This section describes the main Results and Achievements so far obtained in the ERMES project.
The following pages report some of the main activities conducted within ERMES in 2014, 2015 and 2016.
After the 2014/2015 design and preliminary implementation phase, the different functionalities of the ERMES services were further operationalized and tested during the 2016 European rice growing season exploiting the full suite of ERMES products and services. ... Read more
ERMES WARM regional modeling solution already tested in 2015 was applied in real time to predict the 2016 productivity in the Italian, Greek and Spanish rice districts in two moments of the season (i.e., around flowering and around maturity). ... Read more
2016 – ERMES Products Support Variable Rate Fertilisation: Successful Real-world Experiment in Greece
During the 2016 growing season, ERMES partners in Greece successfully performed a variable rate fertilization experiment, exploiting the information provided by the Seasonal Patterns Maps (EP_L3) ERMES product. The EP_L3 product analyses remotely sensed images (obtained either by satellite sensors or UAV-mounted ones throughout the growing season) in order to identify within-field anomalies due to seasonal drivers.... Read more
During the 2016 growing season, ERMES partners in Spain successfully performed a continuous exploitation of the high-resolution (10 m) Sentinel-2 data acquired in the three local study areas in order to derive leaf area index (LAI) maps . ... Read more
In 2016 ERMES team added to the portfolio of products available to support farmers in their agropractices during the season a new service based on regular monitoring of crop condition at the beginning of the season just after sowing exploit all weather condition VHR SAR data. ... Read more
The Valencian Plant Health Service (http://www.agroambient.gva.es/web/agricultura) reported to ERMES some fields which were affected by Akiochi disease in 2016 rice season. In order to identify and evaluate the infection within the affected fields, UVEG analyzed the spectral signature of Sentinel-2 images and the leaf area index (LAI) time series. The effect of Akiochi was detected in some fields due anomalies in the LAI estimates which were not expected for the same rice variety in a nearby field... Read more
2016 – Cooperation of ERMES and RICEGUARD FP7 Projects for Estimating Rice Blast Infection Risk Accurately
For the second year, European FP7 projects ERMES and RICEGUARD collaborated in the forecast of rice blast infection risk, since the results from the preliminary comparison in 2015 were promising. During 2016, results of the models used in the two project were compared in two areas of northern Italy and one in Greece (Figures 1–3). Comparison was conducted between the Daily Infection Warning Hours (DIWH) data (continuous line)—derived from the RICEGUARD model (modified Yoshino model)—and infection risk percentages (orange bars) derived from WARM model, which is used in the ERMES project. ... Read more
An operational version of the AgriNotebook and the Geoportals was deployed for the 2016 rice season in Spain, Italy and Greece, and used by regional and local stakeholders, particularly local farmers and field workers. They include exciting new features and updates, some of them to complete or extend existing functionality, others as a result of feedback on earlier version by the end users. We were happy to see that this user-centric approach was well received by end users, and the new versions of the ERMES tools were enthusiastically welcomed. ... Read more
ERMES processing chains were used again in 2016 to monitor the phenological cycle of rice in the three European study areas. 2x2 km maps of sowing and flowering dates were created by analyzing MODIS multitemporal data, exploiting the PhenoRice algorithm developed by CNR_IREA. ... Read more
In the framework of ERMES 2016, high resolution (20m) maps of rice crop distribution in the regional study area of Greece, Spaoin and Italy were created. This was done by analyzing multitemporal Sentinel 1-A radar images, as well as Sentinel-2A and Landsat 8-OLI data. ... Read more