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Thorough knowledge of soil lithology and its properties are of considerable importance to agriculture. These parameters have a direct impact on water permeability and the content of the water in soil, which represent significant factors in crop yield, decisively determining the design of irrigation systems and farming processes. In the framework of this study, and considering the inevitable impacts of climate change, the rational management of water resources and the optimization of irrigation through innovative technologies become of significant importance. Thus, we propose an interdisciplinary approach based on robust techniques from the allied fields of earth (geological mapping, geophysical methods) and soil sciences (sampling, mechanical analysis) assisted by statistics and GIS techniques. Clay or the sum of clay and silt soil content is successfully determined from the normalized chargeability using induced polarization and electrical resistivity techniques. Finally, we distinguished three classes (S1, S2 and S3) considering the clay or the sum of clay and silt soil content in the study area (a) based on the dry period geophysical data and (b) using as classification criterion the spatial distribution of the geological formations.

Sustainable agriculture management typically requires detailed characterization of physical, chemical, and biological aspects of soil properties. These properties are essential for agriculture and should be determined before any decision for crop type selection and cultivation practices. Moreover, the implementation of soil characterization at the beginning could avoid unsustainable soil management that might lead to gradual soil degradation. This is the only way to develop appropriate agricultural practices that will ensure the necessary soil treatment in an accurate and targeted way. Remote sensing and geophysical surveys have great opportunities to characterize agronomic soil attributes non-invasively and efficiently from point to field scale. Remote sensing can provide information about the soil surface (or even a few centimeters below), while near-surface geophysics can characterize the subsoil. Results from the methods mentioned above can be used as an input model for soil and/or soil/water interaction modeling. The soil modeling can offer a better explanation of complex physicochemical processes in the vadose zone. Considering their potential to support sustainable agriculture in the future, this paper aims to explore different methods and approaches, such as the applications of remote sensing, geophysics, and modeling in soil studies.

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.

This work demonstrates the potential of Fe-modified biochar for the treatment of arsenic (As) simulated wastewater and the monitoring of adsorption in real-time. Specifically, we propose the utilization of date-palm leaves for the production of biochar, further modified with Fe in order to improve its adsorption function against inorganic pollutants, such as As. Both the original biochar and the Fe-modified biochar were used for adsorption of As in laboratory batch and column experiments. The monitoring of the biochar(s) performance and As treatment was also enhanced by using the spectral induced polarization (SIP) method, offering real-time monitoring, in addition to standard chemical monitoring. Both the original and the Fe-modified biochar achieved high removal rates with Fe-modified biochar achieving up to 98% removal of As compared to the 17% by sand only (control). In addition, a correlation was found between post-adsorption measurements and SIP measurements.

The electrical resistivity tomography method has been widely used in geophysics for many purposes such as determining geological structures, water movement, saltwater intrusion, and tectonic regime modeling. Karstic springs are important for water basin management since the karst systems are highly complex and vulnerable to exploitation and contamination. An accurate geophysical model of the subsurface is needed to reveal the spring structure. In this study, several karst springs in the Gökova Bay (SW, Turkey) were investigated to create a 3D subsurface model of the nearby karstic cavities utilizing electrical resistivity measurements. For this approach, 2D resistivity profiles were acquired and interpreted. Stratigraphically, colluvium, conglomerate, and dolomitic-limestone units were located in the field. The resistivity values of these formations were determined considering both the literature and field survey. Then, 2D profiles were interpolated to create a 3D resistivity model of the study area. Medium-large sized cavities were identified as well as their locations relative to the springs. The measured resistivities were also correlated with the corresponding geological units. The results were then used to construct a 3D model that aids to reveal the cavity geometry in the subsurface. Additionally, several faults are detected and their effect on the cavities is interpreted.