Chitosan has become increasingly applied in agriculture worldwide, thus entering the soil environment. We hypothesized that chitosan should affect the water stability of soil. Since this problem has not been studied to date, we examined, for the first time, the influence of chitosan on the water stability and wettability of soil aggregates. The aggregates were prepared from four soils with various properties amended with different amounts of two kinds of powdered chitosan, and subjected to 1 and/or 10 wetting–drying cycles. The water stability was measured by monitoring air bubbling after aggregate immersion in water, and the wettability was measured by a water drop penetration test. The biopolymer with a lower molecular mass, lower viscosity, and higher degree of deacetylation was more effective in increasing the water stability of the soil than the biopolymer with a higher molecular mass, higher viscosity, and lower deacetylation degree. After a single wetting-drying cycle, the water stability of the soil aggregates containing chitosan with a higher molecular mass was generally lower than that of the soil; after ten wetting–drying cycles, the water stability increased 1.5 to 20 times depending on the soil. The addition of low-molecular-mass chitosan after a single wetting-drying cycle caused the water stability to become one to two hundred times higher than that of the soil. A trial to find out which soil properties (pH, C and N content, bulk density, porosity, and particle size distribution) are responsible for the effectiveness of chitosan action was not successful, and this will be the objective of further studies.

Due to the harmful effects of nanoparticles in the environment, their effective removal from aqueous media is of great importance. This paper described the research on the silver nanoparticles (Ag-NPs) sorption on biochars obtained from different feedstock types. The sorbents were produced through pyrolysis (double-barrel method) of the vineyard (BV), paulownia tree (BP), and tobacco (BT). BV exhibited the highest specific surface area, porosity, value of variable surface charge, and content of surface acidic functional groups among the used biochars. The pseudo-second order model best described the obtained adsorption kinetics, whereas the Freundlich model accounted for the registered adsorption data. The Ag-NPs removal was highly efficient in the case of BV, especially in the nanoparticle concentration range 50–500 mg/L. Thus, this biochar can be considered as an ecofriendly, effective, low-cost organic adsorbent, potentially used in the aqueous media purification.

The heterogeneity of soil physical properties of Fluvisols, lack of large pristine areas, and different moisture regimes on non-flooded and flooded terraces impede the possibility to find a soil profile which can serve as a baseline for estimating the impact of natural or anthropogenic factors on soil evolution. The aim of this study is to compare the pore size distribution of pristine Fluvisols on flooded and non-flooded terraces using the method of the soil water retention curve, mercury intrusion porosimetry, nitrogen adsorption isotherms, and water vapour sorption. The pore size distribution of humic horizons of pristine Fluvisols on the non-flooded terrace differs from pore size distribution of Fluvisols on the flooded terrace. The peaks of textural and structural pores are higher in the humic horizons under more humid conditions. The structural characteristics of subsoil horizons depend on soil texture and evolution stage. The peaks of textural pores at about 1 mm diminish with lowering of the soil organic content. Structureless horizons are characterized by uni-modal pore size distribution. Although the content of structural pores of the subsoil horizons of Fluvisols on the non-flooded terrace is low, these pores are represented by biopores, as the coefficient of filtration is moderately high. The difference between non-flooded and flooded profiles is well expressed by the available water storage, volume and mean radius of pores, obtained by mercury intrusion porosimetry and water desorption, which are higher in the surface horizons of frequently flooded Fluvisols.

This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring.