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  • Author or Editor: Francisca López-Granados x
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Hyperspectral reflectance curves of olive (Olea europaea L.) trees under different N or K treatments, and the best wavelengths or vegetation indices to discriminate between different N or K applications using discriminant analysis were investigated. Field hyperspectral studies were carried out in two olive orchards located at Cabra and Lucena (southern Spain) for N and K experiments, respectively, in 2004 and 2005. At Cabra, olive trees have been fertilized since 1993, and annual applications of N per tree consisted of 0 kg (N0), 0.5 kg [N1 (normal)], or 1 kg [N2 (high)]. At Lucena, olive trees were fertilized since 1997, with 0%, 2.5%, and 5% K2CO3. Hyperspectral measurements were collected for each N and K treatments using a handheld field spectroradiometer (spectral range, 400–900 nm) in July of both years. To determine the nutritional status, a leaf analysis was carried out in July 2004 and 2005 at both locations. At Cabra, leaf N concentrations under N0 treatment were below the critical threshold, indicating nutritional deficiencies. Reflectance curves corresponding to N1 and N2 showed higher reflectance values in the near-infrared (NIR) plateau than N0 treatments. Wavelengths within the NIR region (from 710–900 nm) were selected in both years for discriminating between N treatments, with an overall accuracy of up to 99.2%. At Lucena, when K was not applied, leaf K content was below the critical threshold, indicating that olive trees were under a nutritional deficiency. Wavelengths from 710 to 890 nm, and the normalized difference vegetation index {NDVI = [(R780 – R670)/(R780 + R670]} were selected for discriminating K treatments with an overall accuracy of up to 94.4%. Classification matrices for cross-validation classified and misclassified cases into the nearest category. The results suggest that the induction of N or K nutritional deficiency for more than 10 years in olive trees resulted in different leaf nutrient contents, and this consistently affected hyperspectral reflectance curves, mainly in the NIR region. These results are promising and could provide information for further work on the identification of N- or K-deficient olive trees using remote sensing.

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In 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.

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