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Atmospheric water absorption bands. 

Atmospheric water absorption bands. 

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Beginning with a discussion of reflectance spectroscopy, this article attempts to provide a review on fundamental concepts of reflectance spectroscopic techniques. Their applications as well as exploring the role of Near-infrared reflectance spectroscopy that would be used for monitoring and mapping soil characteristics. This technique began to be...

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... study of any soil property is related to the understanding of sensitive areas at the spectrum due to presence of water. The vibrational frequencies of water molecules after 2500 nm affect the water absorption wavelengths (Baumgardner et al., 1985). The 1450 and 1950 nm wavelengths are the absorption bands with sharp peaks (Fig. 8). The broad unordered bands are more common in naturally occurring soils in addition, the highest significant vari- able in determining the reflectance located within a range 2080- 2320mm (Baumgardner et al., 1985and Galvao et al., 2001). The broad unordered bands are more common in naturally occurring soils. Furthermore, the highest significant variable in determining the reflectance changes in the 2080-2320 mm. However, other studies emphasized on the importance role of reflectance spectroscopy and remote sensing to develop spectral models for detecting soil moisture content (Ben-Dor et al., 2002;Whiting et al., ...

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... In the past 10 years, several reviews have described rapid quantitative assessments of soil components based on infrared spectral techniques combined with chemometrics Tinti et al. 2015;Mohamed et al. 2018; Barra et al. 2021). Applications of MVA to soils contaminated with heavy metals at the regional level have been reviewed in several publications Hou et al. 2017). ...
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... Their studies are virtually identical to the current study in terms of the proposed technique and the accuracy of the results. Mohamed et al. (2018) and Morgan et al. (2018) validate the advantages of using bands including visible blue, green, red and Near-Infrared bands in combination with a neural network to map soil salinity, which corroborates our findings. Morgan et al. (2018) used NDVI spectral indices with ANN to produce a salinity map with 0.94 in R 2 . ...
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Chapter
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