2015
DOI: 10.1080/01431161.2015.1024893
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Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

Abstract: The use of hyperspectral data to estimate forage nutrient content can be a challenging task, considering the multicollinearity problem, which is often caused by high data dimensionality. We predicted some variability in the concentration of limiting nutrients such as nitrogen (N), crude protein (CP), moisture, and non-digestible fibres that constrain the intake rate of herbivores. In situ hyperspectral reflectance measurements were performed at full canopy cover for C3 and C4 grass species in a montane grassla… Show more

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Cited by 37 publications
(20 citation statements)
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“…Moreover, it also permits us to acquire images in flexible dates, exploiting the same sunny illumination conditions and eliminating problems due to clouds, in such a way that the imagery of two different periods can be compared. In addition, the possibility to mount a hyperspectral camera on it has allowed us also to fully take advantage of all the potentials of remote sensing, processing hundreds of narrow contiguous spectral bands to which the vegetation is sensitive [4,[12][13][14][15][16]. Indeed, in general, quite homogeneous grassland vegetation cover has relatively small variations in reflectance properties of the canopy, which could be visible and analyzed only from a continuous reflectance spectrum.…”
Section: Discussionmentioning
confidence: 99%
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“…Moreover, it also permits us to acquire images in flexible dates, exploiting the same sunny illumination conditions and eliminating problems due to clouds, in such a way that the imagery of two different periods can be compared. In addition, the possibility to mount a hyperspectral camera on it has allowed us also to fully take advantage of all the potentials of remote sensing, processing hundreds of narrow contiguous spectral bands to which the vegetation is sensitive [4,[12][13][14][15][16]. Indeed, in general, quite homogeneous grassland vegetation cover has relatively small variations in reflectance properties of the canopy, which could be visible and analyzed only from a continuous reflectance spectrum.…”
Section: Discussionmentioning
confidence: 99%
“…The subsequent introduction of hyperspectral sensors has allowed researchers to improve the retrieval of grassland traits considerably [3][4][5]. Based on its narrow contiguous wavebands, hyperspectral sensors are more sensitive to vegetation variables since they provide a continuous reflectance spectrum of the vegetation target [4,[12][13][14][15][16]. Nevertheless, the hyperspectral images include hundreds of spectral bands, many of which are strongly correlated.…”
Section: Introductionmentioning
confidence: 99%
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“…Their results indicated the potential of using canopy reflectance data to estimate forage quality variables of warm-season grass pastures (R 2 = 0.27-0.72 for NDF and 0.67-0.74 for CP). Furthermore, Adjorlolo et al [62] found that using a spectral resampling technique for a few strategically selected band centers of known absorption or reflectance features is sufficient to estimate forage nutrients. Their results indicated prediction accuracies for CP content ranging from R 2 = 0.51 to 0.62.…”
Section: Calculating the Water-absorption Areamentioning
confidence: 99%