## 1 Introduction  
Conventional lithological mapping is time-consuming, costly, and limited due to the practicalities of hilly terrains. Remote sensing (RS) can be used as a cost and time-effective means for lithological mapping of mineral deposits [1; 2; 3]. The suitability of data for lithological mapping depends on the rock type in question, data availability, and spatial, spectral, and radiometric resolutions. The Landsat-8 Operational Land Imager (OLI) consists of 6 visible and near-infrared (VNIR), two shortwave infrared (SWIR), and one panchromatic sensors with 30 m, 30 m, and 15 m resolutions, respectively. The Landsat-8 Thermal Infrared Sensor (TIRS) has two TIR bands of a 100 m spatial resolution available at the 30 m resolution in Google Earth Engine (GEE). In comparison, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has 14 bands, i.e., 3 VNIR bands with a 15 m resolution, 6 SWIR bands with a 30 m resolution, and 5 TIR bands with a 90 m resolution [4]. However, the radiometric response of SWIR bands from the ASTER is unreliable after 2008 due to a technical fault [5]. The Sentinel-2 Multispectral Instrument (MSI) has a relatively higher (20 m) spatial resolution for SWIR bands, but no TIR bands [6; 7]; therefore, further studies are needed to explore its capabilities for mineral exploration [8]. The Sentinel-2 MSI has a resolution of 10-20 m, but it lacks TIR bands, whereas the Landsat-8 TIRs has TIR bands, but its resolution is 100 m. However, GEE can convert bands to the exact spatial resolution by resampling, e.g., 100 m TIR bands are available as 30 m by default in its database. The TIR emissivity for igneous rocks decreases with the high silica content, while a corresponding shift in absorption to a higher wavelength band is observed in the mineral groups due to types of dominant silicates [4]. The minimum TIR emissivity of granite at 9 m is due to the vibration absorption of the Si-O silicate structure, which shifts to 11 m in mafic rocks having a tectosilicate structure [4; 9; 10]. However, TIR satellite sensors have low temporal, spatial, and spectral resolutions, and the spectral responses in these bands may contain a mixture of features [11]. Vegetation or soil cover may cause high variation in granite spectral response, making it challenging to map the deposits [4; 12]. High-spatial-resolution SWIR bands show spectral absorption for different granite compositions (e.g., muscovite and epidote) and calcite in 2.2 m regions; a complete absorption is only observed for calcite near the 2.3 m regions [4; 10]. The fusion of TIR with other high-resolution SWIR datasets can provide insight into understanding different features [4; 11; 13]. Integrating results from multiple sources, e.g., the Landsat-8, the Sentinel-2, and the ASTER, by a suitable criterion would generally improve the outcome compared to single-source data [14]. Analytical techniques for RS are grouped into data-driven and knowledge-based techniques [15]. Several techniques, such as band ratios [16; 17], band ratio matrix transformation (BRMT) [18], spectral angle mappers (SAM) [19; 20], decorrelation stretching (DS) [17; 21], principal components analysis (PCA) [17; 22; 23], directed principal component analysis (DPCA) [24], feature-oriented principal components selection (FPCS) also known as Crosta technique [25; 26; 27], spatially weighted PCA [28; 29], directed PCA [30; 31], and other variants of PCA [32], have been reported for mapping different lithological [33] and environmental [34] features. Recently, Support Vector Machine (SVM), PCA, and independent component analysis (ICA) have been used to map gold-bearing granite-greenstone rocks using AVIRIS-NG hyperspectral and ASTER data [35]. This research presents a fusion strategy of principal components (PCs), representing effective but low-resolution TIR bands loading with relatively less responsive but high-spatial-resolution Sentinel data.