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Author Lopatin, J. doi  openurl
  Title Estimation of Foliar Carotenoid Content Using Spectroscopy Wavelet-Based Vegetation Indices Type
  Year 2023 Publication IEEE Geosciences and Remote Sensing Letters Abbreviated Journal IEEE Geosci. Remote. Sens. Lett.  
  Volume 20 Issue Pages 2500405  
  Keywords Automobiles; Continuous wavelet transforms; Wavelet transforms; Indexes; Reflectivity; Vegetation mapping; Pigments; Brute-force approach; continuous wavelet transform (CWT); noise equivalent (NE); PROSPECT; random forest (RF); vegetation indices (VIs)  
  Abstract The plant carotenoid (Car) content plays a crucial role in the xanthophyll cycle and provides essential information on the physiological adaptations of plants to environmental stress. Spectroscopy data are essential for the nondestructive prediction of Car and other traits. However, Car content estimation is still behind in terms of accuracy compared to other pigments, such as chlorophyll (Chl). Here, I examined the potential of using the continuous wavelet transform (CWT) on leaf reflectance data to create vegetation indices (VIs). I compared six CWT mother families and six scales and selected the best overall dataset using random forest (RF) regressions. Using a brute-force approach, I created wavelet-based VIs on the best mother family and compared them against established Car reflectance-based VIs. I found that wavelet-based indices have high linear sensitivity to the Car content, contrary to typical nonlinear relationships depicted by the reflectance-based VIs. These relations were theoretically contrasted with the synthetic data created using the PROSPECT-D radiative transfer model. However, the best selection of wavelength bands in wavelet-based VIs varies greatly depending on the spectral characteristics of the input data before the transformation.  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
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  Series Volume Series Issue Edition  
  ISSN 1545-598X ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000934984900013 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1740  
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