the ESA (European Space Agency) were used as research data.
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The proposed approach is validated using Sentinel-1 SAR coherence time series and found to be accurate.
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The advent of Sentinel-1 SAR data with high temporal and medium spatial resolutions along with its being unaffected by presence of cloud provided opportunities for using remote sensing in mapping L.
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Along this thread, the present study aims to investigate the feasibility and mode of implementation of Sentinel-1 SAR data and InSAR techniques to estimate post-war damage in war-affected areas as opposed to using commercial high-resolution optical images.
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We utilise Sentinel-1 SAR (Synthetic Aperture Radar) time series data to distinguish charcoal production patterns at regional scale.
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In addition to GNSS data, the InSAR process has been performed by using ESA Sentinel-1 SAR data, and the vertical deformations were clarified with the unwrapped interferogram.
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To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN.
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The objective of this research is to investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR and Sentinel-2 MSI time series for increasing the frequency and accuracy of wildfire progression mapping.
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The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%).
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</p><p>The Sentinel-1 SAR satellites from the Copernicus mission provide acquisitions over Iceland since summer 2015.
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Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.
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Publication of this database reduces the burden of processing and extracting a large volume of Sentinel-1 SAR data for experts.
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In this communication, we present an application of Sentinel-1 SAR images to map the extension of a recent occupation of an area with unfavorable soil conditions against earthquakes.
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In this study, dual-polarization (DP) C-band Sentinel-1 SAR imagery are processed to generate a damage map related to an earthquake damaged area.
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A Recurrent Neural Network multi-sensor regression approach (SenRVM), relying on the systematic acquisitions of Sentinel-1 SAR satellite, has been thereby proposed.
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We present the results of our proposed method on the ICEYE-X2 and SENTINEL-1 SAR data, demonstrating its ability to produce pixel-accurate river masks.
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Accordingly, a novel Sentinel-1 SAR-based flood mapping algorithm S1-L1 to discern flood inundation from water lookalike surfaces in arid regions.
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The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient.
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This study showed the potential application of time-series Sentinel-1 SAR data, annual composite data and knowledge-based classifiers for large-scale coastal zones, and these data will be valuable for coastal ecological restoration and sustainability management.
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In this presentation we explore the algorithm issues associated with the switch and compare the products during the period when both the SMAP radar and Sentinel-1 SARs were operating.
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The preliminary results shows that the Sentinel-1 SAR data would give effective results and spatial information on oil spill detection to decision-makers.
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Of particular importance is the Copernicus program of the European Commission and ESA, which provides us with an inexhaustible source of free SAR data with extraordinary potential for monitoring the earth's surface thanks to the constellation of Sentinel-1 SAR satellites.
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The datasets from Landsat-8 and Sentinel-1 SAR satellite are used for ice stream velocity estimation using feature-offset tracking and differential interferometric SAR (DInSAR) methods.
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In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season.
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To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system.
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Our analysis is based on a diachronic set of high-resolution satellite imagery: declassified CORONA KH-4B from 1968, SPOT-1 from 1989, and multisensor stacked layers from Sentinel-1 SAR together with Sentinel-2MSI from 2018.
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The change detection method of multi-temporal analysis is often used to estimate surface soil moisture from Sentinel-1 SAR data.
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Limited experimental results based on Sentinel-1 SAR images indicate the CVMV-UNet we proposed can achieve state-of-art classification accuracy in SAR images land cover segmentation.
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In this work, we analyze the potential of Sentinel-1 SAR data in mapping natural disturbances in forests, through the 2014 ice break event.
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Here we use 89 Sentinel-1 SAR images to measure surface displacement from January 2017 through December 2019.