Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community. Single and multi-date data preprocessing workflow for Sentinel-1  A generic workflow to obtain dual-pol covariance matrix elements from SLC products  Accurate sub-pixel level coregistration of multi-date data

<p><strong>Abstract.</strong> Glaciers are melting at an alarming rate due to global warming. Two major glaciers of India viz. Gangotri and the Siachen are chosen for the velocity mapping. The line-of-sight (LOS) velocity fields are derived using X-band TerraSAR-X and C-band Sentinel-1A datasets. An intensity-based offset tracking method is used to generate LOS velocities of the glaciers. The single look complex (SLC) images of the TerraSAR-X are converted into intensity before applying the offset tracking method, whereas the ground range detected (GRD) products from Sentinel-1A are directly used to estimate the glacier velocities. The Siachen glacier velocity is mapped using three X-band images from 2011 to 2017 and a C-band image between 2017 and 2018. The X-band images in the case of Siachen are available with the long-time interval between the master and slave images. The velocity of the glacier is observed to be around 30&amp;ndash;40<span class="thinspace"></span>cm<span class="thinspace"></span>day^&amp;minus;1 from X-band and around 45&amp;ndash;50<span class="thinspace"></span>cm day^&amp;minus;1 from C-band. Three X-band images in the year 2012 and a C-band image in the year 2018 are used for the Gangotri glacier velocity estimation. These images are very closely separated in time, and the velocity of the glacier is found to be 15&amp;ndash;20<span class="thinspace"></span>cm<span class="thinspace"></span>day^&amp;minus;1. A dataset with a temporal gap of approximately three years is also used for the Gangotri glacier velocity estimation and observed a large difference in velocity (&amp;sim;10<span class="thinspace"></span>cm<span class="thinspace"></span>day^&amp;minus;1) from that of shorter interval data. Therefore, for a slow-moving glacier like Gangotri, a dataset with a high temporal gap may not give a reliable result. It is also observed that X-band TerraSAR-X results are more accurate than the C-band Sentinel-1A results. The penetration depth of X-band is less compared to C-band, which might result in accurate estimation of glacier surface flow. According to the results, the velocity of the Siachen glacier is increasing at a very high rate.</p>

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Persistent Scatterer Interferometry (PSI) is an advanced technique to map ground surface displacements of an area over a period. The technique can measure deformation with a millimeter-level accuracy. It overcomes the limitations of Differential Synthetic Aperture Radar Interferometry (DInSAR) such as geometric, temporal decorrelation and atmospheric variations between master and slave images. In our study, Sentinel-1A Interferometric Wide Swath (IW) mode descending pass images from May 2016 to December 2017 (23 images) are used to identify the stable targets called persistent scatterers (PS) over Bengaluru city. Twenty-two differential interferograms are generated after topographic phase removal using the SRTM 30 m DEM. The main objective of this study is to analyze urban subsidence in Bengaluru city in India using the multi-temporal interferometric technique such as PSI. The pixels with Amplitude Stability Index ≥ 0.8 are selected as initial PS candidates (PSC). Later, the PSCs having temporal coherence > 0.5 are selected for the time series analysis. The number of PSCs that are identified after final selection are reduced from 59590 to 54474 for VV polarization data and 15611 to 15596 for VH polarization data. It is interesting to note that a very less number of PSC are identified in cross-polarized images (VH). A high number of PSC have identified in co-polarized (VV) images as the vertically oriented urban targets produce a double bounce, which results in a strong return towards the sensor. The velocity maps obtained using VV and VH polarizations show displacement in the range of ± 20 mm year -1 . The subsidence and the upliftment observed in the city shows a linear trend with time. It is observed that the eastern part of Bengaluru city shows more subsidence than the western part.

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