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.

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.

Soil structure has an important role in storing and transporting substances, providing natural habitats for soil microorganisms, and allowing chemical reactions in the soil. A complex investigation on factors affecting soil structure characteristics under herbaceous (H), deciduous (D), mixed (M), and coniferous (SP—Scots Pine and NS—Norway Spruce) vegetation was conducted at three experimental stations—Gabra, Govedartsi, and Igralishte, located correspondingly in the Lozenska, Rila, and Maleshevska Mountains in South-West Bulgaria. The data set obtained includes soil structure indicators and physical, physicochemical, chemical, mineralogical, and microbiological parameters of the A and AC horizons of 11 soil profiles. Under different vegetation conditions, soil structure indicators respond differently depending on climatic conditions and basic soil properties. Regarding the plant available water capacity (PAWC), air capacity (AC), and water-stable aggregates (WSAs), the surface soil layers have an optimal structure in Gabra (H, D), Govedartsi (H, SP, NS), and Igralishte (H). The values for the relative field capacity (RFC < 0.6) showed that the studied soils were water-limited. The WSAs correlated with SOC in Gabra, while in Govedartsi and Igralishte, the WSAs correlated with the β-glucosidase known to hydrolyze organic carbon compounds in soil. The information obtained is important for soil quality monitoring under climatic and anthropogenic changes.