JRC MARS Bulletin: Crop monitoring in Europe: February 2018

Winter conditions so far present no threat to winter cropsJRC.D.5-Food Securit

JRC MARS Bulletin - Crop monitoring in Europe, November 2018

Harvesting of root and tuber crops also affected In large parts of central Europe, persistently dry soil conditions, complicated field preparations and sowing operations, and limited plant emergence and early crop development. Rapeseed areas in Germany, eastern Poland and northern Czechia are expected to be significantly reduced. Soft wheat can still be (re)sown in some countries. Favourable conditions for the sowing and emergence of winter crops prevailed in most parts of western and northern Europe.JRC.D.5-Food Securit

Observing Post-Fire Vegetation Regeneration Dynamics Exploiting High-Resolution Sentinel-2 Data

Information related to the impact of wildfire disturbances on ecosystems is of paramount interest to account for environmental loss, to plan strategies for facilitating ecosystem restoration, and to monitor the dynamics of vegetation restoration. Phenological metrics can represent a good candidate to monitor and quantify vegetation recovery after natural hazards like wildfire disturbances. Satellite observations have been demonstrated to be a suitable tool for wildfire disturbed areas monitoring, allowing both the identification of burned areas and the monitoring of vegetation recovery. This research study aims to identify post-fire vegetation restoration dynamics for the area surrounding Naples (Italy), affected by severe wildfires events in 2017. Sentinel-2 satellite data were used to extract phenological metrics from the estimated Leaf Area Index (LAI) and to relate such metrics to environmental variables in order to evaluate the vegetation restoration and landslide susceptibility for different land use classes

JRC MARS Bulletin global outlook 2019: Crop monitoring European neighbourhood: Morocco, Algeria, Tunisia, Libya and Egypt: June 2019

The cereal campaign in North Africa was characterised by negative rainfall supply and distribution in the west, ranging to positive conditions in the east. Morocco was clearly impacted by drought conditions in the regions of Tensif, Centre and Oriental. Some regions in western Algeria were also impacted, but the unfavourable conditions were more than compensated by good conditions in eastern Algeria. Finally, crops in Tunisia, Libya and Egypt had a good to very good season.JRC.D.5-Food Securit

JRC MARS Bulletin global outlook 2019: Crop monitoring European neighbourhood: Turkey: September 2019

All crops are forecast above the five-year average, as well as above last year’s yields, following a mostly favourable season.JRC.D.5-Food Securit

JRC MARS Bulletin global outlook 2019: Crop monitoring European neighbourhood: Morocco, Algeria, Tunisia, Libya and Egypt: April 2019

The season started with a good supply of rain during autumn and the first vegetative phases, but this was followed by more dry conditions especially in Morocco where conditions need to be closely monitored. A considerable rain surplus has been recorded in parts of Algeria and Tunisia. Two regions are clearly impacted by somewhat drier conditions with negative consequences on barley production.JRC.D.5-Food Securit

Caractérisation des trajectoires d'usage des sols agricoles en Camargue (France)

International audienc

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates

“Contextualized VGI” Creation and Management to Cope with Uncertainty and Imprecision

This paper investigates the causes of imprecision of the observations and uncertainty of the authors who create Volunteer Geographic Information (VGI), i.e., georeferenced contents generated by volunteers when participating in some citizen science project. Specifically, various aspects of imprecision and uncertainty of VGI are outlined and, to cope with them, a knowledge-based approach is suggested based on the creation and management of “contextualized VGI”. A case study example in agriculture is reported where contextualized VGI can be created about in situ crops observations by the use of a smart app that supports volunteers by means of both an ontology and the representation of the context of the geo-localization. Furthermore, an approach to cope with both ill-defined knowledge and volunteer’s uncertainty or imprecise observations is defined based on a fuzzy ontology with uncertainty level-based approximate reasoning. By representing uncertainty and imprecision of VGI, users, i.e., consumers, can exploit quality checking mechanisms to filter VGI based on their needs