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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.

Mapping and assessment of ecosystem services in agricultural landscapes as required by the EU biodiversity policy need a better characterization of the given landscape typology according to its ecological and cultural values. Such need should be accommodated by a better discrimination of the landscape characteristics linked to the capacity of providing ecosystem services and socio-cultural benefits. Often, these key variables depend on the degree of farmland heterogeneity and landscape patterns. We employed segmentation and landscape metrics (edge density and image texture respectively), derived from a pan-European multi-temporal and multi-spectral remote sensing dataset, to generate a consistent European indicator of farmland heterogeneity, the Farmland Heterogeneity Indicator (FHI). We mapped five degrees of FHI on a wall-to-wall basis (250 m spatial resolution) over European agricultural landscapes including natural grasslands. Image texture led to a clear improvement of the indicator compared to the pure application of Edge Density, in particular to a better detection of small patches. In addition to deriving a qualitative indicator we attributed an approximate patch size to each class, allowing an indicative assessment of European field sizes. Based on CORINE land cover, we identified pastures and heterogeneous land cover classes as classes with the highest degree of FHI, while agroforestry and olive groves appeared less heterogeneous on average. We performed a verification based on a continental and regional scale, which resulted in general good agreement with independently derived data.

Agricultural production systems are sensitive to weather and climate anomalies and extremes as well as to other environmental and socio-economic adverse events. An adequate evaluation of the resilience of such systems helps to assess food security and the capacity of society to cope with the effects of global warming and the associated increase of climate extremes. Here, we propose and apply a simple indicator of resilience of annual crop production that can be estimated from crop production time series. First, we address the problem of quantifying resilience in a simplified theoretical framework, focusing on annual crops. This results in the proposal of an indicator, measured by the reciprocal of the squared coefficient of variance, which is proportional to the return period of the largest shocks that the crop production system can absorb, and which is consistent with the original ecological definition of resilience. Subsequently, we show the sensitivity of the crop resilience indicator to the level of management of the crop production system, to the frequency of extreme events as well as to simplified socio-economic impacts of the production losses. Finally, we demonstrate the practical applicability of the indicator using historical production data at national and sub-national levels for France. The results show that the value of the resilience indicator steeply increases with crop diversity until six crops are considered, and then levels off. The effect of diversity on production resilience is highest when crops are more diverse (i.e. as reflected in less well correlated production time series). In the case of France, the indicator reaches about 60% of the value that would be expected if all crop production time-series were uncorrelated., Graphical abstract Unlabelled Image, Highlights • An easy-to-compute index to estimate crop production resilience is proposed. • The proposed index quantifies the added value of crop diversity. • Estimated resilience increases with higher agro-management levels.

Freshwater ecosystems are among the most degraded and threatened globally. A need for systematic assessment of riverine habitats is thus well-documented. Riparian zones are especially important due to the large array of ecosystem and social services they can provide, while also recently gaining a major role in the new European biodiversity policy. In this context, the necessity to gather spatial information on extent, distribution and characteristics of the riparian zones is clear. This paper presents the development of a novel model to derive geographical distribution and basic characteristics of stream riparian zones in Europe, including both river-floodplain systems and the riparian networks of minor and ephemeral streams. A series of relevant descriptive attributes (water boundary, vegetation presence, land-cover, upland boundary and local geomorphology) is initially selected from existing literature to describe the presence of riparian zones. Spatially explicit estimates of these attributes are obtained using available Earth Observation data and pan-European thematic datasets. Finally, the information layers are combined through an aggregation system that assigns a degree of belonging to the riparian zone class using two fuzzy membership functions to evaluate respectively water influence and presence of natural vegetation. Riparian zones with no hydrological connection are also incorporated in by the model based on functional considerations. The distribution of stream riparian zones was derived and mapped for the whole of Europe at fine resolution. Modelled riparian zones extend for around 91,000 km2, considering the entire range of low to high membership to the riparian class. A characterization of land-cover types was derived based on Corine Land Cover 2000 data, showing that European riparian zones are strongly dominated by forest habitats. Accuracy assessment was performed using independent ecological datasets and visual validation, indicating producer accuracy is equal to 84.5%1.3% and user accuracy to 72.6%5.8% at 95% confidence level. The proposed model and output can represent valuable information for large-scale research activities of riparian environments and to support national and supra-national conservation programs., JRC.H.1-Water Resources

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.

Spatial assessment of environmental phenomena at regional/global scale involves the analysis and fusion of multiple, complex, multidisciplinary, and large-scale information. Since very often reliable models of such phenomena are lacking, the ''syndrome approach'' has been adapted to this purpose. In this context, there is a strong need for frameworks capable of handling data from heterogeneous sources in order to fuse them into synthetic indicators by modelling the uncertain and incomplete knowledge of the phenomenon. The approach here proposed models a syndrome by soft revision of bipolar information having heterogeneous role: a set of contextual conditions constraining the flourishing of the syndrome (negative information), identified on the basis of the expert's knowledge, and a typical pattern of notable symptoms (positive information) that are indeed proxies of observations of the syndrome occurrence. Specifically, three soft revision strategies are defined in the paper to combine negative and positive information whose overall aim is to define indicators of occurrence of an environmental syndrome with distinct objectives. The main concern of the first two revisions is to reduce the incoherence of bipolar information by taking into account the information reliability: the first strategy models a cautious decision attitude by eliminating incoherence and by modelling priorities of either negative or positive information and their partial trust, while the second strategy models a cautious and precautionary decision attitude giving credit to observations by taking into account False Negatives and False Positives. Finally, the third strategy models decision attitudes characterized by distinct level of risk. The proposed methodology is exemplified by a real case study whose objective is to define an indicator of the Rural Exodus syndrome for the Mediterranean region. In this case the method fuses biophysical related variables derived from Earth Observation (EO) data with geophysical and socioeconomic conditions based on the proposed strategies.

This study adopts the “syndrome approach”, originally defined by the Potsdam Institute for Climate Impact Research (PIK), ( Downing et al., 2002 ) to assess and map rural land abandonment (RLA), that occurred during the period 1990–2005 within the wider Mediterranean area. The basic idea behind the syndrome approach is to describe change processes by archetypical, dynamic, and co-evolutionary patterns of civilization–nature interactions. In the frame of the Rural Exodus Syndrome the RLA can be interpreted as the occurrence of environmental degradation through the abandonment of traditional agricultural practices. Multi-source spatial data, including biophysical-related variables mainly derived from Earth Observation as well as socio-economical GIS-based data, were used to define proxies for expected underlying processes and drivers of the mentioned syndrome. The analysis of data is rooted in the fuzzy set theory and approximate reasoning techniques which allows for the handling of uncertain and imprecise knowledge of environmental systems. Generalized Conjunction/Disjunction operators (GCD) were applied to compute intermediate indicator score maps representing the conditions that may affect the RLA, and a bipolar operator was used to combine mandatory and favouring conditions with the aim of generating a RLA indicator. The indicator expresses the detailed location and severity, or degree, of the syndrome. The Northern Mediterranean was generally found to suffer from RLA to a distinctly higher degree than the Southern Mediterranean. Reported abandonment studies from the existing literature, the European CORINE land cover map, and the Less Favoured Areas (LFA) map all supported the findings by confirming plausibility through convergence of evidence from comparisons with different types of independent information. This spatially highly-detailed results obtained may be of particular interest to policy and decision makers involved in rural development planning in the conceived areas.

For EU-Mediterranean olive groves (Olea europaea), mapped in CORINE, classes of olive farming intensities were derived from the outcome of a multi-temporal remote sensing vegetation dynamics analysis. The management intensity classes were developed in view of a differentiated accounting of olive groves when delineating High Nature Value Farmland areas (HNV) at pan-European level. The remote sensing input data used was the Green Vegetation Fraction (GVF), derived in 10-day intervals from a long-term time series of NOAA AVHRR data. The key physical parameters for the intensity assessment were obtained by parametrization of the observed annual growth cycle. These parameters represent two functional proportions in olive groves, a seasonally changing annual component and a permanent perennial vegetation component, which were interpreted to classify data in three olive farming intensity levels. 27% of the assessed EU-Mediterranean wide olive areas were found to be of very high intensity, 12% were classified as low intensity olive areas and 61% form an intermediate intensity class. Strong intensification was found in Spain, followed by Italy, Greece and Portugal. Most pronounced indications of management changes for the period 1990–2000 were observed for the Spanish provinces Jaen/Cordoba and the Italian province of Bari.

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

An enhanced long term remote sensing based data set for Green Vegetation Fraction (GVF) was created for the Mediterranean area. The dataset contains 10-day composites of GVF for the time period 1989–2005 on a scale of 0.01°, covering the Mediterranean basin. The MEDOKADS data set was employed to create mixture triangles of NDVI and surface temperature, of which three abundances, the “vegetated”, “non-vegetated” and “cold” abundance were derived. The vegetated abundance was eventually converted to GVF. Compared to NDVI, clear improvements have been made for GVF, in particular in respect to the mitigation of undesired effects of bad atmospheric conditions. GVF can be derived in an almost fully operational way, which enables it as base data for monitoring vegetation and related purposes. The data has been successfully employed in two case studies on olive farming intensity and rural land abandonment.

The paper proposes to model environmental syndromes based on a soft revision of bipolar information, namely consisting of a set of contextual conditions constraining the flourishing of the syndrome (negative information), and a typical pattern of notable symptoms (positive information) that are indeed proxies of observations of the syndrome occurrence. Three strategies are defined to revise positive information based on negative one with the objective of modeling distinct decision attitudes, both rational cautious and rational precautionary attitudes, taking into account information coherence, and emotional alarming attitude, taking into account only bits of information in order to highlight critical situations.

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

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.