2018
DOI: 10.3390/rs10060880
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Rimaal: A Sand Buried Structure of Possible Impact Origin in the Sahara: Optical and Radar Remote Sensing Investigation

Abstract: This work communicates the discovery of a sandy buried 10.5 km diameter near-circular structure in the eastern part of the Great Sahara in North Africa. Rimaal, meaning "sand" in Arabic, is given as the name for this structure since it is largely concealed beneath the Sahara Aeolian sand. Remote sensing image fusion and transformation of multispectral data (from Landsat-8) and synthetic aperture radar (from Sentinel-1 and ALOS PALSAR), of dual wavelengths (C and L-bands) and multi-polarization (HV, VV, HH, and… Show more

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Cited by 6 publications
(5 citation statements)
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“…The soil texture properties along the main wadi channel bed were mapped using PALSAR radar imagery ( Figure 3f). Unlike multispectral satellite imagery, radar data provides surface roughness (grain size relative to the radar wavelength) that can be used to distinguish fine-grained from coarse-grained material [13,14,25]. Radar backscatter coefficient values extracted from radar data were correlated and checked in the field, confirming that radar wavelengths produced similar textural classes that correspond to specific grain or fragment sizes of alluvial deposits in dry lands [25].…”
Section: Hydrologic Modeling and Generation Of Hydrographsmentioning
confidence: 88%
See 1 more Smart Citation
“…The soil texture properties along the main wadi channel bed were mapped using PALSAR radar imagery ( Figure 3f). Unlike multispectral satellite imagery, radar data provides surface roughness (grain size relative to the radar wavelength) that can be used to distinguish fine-grained from coarse-grained material [13,14,25]. Radar backscatter coefficient values extracted from radar data were correlated and checked in the field, confirming that radar wavelengths produced similar textural classes that correspond to specific grain or fragment sizes of alluvial deposits in dry lands [25].…”
Section: Hydrologic Modeling and Generation Of Hydrographsmentioning
confidence: 88%
“…Important surface topology variables such as stream network, catchment area, and surface gradient can be derived from a digital elevation model (DEM). Land use and land cover characteristics can be derived from the classification of multispectral satellite imagery, whereas surface lithology and soil texture properties can be extracted from a fusion of multispectral and spaceborne radar images [13,14]. The ultimate goal of this work is to integrate remote sensing and GIS techniques, along with hydrological and hydraulic models, to explore the flood response of Wadi El-Ambagi to intense, short-lived precipitation events.…”
Section: Introductionmentioning
confidence: 99%
“…In this context, radar waves strip away the surface sand layer and expose previously unidentified buried channels. The penetration capability of radar waves in the hyper-arid regions of North Africa is well documented 4,[34][35][36][37] . The penetration depth varies according to the radar wavelength used at the time of imaging.…”
Section: Satellite Remote Sensing and Historical Mapsmentioning
confidence: 99%
“…Topographic data represents a primary tool in investigating surface landforms and geomorphological change both spatially and temporally. This data is vital in mapping past river systems due to its ability to show subtle variations in landform morphology 37 . In low lying areas, such as the Nile floodplain, detailed elevation data can detect abandoned channels, fossilized natural levees, river meander scars and former islands, which are all crucial elements for reconstructing the ancient Nile hydrological network.…”
Section: Satellite Remote Sensing and Historical Mapsmentioning
confidence: 99%
“…A high drainage density, particularly of those structurally controlled channels, is favorable for geothermal energy availability. The drainage network was delineated based on a procedure by [53], using the widely used 8D flow direction algorithm [54][55][56]. The stream network was generated using a threshold of 200 cells and matched with those visible in Google Earth Pro imagery for verification.…”
Section: Digital Elevation Model (Dem)mentioning
confidence: 99%