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Title

Classifying irrigated crops as affected by phenological stage using discriminant analysis and neural networks

AuthorsLópez Granados, Francisca ; Gómez-Casero, M. Teresa; Peña Barragán, José Manuel ; Jurado-Expósito, Montserrat ; García Torres, Luis
Issue DateSep-2010
PublisherAmerican Society for Horticultural Science
CitationJournal of the American Society for Horticultural Science 135(5): 465-473 (2010)
AbstractIn Spain, water for agricultural use represents about 85% of the total water demand, and irrigated crop production constitutes a major contribution to the country's economy. Field studies were conducted to evaluate the potential of multispectral reflectance and seven vegetation indices in the visible and near-infrared spectral range for discriminating and classifying bare soil and several horticultural irrigated crops at different dates. This is the first step of a broader project with the overall goal of using satellite imagery with high spatial and multispectral resolutions for mapping irrigated crops to improve agricultural water use. On-ground reflectance data of bare soil and annual herbaceous crops [garlic (Allium sativum), onion (Allium cepa), sunflower (Helianthus annuus), bean (Vicia faba), maize (Zea mays), potato (Solanum tuberosum), winter wheat (Triticum aestivum), melon (Cucumis melo), watermelon (Citrillus lanatus), and cotton (Gossypium hirsutum)], perennial herbaceous crops [alfalfa (Medicago sativa) and asparagus (Asparagus officinalis)], deciduous trees [plum (Prunus spp.)], and non-deciduous trees [citrus (Citrus spp.) and olive (Olea europaea)] were collected using a handheld field spectroradiometer in spring, early summer, and late summer. Three classification methods were applied to discriminate differences in reflectance between the different crops and bare soil: stepwise discriminant analysis, and two artificial neural networks: multilayer perceptron (MLP) and radial basis function. On any of the sampling dates, the highest degree of accuracy was achieved with the MLP neural network, showing 89.8%, 91.1%, and 96.4% correct classification in spring, early summer, and late summer, respectively. The classification matrix from the MLP model using cross-validation showed that most crops discriminated in spring and late summer were 100% classifiable. For future works, we would recommend acquiring two multispectral satellite images taken in spring and late summer for monitoring and mapping these irrigated crops, thus avoiding costly field surveys.
URIhttp://hdl.handle.net/10261/84960
Identifiersissn: 0003-1062
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