Resultados y Aportaciones

El Proyecto ERMES investiga tres áreas tecnológicas: Observación de la Tierra (EO), modelado de cosecha y tecnologías de la información. Esta sección describirá los resultados y logros locales y regionales obtenidos gradualmente en el proyecto ERMES.

(English) ERMES at work: Monitoring 2016 European rice growing season
(English) 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
(English) 2016 – Forecasting rice yield at rice district scale from WARM
(English) 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
(English) 2016 – ERMES Products Support Variable Rate Fertilisation: Successful Real-world Experiment in Greece
(English) 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
(English) 2016 – Derivation of time series of LAI maps from Sentinel-2A
(English) 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
(English) 2016 – Early season field monitoring with VHR SAR data to highlight problems in germination
(English) 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
(English) 2016 – Detection of diseases in paddy rice fields with Sentinel-2
(English) The Valencian Plant Health Service ( 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
(English) 2016 – Cooperation of ERMES and RICEGUARD FP7 Projects for Estimating Rice Blast Infection Risk Accurately
(English)  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
(English) 2016 – Final versions of ERMES geoportal and AgriNotebook deployed
(English) 2016 – Phenological monitoring in Europe
(English) 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
(English) 2016 – Rice mapping in Europe: ERMES results
(English) 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