The historical Ukrainian rock-salt mining town of Solotvyno and its environmentally related problems are well-known. A complex monitoring system is needed to evaluate the current situation in order to revitalize the investigated area. In addition to other risks, surface deformation due to undermining is one of the major risks endangering building infrastructure in the inhabited area of the town. These processes are well-known in the area, and damages caused by the surface movement are often recognized. Measurement of the process’s intensity and identification of the impacted area are crucial for any revitalization work. Information on these processes is the most important element of the hazard management and spatial-developmental planning of the town. This study aimed to characterize the long-term surface deformation processes and to identify the spatial and temporal trends and changes of these processes to assist spatial planning. The first step was to understand the surface deformation history from 1992. An InSAR-based assessment of the surface displacement of the undermined Solotvyno area was performed using data from three satellites, namely the ERS, Envisat, and the Sentinel-1, covering the time period between 1992 and 2021. The derived quantitative analysis indicated an intensive surface displacement and subsidence over the mining area. However, these displacements have not been even in the last 30 years of the investigation. The identification of the stabilized areas and recently started movements indicated the dislocation of the processes, which requires adequate actions for geohazard management and strategic planning. The demonstrated technology (InSAR) has the potential to set up an appropriate alarm system and provides an automated mechanism for continuous risk detection. A complex systems development is able to significantly reduce the geohazards over the unstable built-up zones.

Soil erosion is a complex, destructive process that endangers food security in many parts of the world; thus, its investigation is a key issue. While the measurement of interrill erosion is a necessity, the methods used to carry it out vary greatly, and the comparison of the results is often difficult. The present study aimed to examine the results of two rainfall simulators, testing their sensitivity to different environmental conditions. Plot-scale nozzle type rainfall simulation experiments were conducted on the same regosol under both field and laboratory conditions to compare the dominant driving factors of runoff and soil loss. In the course of the experiments, high-intensity rainfall, various slope gradients, and different soil surface states (moisture content, roughness, and crust state) were chosen as the response parameters, and their driving factors were sought. In terms of the overall erosion process, the runoff, and soil loss properties, we found an agreement between the simulators. However, in the field (a 6 m² plot), the sediment concentration was related to the soil conditions and therefore its hydrological properties, whereas in the laboratory (a 0.5 m² plot), slope steepness and rainfall intensity were the main driving factors. This, in turn, indicates that the design of a rainfall simulator may affect the results of the research it is intended for, even if the differences occasioned by various designs may be of a low order.

Soil water content or soil moisture content is considered one of the most critical properties of the soil for crop production, irrigation, and environmental studies. The technical development of soil moisture measurement devices is swift, but calibration among field conditions is still not entirely resolved. Accurate calibration requires samples taken right next to the sensor that disturbs the site and changes the soil conditions. Real field operation requires the probe to represent larger areas that have undisturbed soils around the probe. These would describe the parcel’s general soil conditions and start providing data from the time of installation. This study aimed to compare several potential solutions for off-site calibration of an operational EnviroScan sensor (Sentek Technologies, Stepney South, Australia). The performances of the default and soil texture-specific equations provided by the manufacturer were compared with the field and laboratory calibration approaches. Two statistical parameters, coefficient of determination (R²) and root square mean error (RMSE) was used to determine logarithmic model results. The results show that the default calibration equations in all three classes have relatively low performances with RMSE values of around 10–15 and R² values ranging from 0.4 to 0.8. However, significant refinement was achieved by selecting texture-specific equations from the manufacturer’s libraries. The soil texture-specific equations of the EnviroScan often yielded quite satisfactory results, with RMSEs ranging between 2 and 4. Similar RMSE values were achieved from the laboratory calibration exercises, but the reapplication potential of these equations was often questionable due to the severely changed soil conditions of the laboratory processed soil compared to the field soil conditions.

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R²) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

Climate change is increasing the occurrence of extreme precipitation events and causing irregular precipitation patterns. This occurs in parallel with the degradation of crop fields, and triggers the occurrence of pluvial floods and droughts on the same field. Consequently, irrigation must be adapted to the changing soil properties. Detailed spatial and temporal measurements of changes in infiltration are required. This study aimed to quantify changes in infiltration for a subhumid irrigated cropland with various soil types (Phaeozem, Solonetz, Chernozem) and field conditions (seedbed and stubble) by simulating rainfall. As the soil structure determines hydrology, the aggregate stability/surface roughness was tested as a proxy of infiltration through photogrammetry. The soil losses caused by precipitation did not exhibit connections to changes in the surface roughness, and lower aggregate stability did not necessarily cause lower infiltration intensities, suggesting that sedimentation could only partly seal drainage pores. The final infiltration intensities varied within a wide range (0.2–28.4 mm h⁻¹). Seedbed preparation did not increase the volume of micropores (<10 µm), which resulted in higher infiltration under stubble. Photogrammetry was found to be a potentially useful tool for measuring aggregate stability, however, further investigations on in situ soil surfaces are required for technical improvement.