Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

Abstract

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

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

“…1st element of polarimetric C2 matrix; -The 1st and 4th elements (i.e., C11 and C22) refer to ensemble averaging of first (co-pol VV) and second channel (cross-pol VH), which represents the modulus (amplitude). The 2nd and 3rd elements (i.e., C12imag and C12real) contain the information about the real and imagery part of the channels (Erten, 2012;Nielsen et al, 2017;Mandal et al, The prediction datasets included (i) SAR parameters only (after this θ Nov SAR ) and (ii) the combination of SAR and terrain parameters (after this θ Nov SAR+Terrain ). Since 70-30% splitting for training/testing did not prove successful because of the limited number of point-observations, we decided for k-fold cross-validation with k = 10, splitting the randomly shuffled data into 10 complementary subsets.…”

Flood Inundation Mapping of Cempaka Tropical Cyclone Effect in Gunung Sewu Karst Landscape Using Sentinel 1

“…First, the subset stage is achieved by more precisely cutting satellite imagery in the study area. This stage aims to minimize the data size so that that processing can be accelerated at a later level [31]. Second, to change the spatial resolution from 10 m to 15 m, multi-looking is applied.…”

Sentinel-1 Polarimetry to Map Apple Orchard Damage after a Storm

“…The available S1 IW SLC image was processed to compute the polarimetric decomposition parameters. The adopted workflow is shown in Figure 4 and proposed by [65]. The target polarimetric analysis is ordinarily performed starting from the coherency matrix [66,67] or from the 2×2 covariance matrix (C 2 ).…”