## 2 Materials and Methods
### Microwave Data  
Radar data from both Sentinel-1 and ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) sensors were used. The Sentinel-1 system is a radar satellite constellation consisting of two C-band synthetic aperture radar sensors, Sentinel-1A (launched in April 2014) and Sentinel-1B (launched in April 2016), operating at 5.405 GHz, and dual-polarization support (HH+HV, VV+VH). Both satellites fly in the same near-polar, sun-synchronous orbit [(22)]. The Sentinel-1 image used in this study was acquired on 7 January 2015 in a descending orbit with an interferometric wide swath mode (IW), a large swath width of 250 km at ground resolutions of 5 m \(\times\) 20 m, and dual polarizations of VV+VH.  
An ascending ALOS PALSAR, Level 1.5 (geo-referenced amplitude image) data product, along with a radiometrically terrain-corrected (RTC) data product, was utilized in this study. The PALSAR scene with a multi-look complex (MLC) and geo-referenced to UTM coordinate data, was acquired in 13 July 2008 in a fine beam mode (FBD). It operates at 1.27 GHz (L-Band), and dual-polarization support of HH and HV at a spatial resolution of 12.5 m.  
The process began by applying a precise orbit to the radar data and conducting image radiometric calibration to obtain backscattering values. The radar images were then speckle filtered using a Lee Sigma algorithm with a 5 \(\times\) 5 pixel window. Terrain geocoding was then implemented using the SRTM 30 m DEM, along with the ALOS orbit data. Ortho images were projected to UTM coordinates, zone 35. A multi-looking function was then applied to generate accurate pixel dimensions (to produce square pixels out of rectangular SAR pixels). These processing sequences generated a geo-coded, orthorectified, terrain-corrected, radiometrically-calibrated and normalized Sentinel-1 and PALSAR scenes with a pixel size of 12.5 m.  
A LS8 and PALSAR image fusion, using the Gram Schmid algorithm [(23)], along with image transformation, using the minimum noise fraction (MNF) approach [(24)] were, therefore, applied to enhance the visual interpretability of the structure morphology. LS8 was fused together with the PALSAR L-HH imagery to produce a hybrid image that combines information of spectral properties (from the LS8) and surface roughness (from the PALSAR image) to improve the visual interpretation of the structural features and drainage systems in the surrounding area. The minimum noise fraction(MNF), a principal component transformation algorithm [24], was applied on a stacked multispectral LS8 and PALSAR microwave image to eliminate data redundancy and enhance the surficial mapping of the Rimaal structure. In the MNF procedure, the first phase results in transformed data in which the noise has unit variance and no band-to-band correlations, whereas the second phase is a standard PC transformation of the noise-whitened data, which gives rise to final outputs that are not correlated and are arranged in terms of decreasing information content [24]. The first three MNF components were used in the image analysis as they explain most of the variance in the scene (96.2%). The remaining components were discarded from the analysis since they contain proportion of noise contents. This procedure generated a hybrid MNF image that contains spectral and texture information and, thus, compensates for the limitation of using single remote sensing data products alone.