Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes.

The presence of an economical solution to predict soil behaviour is essential for new construction areas. This paper aims to investigate the ultimate interpolation method for predicting the soil bearing capacity of An-Najaf city-Iraq based on field investigation information. Firstly, the engineering bearing capacity was calculated based on the in-site N-SPT values using dynamic loading for 464 boreholes with depths of 0–2 m, using the Meyerhof formula. The data then were classified and imported to the GIS program to apply the interpolation methods. Four deterministic and two geostatistical interpolation methods were applied to produce six bearing capacity maps. The statistical analyses were performed using two methods: the common cross-validation method by the coefficient of determination (R²) and root mean square error (RMSE), where the results showed that ordinary kriging (OK) is the ultimate method with the least RMSE and highest R². These results were confusing so, the backward elimination regression (BER) procedure was applied to gain the definite result. The results of BER show that among all the deterministic methods, the IDW is the optimal and most significant interpolation method. The result of geostatistical methods shows that EBK is the best method in our case than the OK method. BER also applied to all six methods and shows that IDW is the ultimate significant method. The results indicate no general ultimate interpolation method for all cases and datasets type; therefore, the statistical analyses must be performed for each case and dataset.

The unified soil classification system (USCS) first proposed by Casagrande and subsequently developed by the Army Corps of Engineers. It widely used in many building codes and books. An-Najaf city is the most important city in Iraq due to its religious and spiritual value in the Muslim world, so it is fast expanding and continuous developing city in Iraq. The data from 464 boreholes in the study area for depths of 0–26 m have been used. 13 Soil samples were collected from each borehole with 13 depths level (0–26) m with 2 m intervals. The USCS was applied to the soil samples from 13 depth levels borehole. This research aims to create a geodatabase for soil properties for An-Najaf. The ArcGIS 10.5 software was used to interpolate the spatial data to produce 33 geotechnical maps for fine soil, coarse soil and USCS for 13 depth levels. For numerical soil data, Ordinary Kriging has been used for interpolation mapping of Fine and Coarse percentage data for each depth. For non-numerical (nominal) soil data (USCS class), the Indicator Kriging method is used. The results show that the coarse soil occupied 85–95% for depth 0–16 m and consist of (SP, SP-SM, SM) while fine soil occupied 5–15% consisting of (OL, CH, ML) subsequently, this soil when compacted has a permeability of pervious to semi impervious, good shearing strength, low to very low compressibility and acceptable workability as a construction material. The results also show that after 16 m depths until 26 m, the fine soil percentage increased to 40% with a coarse soil percentage of 60%, indicating changes in soil characteristics as the permeability became semi-pervious to impervious, fair shearing strength, medium compressibility and fair workability as a construction material. The study results will provide help and saving time, efforts and money in preliminary engineering designs.

Groundwater is an important resource that can be used for various purposes. Various factors can change the chemistry of the GW, such as the chemical composition of an aquifer as well as the leaching of human waste into groundwater. The study area is a barren land covered by some sabkhas, in addition to some agricultural fields. The study aims to assess groundwater quality for drinking purposes using the Water Quality Index. The groundwater is chemically heterogeneous and has a wide quality range from very poor to excellent. Evaporation appears to be the controlling factor among the other shallow waters, while relatively deep water is related to rock-soil dominance. Rocks, land use and land cover have helped control the groundwater quality. Moreover, the heavy use of fertilizers, pesticides and irrigation, in addition to the presence of sabkhas, contributed to the deterioration of the groundwater quality. The water-rock interaction and evaporation are the dominant mechanisms that are controlling the groundwater quality in the study area.