Introduction to Sentinel 1a Sar

What is/are Sentinel 1a Sar?

Sentinel 1a Sar - In this paper, based on sentinel-1A SAR data, the surface deformation along the Gongyu expressway was tried to evaluate using time-series SBAS-InSAR method. ^[1] We applied method of persistent scatterers to the satellite Sentinel-1A SAR images and estimated the rates of displacement of the lava field surface for 2017–2019. ^[2] This research formulates a fuzzy classifier based upon Time-Weighted Dynamic Time Warping (TWDTW) distances to map vegetable types from time series of Sentinel-1A SAR images. ^[3] In order to effectively observe the rapid flow velocity, we applied the offset tracking technique to Sentinel-1A SAR images obtained from 2016 to 2020 with 36-day temporal baseline. ^[4] Here, we investigate the most recent land surface deformation in Hanoi for the period 2016 2020 using Sentinel-1A SAR data. ^[5] One case, located in Hongheyan, Gansu Province, China, was selected to reconstruct landslide morphology, identify deformation evolution behaviour and produce dynamic deformation zonation maps using 85 Sentinel-1A SAR images and three UAV fight surveys from pre-sliding to post-sliding. ^[6] The validation of FADSE was performed in the case of loess subsidence detection in Tongren county, Qinghai Province of China, using 20 Sentinel-1A SAR images acquired between February 2016 and June 2017. ^[7] In this paper, the scattering characteristics of winter wheat are studied, using time series dual-polarization Sentinel-1A SAR images. ^[8] The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. ^[9] This study aims to model surface soil moisture in bare fields using Sentinel-1A SAR data at a regional scale. ^[10] In this study, an InSAR technique called Small Baseline Subset was selected to process 60 Sentinel-1A SAR images acquired from March 2018 to March 2019. ^[11] By making full use of polarization information from Sentinel-1A SAR data before and during the disaster in Weifang City, we were able to employ the Doublet, Otsu and Region Growing methods towards VV and VH images to extract the water body information. ^[12] The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. ^[13] In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. ^[14] The paper presents the mathematical analysis of designing a down link for receiving Sentinel-1A SAR signals. ^[15] In this paper, we used persistent scatterer interferometric synthetic aperture radar (PS InSAR) measurements to study and characterize the deformation process resulted from subway-induced subsidence in the construction and operation periods of Suzhou subway using 24-scene high-resolution TerraSAR data (from December 2009 to April 2015) and 19-scene low-resolution Sentinel-1A SAR data (from May 2017 to February 2020). ^[16] Based on the Multi-criteria analysis technique, cyclone hazard maps are derived from the spatio-multi-temporal satellite data sets like RADARSAT SAR and Sentinel-1A SAR. ^[17] The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between October of 2014 and November of 2020. ^[18] For validation purposes, the proposed approach is compared to the traditional eigenvalue decomposition-based method by using simulated data and 101 real Sentinel-1A SAR images. ^[19] While comparing the two satellite-based SAR images, it is observed that high correlation between radar backscatter and field based AGB was observed in ALOS PALSAR as compared to Sentinel 1A SAR. ^[20] A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. ^[21] An investigation was carried out to generate banana area map in major banana growing districts of Tamil Nadu using polarized (VV and VH) Synthetic Aperture Radar Satellite images Sentinel 1A SAR Data with VV and VH polarization at 12days interval were downloaded for the crop growing season. ^[22]

sentinel 1a sar datum

A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. ^[1] An investigation was carried out to generate banana area map in major banana growing districts of Tamil Nadu using polarized (VV and VH) Synthetic Aperture Radar Satellite images Sentinel 1A SAR Data with VV and VH polarization at 12days interval were downloaded for the crop growing season. ^[2]