Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

Abstract

Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.

Analysing the capability of a catchment's spectral signature to regionalize hydrological parameters

“…In addition, for a better characterization of the watersheds, spectral‐derived metrics (vegetation, water, and soil indices) and texture metrics derived from the grey‐level cooccurrence matrix (GLCM) were also used (Haralick & Shanmugam, 1973). These metrics have been proven to be very useful in previous works related to land cover (Fragoso‐Campón et al, 2021) and lithological analysis (Lu et al, 2021; Radford et al, 2018). A complete description of these metrics is shown in Table SI.2.…”

“…Hence, the reflectance captured by the optical sensor is related to the surface mineralogy, depending on the composition of the soil of the geological formations of the area (Rajendran & Nasir, 2021). Moreover, the response of synthetic aperture radar (SAR) backscatter intensity is affected by surface roughness, soil moisture content (Purinton & Bookhagen, 2020), dielectric constant and grain size (Lu et al, 2021). Consequently, considering that the abovementioned characteristics are directly involved in the hydrological response of the territory, the spectral response of a catchment can be related to its hydrological behaviour.…”

Site Suitability Analysis for Landfill in an Industrial Area in Nigeria

“…Sentinel imagery was processed using digital image processing techniques with a view to increase the pictorial quality of the image and to clearly identify various landcover in the study area (Braun, 2020;Muhammed, 2020;Wu et al, 2020;Lu 2021 et al). The study area map was clipped out from the pre-processed Sentinel-2A, which has been set to World Geodetic Survey (WGS) 1984, Universal Transverse Mercator (UTM) Zone 31N.…”

An integrated study of Landsat ETM and Cartosat DEM data in identification of banded iron formations (BIFs) associated with sulphide mineralization

“…Also, rapid remote sensing data collection is possible at regular and frequent intervals. These developments have made it possible to identify and map different geological units and even alteration zones (Mohamed, 2021;Yi et al, 2021). Furthermore, due to rapid advances in sensor technology, remote sensing applications in the field of rock identification and, thus, in geological mapping, using spectral signatures characteristic of minerals constituting a rock or an ore have increased significantly.…”