• Energy Research
• 6. Clean water close

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

This study focuses on the use of radar and optical satellite imagery for flood hazard mapping and monitoring around the archaeological sites of the Wadi Baba area, situated at Sinai (Egypt) and well known for its heritage treasures belonging to diverse historical periods and civilizations from the Pharaonic, Nabateans, Christian, and Islamic eras. Although this area is located in an arid to semi-arid climatic region, it is intermittently flooded due to torrential rainstorms. To assess the amount of rainfall expected and its impacts on heritage sites, satellite Sentinel-1 (C-Band) and Tropical Rainfall Monitoring Mission (TRMM) data were jointly used with measurements from meteorological stations and the Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM). Envi5.1, ArcGIS 10.4.1, Snap 6.0, and the GEE platform were used to process optical and radar data, which were further analysed using the ArcHydro model. In this study, the TRMM accumulated rainfall data acquired on 17 January 2010, Sentinel-1 radar images between 2017 and 2019, and Sentinel-1 data captured from 1 to 30 March 2020 processed by GEE platform were chosen to assess the effects of flood events on the archaeological sites in the study area. The results indicated that the study area is exposed to flood risk that significantly threatens these heritage sites. Based on that, mitigation strategies were devised and recommended to mitigate the flood hazard impact around the archaeological areas.