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PROYECTO AiRGOS: ABSTRACT
Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount for human livelihood. This vital function of agriculture must be satisfied within the context of environmental sustainability and climate change, combined with an unprecedented and still-expanding human population size every year. In that way, remote sensing has the capacity to assist the adaptive evolution of agricultural practices to face this major challenge, by providing repetitive information on crop status throughout the season at different scales. The practice of plastic covered agriculture is part of the transformation of traditional agriculture into a more industrial and high-tech precision agriculture. According to data from the Agrarian Census, Spain has around 65,000 ha of plastic covered greenhouses, with approximately 32,000 ha in Almería. Even more important than the area covered by greenhouse is the fact that, being only 0.27% of the useful agricultural area in Spain, protected crops manage to contribute around 15% of final Spanish agricultural production. Almost nine out of every ten hectares of greenhouses are devoted to vegetables, although woody crops, flowers, seeds, aromatic and medicinal plants, and spices are also produced. Following the research line materialized in the projects: GreenhouseSat (https://w3.ual.es/Proyectos/GreenhouseSat/) and Sentinel-GH (https://w3.ual.es/Proyectos/SentinelGH/). With this new proposal, we intend to do our bit in the case of intensive agriculture under plastic, so important for the province of Almería. Specifically, we propose to continue advancing in the application of remote sensing for the detection of plastic greenhouses and the crops that are growing inside them. For this, we propose the use of medium-resolution satellite time series (mainly Sentinel-2) using emerging Deep Learning and Machine Learning techniques. Within this general objective, two specific objectives are included, such as: (i) testing super-resolution methods using Deep Learning to increase the geometric and spectral resolution of Sentinel-2, being able to use these improved images in studies that require higher resolution such as the detection of illegal plastic dumping and (ii) to identify unique crops (papaya, aloe, cannabis) with a spectral signature and temporal behaviour different from classic horticultural crops in Almería. |
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Información de contacto:
Manuel Ángel Aguilar Torres Investigador Principal Full Professor
Teléfono: +34 950 015997 Fax: +34 950 015491 Correo: maguilar@ual.es |
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