Conference Agenda

This study evaluates the classification performance of Sentinel-2 multispectral and EnMAP hyperspectral imagery across three Canadian agricultural sites. Using Random Forest with recursive feature elimination, we assess whether EnMAP’s spectral richness provides measurable improvements for operational crop mapping. Results show comparable overall accuracies, with EnMAP offering advantages for spectrally complex crop types. Findings highlight the potential and practical limitations of incorporating satellite hyperspectral data into national agricultural monitoring workflows.

Agricultural land in Slovakia has undergone a significant transformation over the past two decades, but the extent and direction of this change remain insufficiently quantified at the national level. This study provides the first spatially explicit assessment of agricultural land loss using Land Parcel Identification System (LPIS) records (2004–2022) in combination with Sentinel-2-based land cover classification. By differentiating the LPIS, we identified polygons that were lost from the agricultural land register; these polygons could represent abandoned agricultural land as well as areas converted to other uses. These polygons were classified into four land cover classes using a Random Forest model trained on Sentinel-2 spectro-temporal metrics and cleaned LUCAS 2022 samples (F1 = 0.867), with additional filtering applied to separate grasslands from shrubs based on vegetation height. The results show that more than 1,000 km² of originally suitable agricultural land has been converted to other land cover types. Forest expansion accounts for 834 km², while 298 km² has been converted to shrubland and 553 km² remains as grassland. Non-forested areas, including buildings and infrastructure, cover an area of 258 km². Only 17 km² of formerly agricultural land remained as actively utilised arable land. These findings indicate that agricultural land abandonment, ecological succession, and urbanisation are the primary causes of agricultural land loss in Slovakia. The research presented provides important data confirming that the loss of agricultural land is extensive and largely threatens habitats with high biodiversity, highlighting the urgent need to harmonise strategies across agriculture, the environment, and land-use planning.

Agricultural drought affects crop productivity and threatens food security. This study presents the Hierarchical Robust Combined Drought Index (HRCDI) for operational agricultural drought monitoring, based on Earth Observation (EO) data freely available in Google Earth Engine. The HRCDI integrates four complementary indicators—Standardized Precipitation Evapotranspiration Index (SPEI3), Soil Moisture (SM), Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI)—using a hierarchical fuzzy logic framework that reflects the progression of drought impacts from climatic anomalies to vegetation stress. To ensure robustness, monthly anomalies of SM, LST, and NDVI were computed through a robust z-score formulation based on median and NMAD, which reduces the influence of outliers. The HRCDI was applied to the Province of Foggia (southern Italy), one of the main durum wheat production areas in Italy, over the period 2017–2022. HRCDI outputs were aggregated at the municipality scale and validated against independent datasets, including durum wheat yield statistics (2006–2022), SPEI3 provided by the Institute of Services for Agricultural and Food Market (ISMEA), and reports from the European Drought Impact Database. The HRCDI effectively captured the severity and spatial extent of major drought events, particularly in 2017 and 2022, which corresponded to documented yield losses of −5% and −22%, respectively. Results highlight the scalability, operational relevance, and transferability of the HRCDI for supporting drought early warning and agricultural risk management. The HRCDI framework could be applied to other regions and integrated with higher-resolution satellite data to enhance drought monitoring in line with the objectives of the Common Agricultural Policy.

Fall Armyworm (FAW) is a major pest, threatening maize production and food security. FAW preferentially attacks nitrogen-rich maize due to its nutritional composition, which accelerates larval development. Understanding the effects of FAW infestation on maize growth and nutrient status is critical for effective crop management. This study (i) examines the impact of FAW infestation on maize using key physiological metrics, including fresh/dry weight, chlorophyll content (as measured by SPAD), leaf area index (LAI), stem length, and leaf dimensions (length, width, and area); (ii) investigates differences in carbon, hydrogen, nitrogen, and sulphur concentrations between healthy and infested maize, and (iii) analyses spectral variability between healthy and infested maize using leaf-level hyperspectral data. Hyperspectral data at the leaf scale were used to identify and distinguish the spectral reflectance between healthy and infested FAW maize crops. Results indicate that infested crops exhibit lower fresh weight, reduced LAI, shorter stems, and smaller leaves compared to healthy crops, highlighting a substantial negative effect on above-ground biomass and overall crop vigour. Infested crops showed higher nitrogen levels than healthy crops. This trend could be attributed to nitrogen redistribution following FAW damage, where nitrogen decreases in attacked leaves and increases in roots, with partial recovery after the pest moves on to another crop. The study establishes a methodological framework for linking laboratory, field, and remote sensing approaches, providing a foundation for future predictive modelling of pest impacts on maize nutrient content and productivity.