ERMES old – GEOTEC

An Earth Observation Based Rice information service

Short Description

The ERMES project (EU-FP7) aims to develop pre-operational downstream services dedicated to rice farming systems based on the assimilation of Earth Observations (EO) products and in-situ data within crop yield modelling. ERMES exploits state-of-the-art of Geospatial Information Infrastructures (GII), also known as Spatial Data Infrastructures (SDI), and smart applications to acquire in-field information and deliver the service information to End-Users.

GEOTEC’s contribution

GEOTEC’s main contribution to the ERMES project consists of three software tools to support the regional and local downstream rice farming services: the smart app, and the local and regional geoportal. The smart app gives local end users (farmers, field workers) the necessary facilities to identify and manage their fields. The local geoportal allows local end users to visualize, interpret and compare in-situ data, sensor data, scientific simulations and satellite data. Finally, the regional geoportal operates on a regional scale, and aims to help regional authorities and agro-business in monitoring larger areas, planning and policymaking. The regional geoportal is developed along the same principles as the local geoportal, and offers sensor data and analysis results of (Sentinel) satellite images at regional scale.

Publications

7 entries « ‹ 1 of 4 › »

Granell-Canut, Carlos; Casteleyn, Sven; Busetto, Lorenzo; Pascucci, S.; Garcia-Haro, Javier; Gitas, I.; Holecz, F.; Katsantonis, D.; Confalonieri, R.; Boschetti, Mirco

EO-based agro monitoring system to support regional decision-making. Book Chapter

In: The ever growing use of Copernicus across Europe’s regions: A selection of 99 user stories by local and regional authorities, pp. 72-73, Nereus, Luxembourg, 2018.

BibTeX

Pagani, Valentina; Guarneri, Tommaso; Busetto, Lorenzo; Ranghetti, Luigi; Boschetti, Mirco; Movedi, Ermes; Campos-Taberner, Manuel; Garcia-Haro, Francisco Javier; Katsantonis, Dimitrios; Stavrakoudis, Dimitris; Ricciardelli, Elisabetta; Romano, Filomena; Holecz, Francesco; Collivignarelli, Francesco; Granell-Canut, Carlos; Casteleyn, Sven; Confalonieri., Roberto

A high-resolution, integrated system for rice yield forecasting at district level Journal Article

In: Agricultural systems, vol. 168, pp. 181-190, 2018, ISSN: 0308-521X, (IF).

Abstract | Links | BibTeX

@article{Pagani2018,

title = {A high-resolution, integrated system for rice yield forecasting at district level},

author = {Valentina Pagani and Tommaso Guarneri and Lorenzo Busetto and Luigi Ranghetti and Mirco Boschetti and Ermes Movedi and Manuel Campos-Taberner and Francisco Javier Garcia-Haro and Dimitrios Katsantonis and Dimitris Stavrakoudis and Elisabetta Ricciardelli and Filomena Romano and Francesco Holecz and Francesco Collivignarelli and Carlos Granell-Canut and Sven Casteleyn and Roberto Confalonieri. },

doi = {https://doi.org/10.1016/j.agsy.2018.05.007},

issn = {0308-521X},

year  = {2018},

date = {2018-07-23},

journal = {Agricultural systems},

volume = {168},

pages = {181-190},

abstract = {To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project.},

note = {IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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