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Remote sensing involves techniques that use sensors to detect and record signals emanating from target of interest not in direct contact with the sensors. Remote sensing systems integrate cameras, scanners, radiometers, radar and other devices, and deal with the collection, processing, and distribution of large amounts of data. They often require massive computing resources to generate the data of interest for their users. Nowadays, remote sensing is mainly applied to satellite imagery. Satellites have proven in the last two decades their powerful capabilities to allow the Earth observation on a global scale. This observation is currently used in strategic planning and management of natural resources. The applications based on satellite data are often encountered in at least six disciplines: (1) agriculture, forestry and range resources in vegetation type, vigor and stress, biomass, soil conditions, or forest fire detection; (2) land use and mapping for classification, cartographic mapping, urban areas, or transportation networks; (3) geology for rock types, delineation, landforms, or regional structures detection; (4) water resources for water boundaries, surface, depth, volume, floods, snow areal, sediments, or irrigated fields detection; (5) oceanography and marine resources for marine organisms, turbidity patterns, or shoreline changes detection; (6) environment for surface mining, water pollution, air pollution, natural disasters, or defoliation detection. Current applications involving satellite data needs huge computational power and storage capacities. Grid computing technologies that have evolved in the last decade promise to make feasible the creation of an environment, for these kinds of applications, which can to handle hundreds of distributed databases, heterogeneous computing resources, and simultaneous users. Grid-based experimental platforms were developed already at this century’s beginning with a strong support from NASA and ESA. In this context, the chapter presents for the beginners an overview of the technological challenges and user requirements in remote sensed image processing, as well as the solutions provided by the Grid-based platforms built in the last decade. Section 2 starts with a short description of the basic principles of the satellite imagery, the technical problems and state of the art in solving them. It points also the fact that the training activities in Earth observation are not following the intensity of the research activities and there is a clear gap between the request for specialists and the labor market offer.

Remote Sensed Image Processing on Grids for Training in Earth Observation