This research deals with the characterization of areas associated with flash floods and erosion caused by severe rainfall storm and sediment transport and accumulation using topographic attributes and profiles, spectral indices (SI), and principal component analysis (PCA). To achieve our objectives, topographic attributes and profiles were retrieved from ASTER-V2 DEM. PCA and nine SI were derived from two Landsat-OLI images acquired before and after the flood-storm. The images data were atmospherically corrected, sensor radiometric drift calibrated, and geometric and topographic distortions rectified. For validation purposes, the acquired photos during the flood-storm, lithological and geological maps were used. The analysis of approximately 100 colour composite combinations in the RGB system permitted the selection of two combinations due to their potential for characterizing soil erosion classes and sediment accumulation. The first considers the "Intensity, NDWI and NMDI", while the second associates form index (FI), brightness index (BI) and NDWI. These two combinations provide very good separating power between different levels of soil erosion and degradation. Moreover, the derived erosion risk and sediment accumulation map based on the selected spectral indices segmentation and topographic attributes and profiles illustrated the tendency of water accumulation in the landscape, and highlighted areas prone to both fast moving and pooling water. In addition, it demonstrated that the rainfall, the topographic morphology and the lithology are the major contributing factors for flash flooding, catastrophic inundation, and erosion risk in the study area. The runoff-water power delivers vulnerable topsoil and contributes strongly to the erosion process, and then transports soil material and sediment to the plain areas through waterpower and gravity.

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity 24validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

The aim of this research is to map the salt-affected soil in an arid environment using an advanced semi-empirical predictive model, Operational Land Imager (OLI) data, a digital elevation model (DEM), field soil sampling, and laboratory and statistical analyses. To achieve our objectives, the OLI data were atmospherically corrected, radiometric sensor drift was calibrated, and distortions of topography and geometry were corrected using a DEM. Then, the soil salinity map was derived using a semi-empirical predictive model based on the Soil Salinity and Sodicity Index-2 (SSSI-2). The vegetation cover map was extracted from the Transformed Difference Vegetation Index (TDVI). In addition, accurate DEM of 5-m pixels was used to derive topographic attributes (elevation and slope). Visual comparisons and statistical validation of the semi-empirical model using ground truth were undertaken in order to test its capability in an arid environment for moderate and strong salinity mapping. To accomplish this step, fieldwork was organized and 120 soil samples were collected with various degrees of salinity, including non-saline soil samples. Each one was automatically labeled using a digital camera and an accurate global positioning system (GPS) survey (σ ≤ ± 30 cm) connected in real time to the geographic information system (GIS) database. Subsequently, in the laboratory, the major exchangeable cations (Ca 2+ , Mg 2+ , Na + , K + , Cl − and 2 4 SO −), pH and the electrical conductivity (EC-Lab) were extracted from a saturated soil paste, as well as the sodium adsorption ratio (SAR) being calculated. The EC-Lab , which is generally accepted as the most effective method for soil salinity quantification was used for statistical analysis and validation purposes. The obtained results demonstrated a very good conformity between the derived soil salinity map from OLI data and the ground truth, highlighting six major salinity classes: Extreme, very high, high, moderate, low and non-saline.

This research compares the potential of SRTM-V4.1 and ASTER-V2.1 with 30-m pixel size to derive topographic attributes (elevation, slopes, aspects, and flow accumulation) and hydrologic indices such as STI (sediment transport index), CTI (compound topographic index) and SPI (stream power index) to detect areas associated with flash floods caused by rainfall storms and sediment accumulation. The study area is Guelmim city in Morocco, which has been flooded several times over the past 50 years, and which was declared a "disaster area" in December 2014 after violent rainfall storms killed 46 people and caused significant damage to the infrastructure. The obtained results indicate that the SRTM DEM performs better than ASTER in terms of micro-topography, hydrologic-network and structural information characterization. In addition, with reference to a topographic contours map (1:50000), the derived global height surfaces accuracies are ±3.15 m and ±9.17 m for SRTM and ASTER, respectively. These accuracies are significantly influenced by topography; errors are larger (SRTM = 11.34 m, ASTER = 19.20 m) for high altitude terrain with strong slopes, while they are smaller (SRTM = 1.92 m, ASTER = 3.76 m) in the low to medium-relief areas with indulgent slopes. Moreover, all the considered hydrological indices are significantly characterized with SRTM compared to ASTER. They demonstrated that the rainfall and the topographic morphology are the major contributing factors in flash flooding and catastrophic inundation in this area. The runoff waterpower delivers vulnerable topsoil and contributes strongly to the erosion and transport of soil material and sediment to the plain areas through waterpower and gravity. Likewise, the role of the lithology associated with the terrain morphology is decisive in the erosion risk and land degradation in this region.

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