USE OF REMOTE SENSING AND GIS FOR MODELING AND MAPPING THE SOIL SALINITY OF AGRICULTURAL LAND IN EL-FAYOUM GOVERNORATE, EGYPT | ||||
| Menoufia Journal of Soil Science | ||||
| Article 3, Volume 6, Issue 4, May and June 2021, Page 161-162 PDF (337.09 K) | ||||
| Document Type: original papers | ||||
| DOI: 10.21608/mjss.2021.175598 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| F. E. Abu-Agwa1; M. S. E. Amira1; A. H. El-Nahry2 | ||||
| 1Prof. of Pedology, Fac. of Agric., Menoufia Univ. | ||||
| 2Prof. of Pedology, National Authority for Remote Sensing and Space Sciences. | ||||
| Abstract | ||||
| This study was conducted aiming to use remote sensing and GIS for modeling and mapping the soil salinity of agricultural land in El-Fayoum governorate. This included geomorphological and pedological characteristics as well as classification and land capability evaluation for the soils of the studied area. The interpretation of satellite multispectral images Landsat 8 indicated that, this area has five main geomorphic units with different landforms. These geomorphic units are: 1) Recent river terraces (High, Moderate and Low), occupies; 2) Basins (Overflow basins & Decantation basins),; 3) Old river terraces (High, moderately high, Moderate and Low),; 4) Former lake bed,; and 5) Water bodies (Fish ponds and Lake Qaroun). Thirty profiles were selected representing the soils of these units. Land and site features are observed and registered. Soil profiles were dug deep down to 150 cm or water table level, morphologically described, and samples were collected representing the vertical variations in each profile for integrated physiochemical analyses. Also, thirteen water samples were collected from the surface and groundwater resources in each landform for chemical analysis to use their properties in the land capability evaluation model and assess their quality for irrigation purpose. Moreover, forty-three surface soil samples were collected in addition to statistical regression models were developed based on remotely sensed indicators to predict and map spatial variation in soil salinity in the studied area. Most of studied soils of are deep and well drained having almost flat to gently sloping topography. Some soils that having moderately high-water table level, are moderately deep and imperfectly drained. Most of the studied soils have clay loam texture grade. All studied soils are slightly alkaline, non-to slightly saline. Some soils of old river terraces and all soils of Overflow basin as well as Former lake bed are moderately saline having sodicity effect. All studied soils have low Organic matter (OM) and gypsum contents. Most soils are moderately calcareous, and few are strongly calcareous. Statistical regression models were developed based on remotely sensed indicators to predict and map spatial variation in soil salinity in the studied area. There is a high correlation found between the Salinity indices SI5 and SI6 and estimated EC Model Validation. Most of the studied soils haven’t any diagnostic horizons and classified under Entisols order. Few soils are classified under Aridisols. The land capability evaluation indicated that, 10% from the studied soils are considered as very good (C1), 59% as good (C2), and 20% as fair (C3). The water samples taken from the Nile water canals have none sodium hazard, slight to moderate chloride and salinity hazard. Other collected water samples have severed salinity, sodicity and chloride hazards | ||||
| Keywords | ||||
| RS; GIS; geomorphic unit; soil classification; land capability evaluation; irrigation water evaluation; modeling and mapping the soil salinity | ||||
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