The MERIS maximum chlorophyll index (MCI), measuring the radiance peak at 709 nm in water-leaving radiance, indicates the presence of a high surface concentration of chlorophyll a against a scattering background. The index is high in 'red tide' conditions (intense, visible, surface, plankton blooms) and is raised when aquatic vegetation is present. A bloom search based on the MCI has resulted in the detection of a variety of events in Canadian, Antarctic and other waters round the world, as well as detection of extensive areas of pelagic vegetation (Sargassum spp.), previously unreported in the scientific literature. Since 1 June 2006, global MCI composite images, at a spatial resolution of 5 km, are being produced daily from all MERIS (daylight) passes of reduced resolution (RR) data. The global composites significantly increase the area now being searched for events, although the reduced spatial resolution may cause smaller events to be missed. This paper describes the composites and gives examples of plankton bloom events that they have detected. It also shows how the composites are affected by the South Atlantic anomaly (SAA), where cosmic rays impact the detectors of the MERIS instrument.

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The current diversity of Cloud computing services, benefic for the fast development of a new IT market, hinders the easy development, portability and inter-operability of Cloud oriented applications. Developing an application oriented view of Cloud services instead the current provider ones can lead to a step forward in the adoption of Cloud computing on a larger scale than the actual one. In this context, we present a position paper exposing the concepts behind a recent proposal for an open-source application programming interface and platform for dealing with multiple Cloud computing offers.

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.