The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km² test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single- and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%–77% (PROBA-V 100 m data) and 66%–70% (PROBA-V 300 m data) with four classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted. Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a “gap-filler mission”.

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.

Agricultural management produces soil compaction depending on intensity of use, specific management and soil properties. We used an adapted, home‐built dynamic penetrometer to evaluate 20 long‐term sites of arable land, tree orchards, vineyards and grassland in Europe and China, each with different tillage and cover crop strategies. To ensure comparable results across all sites, we pre‐tested different penetrometer settings in the laboratory to cover all local conditions and provided Standard Operating Procedures. The laboratory tests showed that different settings in terms of falling hammer height and cone angle produced replicable results, and that narrow plough pans (3 cm) could be detected, even though their density was underestimated by 50%. The pre‐tests also demonstrated the dependence of the measurements on soil water content and texture, even under preset conditions close to field capacity. Consequently, the effects of different management practices were only compared directly for each site individually. The field study showed that tillage had a greater effect on penetration resistance than different cover crop systems and intensity. The majority of the more intensively tilled fields showed penetration resistance that was up to 3 MPa lower, at least partially, up to the ploughing depth compared to less intensively tilled fields. However, in only 27% of the fields, the no‐till or reduced‐till management led to an SPR greater than 2.5 MPa, indicating harmful compaction, compared to conventional management. The effects of cover crops on soil compaction were unclear with differences observed between different mixtures at only one site. Nevertheless, unlike bare soil, cover crops increased penetration resistance in the tillage horizon and reduced infiltration capacity in 75% of the fields investigated. Trends depending on management practices varied due to local soil properties. We therefore recommend farmers to include penetrometer measurements in their routine to recognize soil compaction in time and apply tailored mitigation strategies.

The approach developed by us adheres to the limitation methods recommended by the FAO, and more precisely to those that take into account the number and degree of existing limitations for a certain set of soil characteristics, and is tailored to the specifics of agricultural production at the farm level. The research enables dynamic monitoring of changes in soil quality in accordance with measures taken to improve its health. This study presents a methodology that combines a limited set of soil characteristics specific to each farm. The aim is to make it easier for farmers to provide the necessary information and to make data-driven decisions, taking into consideration soil health and ensuring sustainable farm management. The suggested methodology will be used as a backbone of software that will be developed for profit optimization at the farm level. The soil features selected for work are grouped into four large groups, complying with the rule to work with the smallest possible number of characteristics that describe as fully as possible the agroecological conditions in relation to the requirements of the plants, while at the same time not allowing double and triple evaluation of the same characteristic.The degrees of restrictions (from L0 to L4) are determined for each soil feature separately using the developed scales, and based on this, the suitability for agricultural use of the farm is assessed. A comprehensive assessment and classification of farms by suitability for agriculture is carried out.