The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (K_c). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based K_c and NDWI, NDRE, NDVI, and LAI-based K_c were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET₀) and VI-based K_c. The weather station data of CER were used to calculate ET₀ based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Remote sensing is an efficient method of monitoring experiments rapidly and by enabling the collection of significantly more detailed data, than using only field measurements, ensuring new possibilities in scientific research. A small plot field experiment was conducted in a randomized block design with winter oat (Avena sativa L.) varieties in Debrecen, Hungary in the 2020/2021 cropping year. Multiple field measurements and aerial surveys were carried out examining the response of oat on Silicon and Sulfur foliar fertilization treatments thereby monitoring their effects on the physiology, production and stress tolerance. Parallel application of in situ (elevation, soil pH, NDVI, SPAD, chlorophyll content) and aerial (NDVI, NDRE) surveys including unmanned aerial vehicles (UAVs) provided a diverse source of data for evaluation. Both the oat varieties (88.9%) and the foliar fertilization treatments (87.5%) were correctly classified and clearly separated with the discriminant analysis based on measured data. The Pearson correlation analysis showed a very strong positive connection (r = 0.895–1.00) between the NDVI values measured using a hand-held system and UAV-installed camera, except the third measurement time, where the correlation was weaker (r = 0.70). Our results indicate that field experiments can be effectively supported by UAVs.

Climate change is an essential environmental challenge nowadays. Its effects are already being felt in multiple ways. In the future, we will also have to adapt to its effects because of our farming and our daily lives. In our research, we assessed the climate sensitivity of the lowland areas of Hungary through the changes in landscapes and the changes in groundwater resources that have the greatest impact on agriculture, using data from more than half of a century. We have quantified that at the mid-territory level (5-10 thousand km2) the groundwater resources show up to 3-5 km3/year changes in both positive and negative directions due to climatic effects. This significantly exceeds the anthropogenic water uses (the total water use of Hungary is about 5 km3 per year), so the effect of climate is the determining factor in the changes of regional water resources. Future changes in water circulation were modelled using the MIKE-SHE model in two micro-regions in Hungary. We have found that already at the level of the small catchments presented in our study, the water shortage increases by hundreds of millions of m3 per year due to the expected increase in temperature (mainly due to the increase in evapotranspiration), which cannot be compensated by current water supply solutions. Model simulations have confirmed previous results showing that groundwater movements play a very important role even in lowland landscapes. Based on our research, we would like to draw the attention of decision-makers and agricultural experts to the fact that current methods (irrigation, regional water transfers) are not sufficient for successful adaptation to climate change. So, it is not the limited precipitation but the inappropriate agricultural practices that cause a real threat in a changing climate. Based on our research, we have made a proposal for the adaptation of agriculture to climate change.

Monitoring the concentration of heavy metals in urban soils is of a paramount importance for several reasons. These inorganic pollutants can pose a significant health risk to living organisms, as they are toxic even at low concentrations and can be present in the soil for a long period of time. This study assesses the spatial distribution, concentration levels, and potential anthropogenic and natural sources of eight typical heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb and Zn) occurring in urban surface soils across Debrecen, Hungary. A total of 295 topsoil samples were collected; heavy metal concentrations were determined by energy-dispersive X-ray fluorescence (EDXRF) spectrometry. The results were interpreted using descriptive statistics, correlation analysis, hierarchical clustering, factor analysis, ordinary kriging interpolation, and spatial-discriminant analysis. The dual origin of the metal contaminants was revealed: As, Co, Pb, and Zn showed strong anthropogenic signatures associated with traffic, urban waste, and construction materials, whereas Cr and Ni were associated with natural geogenic sources. Cd reflected both lithogenic and point-source urban pollution. The current evaluation incorporated Hungarian and Dutch regulatory benchmarks to identify exceedances of environmental quality thresholds. It was found that only Cd and Cr exceeded the Hungarian target values, on average. Linear discriminant analysis based on pollution maps highlighted contamination hotspots around traffic corridors and newly industrialized zones. The importance of high-resolution soil monitoring in the rapidly urbanizing city is highlighted. Given its anticipated industrial and transportation developments, accumulations of heavy metals are probably going to be further exacerbated; therefore, the results provide a critical baseline for future environmental assessments and long-term monitoring.

Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making.

Understanding better the spatial and subsurface infiltration patterns of heavy metal distribution in urban soils can help in environmental and health risk assessment and the development of effective environmental policy. The present paper investigates the overall environmental quality of the Debrecen area in terms of heavy metal pollution and describes a simulation procedure for the spatial and statistical patterns of heavy metals in atmospheric deposited dust samples and the spatial diversity of health vulnerability. The research is based on the evaluation of 300 soil samples from Debrecen and its surroundings. Maps of topsoil major heavy metal distributions, including As, Cd, Cr, Co, Cu, Mo, Ni, Pb, and Zn, were obtained using sequential Gaussian simulation. Then, the pollution levels were determined from the stochastic maps, thus obtaining a comprehensive quantitative pollution characterization of the nine elements in the city. According to the results, the potential risk of pollutants to the environment is in the order of Cd > Mo > As > Cu > Ni > Cr > Pb > Co > Zn, with a pollution load of 0.66. Cd, Mo, and Cu are the main toxic pollutants in Debrecen topsoils, with mean concentrations of 1.9 mg/kg, 77.3 mg/kg, and 1.4 mg/kg, exceeding the threshold values by 0.9 mg/kg, 2.3 mg/kg, and 0.4 mg/kg, respectively. The stochastic models assessed the uncertainties and risks related to the spatial distribution of heavy metals in soil within the contaminated areas, and a positive correlation can be found between the increase in pollution values above the predetermined threshold limits and the corresponding increase in threshold levels. Additionally, there is a consistent decrease in the relative area of excess contamination, although the ecological pollution index (96.9) revealed the Debrecen region as a hotspot area for considerable risk of contamination that needs continuous monitoring and remediation.