Lineament extraction and structural mapping using Landsat-9 OLI and Sentinel-1 SAR data in the Proterozoic North Singhbhum Mobile Belt, Eastern India

• Integrated remote sensing datasets with field investigations for structural analysis. • Generated detailed lineament maps using automatic and manual extraction methods. • Identified two previously unreported sheared rock locations during fieldwork. The present study explores the application of Landsat-9 OLI and Sentinel-1 SAR data for effective lineament extraction and structural mapping in the Proterozoic North Singhbhum Mobile Belt, Eastern India, an area characterized by complex geological and significant tectonic history. The study employs a combination of manual and automated lineament extraction techniques, with principal component analysis (PCA) applied to Landsat 9 OLI imagery to enhance the visibility of underlying geological structures. The analysis revealed that PC1 captured the highest accuracy for lineament extraction. Sentinel-1 SAR data, acquired in Interferometric Wide (IW) swath mode with dual polarization (VV and VH), was processed using Google Earth Engine, allowing for detailed analysis of surface features. The VH polarization was particularly effective in detecting fine-scale linear features due to its sensitivity to surface roughness and vegetation penetration. Field data collection was integral to the research, allowing for the validation of remote sensing results and the construction of a detailed regional structural map. Stereographic projections were utilized to analyse and visualize structural orientations, including schistosity, lineations, shear foliations and fold axes, providing insights into the area's tectonic evolution. The comparative analysis of lineament characteristics from Landsat-9 OLI and Sentinel-1 SAR datasets indicated that while Sentinel-1 detected a greater number of shorter lineaments, Landsat-9 provided longer lineaments with distinct orientations. This study underscores the efficacy of integrating remote sensing data with field observations and advanced analytical tools, contributing to a deeper understanding of the geological framework.