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Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for improved ecosystem management, based on an accounting of changes and on monitoring of their functioning over time. In Europe, the main legislative driver behind this goal is the European Commission's Biodiversity Strategy to 2020, on the one hand aiming at halting biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as is feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%-1.19% of the study area can be attributed as actual riparian area (considering Strahler's stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558-69,128 km2. Similarly, depending on the accounting approach, the potential riparian zones cover an area about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas. © 2016 by the authors.

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for an improved ecosystem management, based on an accounting of changes and on monitoring of their functioning in time. In Europe, the main legislative driver behind this goal is the European Commission’s Biodiversity Strategy to 2020, on one hand aiming at reducing biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%–1.19% of the study area can be attributed as actual riparian area (considering Strahler’s stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558–69.128 km2. Similarly depending on the accounting approach, the potential riparian zones are accounted for about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas.

Patches of semi-natural vegetation, encompassing both woody vegetation and grassland, located within larger areas of agricultural land, are acknowledged to play an important role in biodiversity conservation and ecosystem service supply. In spite of its relevance, semi-natural vegetation in agricultural land (SENAGRI) has never been mapped at the EU spatial extent. To recover from this information lack, the original contribution of this paper is twofold. First, it delivers the first 1 km resolution continuous map of abundance of SENAGRI features at the EU scale. Second, it provides an EU region-based assessment of the correlation between the SENAGRI map and existing maps of ecosystem service supplies. The proposed mapping system adopts a convergence-of-evidence approach, whose inputs are multi-resolution geospatial information/data sources, like the 25 m resolution satellite Image 2000/Image 2006 mosaics, to be mapped by the fully automatic Spectral Rule-based preliminary Classifier (SRC), and several ancillary classification maps, like the 1 ha-resolution CORINE Land Cover (CLC) 2006 map and the 25 m resolution European Forest 2006 map. Validation of the SENAGRI output map with the Land Use and land Cover Area frame Sampling (LUCAS) 2006 reference data set reveals that the SENAGRI map accuracy is superior to those of the individual classification maps adopted as input. A region-specific analysis at the EU scale shows that: (i) correlation between SENAGRI abundance and regulating ecosystem services is significant (R2 = 0.67) and (ii) almost no correlation is found between SENAGRI abundance and provisioning ecosystem services. Finally, agricultural land in EU regions is parameterized by SENAGRI abundance, provisioning and regulating ecosystem services supply data. These EU region-specific values reveal that, within the Common Agricultural Policy (CAP), a portfolio of agro-environmental schemes is recommended to ensure agro-ecosystem sustainability, in compliance with the EU Strategy for Biodiversity to 2020 and the targets of the CAP post-2013., JRC.H.4-Monitoring Agricultural Resources

The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led to mixed phenology signals; (ii) the GHC scheme classification design, which allows for parcels of heterogeneous covers, and (iii) the low number of the training samples available from field surveys. A mapping strategy integrating EO-based phenology with vegetation height information is expected to be more effective than a purely phenology-based approach., JRC.H.1-Water Resources

Riparian land supports multiple ecosystem services that are essential for good water quality and aquatic biodiversity, providing habitat and hydrological connectivity, and retaining pollutants and sediments. Riparian land reduces sediment fluxes in the freshwater systems by trapping sediments generated on the hillslopes before they reach the stream network, and by stabilizing stream banks. The aim of this study was to assess the impact of current riparian land in reducing sediment fluxes in the stream network of the Danube River Basin. The Soil and Water Assessment Tool (SWAT) model was used to assess sediment yields across the basin and quantify sediment retention by riparian vegetation. Europe-wide spatial information on riparian land type and extent was used to set up agricultural to riparian land ratio and the streambank reach vegetation cover. SWAT sediment simulations for current conditions, i.e. including riparian land parameterization, were calibrated and validated for the period 1995–2009. The impact of riparian land was quantified by analyzing differences in mean annual specific sediment yields between scenarios without riparian land and current conditions. Sediment yield reductions and efficiency of riparian land were quantified at several spatial scales across the basin, considering hillslopes, stream order, and administrative regions. The impact of riparian filtering in reducing sediment fluxes to the stream network at the hillslope scale was always positive, with median efficiency of 50%. Efficiency was higher where incoming sediment and water yields per unit of area were larger, and in smaller HRUs (areas lower than 10 km 2 ). Sediment filtering in riparian buffers was more efficient in smaller reaches, and decreased from about 17% to 5% with Strahler’s order. Streambank protection was important locally in about 8% of reaches characterized by high stream power, where current streambank protection reduced potential sediment yields by more than 5 t/km 2 /y, and in large reaches, like in the Sava and Danube Rivers. At the Danube outlet to the Black Sea, the reduction in sediment yield attributable to current riparian land was estimated at 480 kt/y. Although riparian efficiency declined with spatial scale in terms of sediment yield reduction, filtering of sediments in riparian buffers abated in-stream sedimentation substantially. While occupying only about 2% of the Basin, current riparian land in the Danube Basin reduces sediment fluxes in river networks by about 8% regionally, and contributes to the improvement of the ecological conditions of freshwater ecosystems.

Vegetated riparian areas alongside streams are thought to be effective at intercepting and controlling chemical loads from diffuse agricultural sources entering water bodies. Based on a recently compiled European map of riparian zones and a simplified soil chemical balance model, we propose a new indicator at a continental scale. QuBES (Qualitative indicator of Buffered Emissions to Streams) allows a qualitative assessment of European rivers exposed to pesticide input. The indicator consists of normalised pesticide loads to streams computed through a simplified steady-state fate model that distinguishes various chemical groups according to physico-chemical behaviour (solubility and persistence). The retention of pollutants in the buffer zone is modelled according to buffer width and sorption properties. While the indicator may be applied for the study of a generic emission pattern and for a chemical of generic properties, we demonstrate it to the case of agricultural emissions of pesticides. Due to missing geo-spatial data of pesticide emissions, a total pesticide emission scenario is assumed. The QuBES indicator is easy to calculate and requires far less input data and parameterisation than typical chemical-specific models. At the same time, it allows mapping of (i) riparian buffer permeability, (ii) chemical runoff from soils, and (iii) the buffered load of chemicals to the stream network. When the purpose of modelling is limited to identifying chemical pollution patterns and understanding the relative importance of emissions and natural attenuation in soils and stream buffer strips, the indicator may be suggested as a screening level, cost-effective alternative to spatially distributed models of higher complexity.