Downstream services for rice crop monitoring in Europe: from regional to local scale.

2017 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 5423-5441.
Busetto, L., Casteleyn, S., Granell, C., Pepe, M., Crema, A., Barbieri, M., Campos-Taberner, M., Casa, R., Collivignarelli, F., Confalonieri, R., García-Haro, J. ... Movedi, E., Nutini, F. ... Boschetti, M.


The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support
more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data
processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development
of advanced, integrated agricultural monitoring systems.

Keywords: Agriculture, food industry, modeling, monitoring, remote sensing
DOI: 10.1109/JSTARS.2017.2679159

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