This study aimed to develop a new method for combining Sentinel-2 and PlanetScope imagery. The normalized difference vegetation indices (NDVI) data were retrieved from the Earth observation satellites S2 Level-2A and PS Level-3 surface reflectance (PS) during the soybean growing phase. The proposed method utilizes the pyDMS algorithm, which is a decision tree-based technique for enhancing low and high-resolution images with information from large-scale images. The robustness and flexibility of a multidimensional data fusion, deep neural network, and machine learning-based yield estimation model were analyzed based on the within-field variability in soybean yield. A comparative analysis revealed that the fusion data with 1.5 to 2.5 tons significantly outperformed individual predictions, demonstrating higher accuracy and fewer errors. The fusion data used to predict yields showed relatively small error ranges of 0.5 to 0.2 t/ha. In contrast, the PS and S2 datasets showed higher prediction errors. The study employed vegetation indices, and during validation, crop forecasts were compared using an NDVI map. The effectiveness of artificial neural networks in predicting crop yields was highlighted, demonstrating superior performance across diverse datasets compared with other algorithms. This novel fusion technique is essential for monitoring crop health and growth, improving agricultural practices, such as fertilization and water management, and improving yield forecast accuracy. This study provides valuable insights into phenology monitoring, image fusion accuracy, and the effectiveness of machine learning algorithms in predicting crop yields, emphasizing the benefits of fused imagery.

Ground penetrating radar (GPR) has been widely used to assess asphalt and pavement, especially in quality testing for newly constructed roads. However, its usage has been limited in regard to aged roads. Thus, this study focuses on the applicability of GPR to extract diverse information regarding structure, thickness, and various conditions, including the moisture content of an aged road section that has undergone repeated renewals. First, two methods were employed to calculate the thickness and dielectric values; the reflection amplitude and ground truth methods. The analysis was done by RADAN 7 software. Based on the findings, the average error of thickness on the same day between continuous GPR and the core data were 2.87% and 8.72%, respectively. Second, dielectric analysis of three structural units was performed under different moisture conditions. As a result, the average dielectric values of macadam (3, 3.3, and 4), surface asphalt layer (4, 7.06, and 8.31), and cement-treated base (4.83, 10.88, and 11.88) were determined under dry, medium-wet, and wet conditions, respectively. The volumetric water difference (f) within the pavement was also estimated. As for the asphalt, macadam, and cement-treated base, the difference in the volume fraction of water (f) was 0.06, 0.01, and 0.1, respectively, under dry and wet conditions, and 0.04, 0.004, and 0.09, respectively, under dry and medium-wet conditions. Overall, the findings demonstrate that reasonably accurate assessments of the pavement thickness, structure, dielectric values, and amplitude of aged roads can be achieved by using a GPR survey under various conditions.

Artificial levees along alluvial rivers are major components of flood-risk mitigation. This is especially true in the case of Hungary, where more than one-third of the country is threatened by floods and protected by an over 4200-km-long levee system. Most of such levees were built in the nineteenth century. Since then, several natural and anthropogenic processes, such as compaction and erosion, might have contributed to these earth structures' slow but steady deformation. Meanwhile, as relevant construction works were scarcely documented, the structure and composition of artificial levees are not well known. Therefore, the present analysis mapped structural differences, possible compositional deficiencies, and sections where elevation decrease is significant along a 40-km section of the Lower Tisza River. Investigations were conducted using real-time kinematic GPS and ground-penetrating radar (GPR). Onsite data acquisition was complemented with an analysis using a Persistent Scatterer Synthetic Aperture Radar to assess general surface deformation. GPR profiles showed several anomalies, including structural and compositional discontinuities and local features. The GPR penetration depth varied between 3 and 4 m. According to height measurements, the mean elevation of the levee crown decreased by 8 cm in 40 years. However, the elevation decrease reached up to 30 cm at some locations. Sections affected by structural anomalies, compositional changes, and increased surface subsidence are especially sensitive to floods when measurement results are compared with flood phenomena archives.

Levees are earth structures constructed along alluvial rivers and are considered to be one of the essential components of flood risk and natural hazard reduction. The preservation of their condition would require orderly monitoring. In Hungary, an over 4200 km long levee system was constructed from the 19th century on. Since then, many natural and anthropogenic processes, such as compaction, erosion, subsidence etc., could contribute to the slow but steady deformation of these structures. In the meantime , due to the lack of documentation, their structure and internal composition are still unclear in many sections. The present study uses different geophysical techniques to validate their efficiency in detecting the structure, composition and potential defects along a 3.6 km levee section of the Lower Tisza River, affected significantly by seepage and piping phenomena during floods. Measurements were made using Ground Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT) and drillings. Information obtained by the different techniques was cross-checked and combined. This way, the potential of the applied survey strategy could be demonstrated, and the selected levee section could be assessed in terms of its structure and composition. Consequently, the major reasons for frequently occurring adverse flood phenomena at the site could be revealed. The survey approach outlined in the present paper can be applied extensively along lowland levee systems in the region and elsewhere.

This paper deals with the possibilities of ground-penetrating radar (GPR)-based quality control testing, which was demonstrated on an experimental road section of a ~220–240 m long Hungarian residential street. The measurements and their assessment aimed to control the prescribed compactness and air void content of newly built asphalt layers. Research has discussed the relationship between the air void content and the dielectric constant of asphalt layers, and provided empirical results for this relationship. We suggest a new logistic model with lower and upper asymptotes instead of the exponential formula often used in the literature. Contrary to this newly developed robust model, existing models are sensitive to extreme dielectric constant values due to the mathematical nature of their exponential function. The results of the new logistic model are compared to those of the Hoegh–Dai (HD) and Minnesota Department of Transportation (MnDOT) models on the basis of a few calibration data points. Through systematic data collection and analysis, the developed robust empirical model demonstrates a significant correlation between the relative permittivity and air void content in asphalt mixes, enabling accurate estimation of the air void content within a ±0.5% margin of error. The air void content can be applied to estimate the asphalt layer modulus. The developed model can be further exploited by utilizing a combination of GPR and drone technology. The “symbiosis” of these technologies can lead to a totally non-destructive imaging system, which can then be applied to environmental monitoring of roads and their surroundings in terms of quality control of asphalt compaction work and the hot asphalt mix behind the compaction roller during pavement construction.