10.6084/m9.figshare.3519872.v1
Shawn P. Serbin
Shawn P.
Serbin
Aditya Singh
Aditya
Singh
Brenden E. McNeil
Brenden E.
McNeil
Clayton C. Kingdon
Clayton C.
Kingdon
Philip A. Townsend
Philip A.
Townsend
Supplement 1. The resulting PLSR model coefficients (and their uncertainties) for predicting foliar traits using leaf-level dried and ground spectral reflectance data.
Wiley
2016
remote sensing
reflectance spectroscopy
forests
plant functional traits
partial least-squares regression, PLSR
foliar chemistry
2016-08-04 17:55:37
Dataset
https://wiley.figshare.com/articles/dataset/Supplement_1_The_resulting_PLSR_model_coefficients_and_their_uncertainties_for_predicting_foliar_traits_using_leaf-level_dried_and_ground_spectral_reflectance_data_/3519872
<h2>File List</h2><div>
<p><a href="PLSR_Model_Coefficients.csv">PLSR_Model_Coefficients.csv</a> (MD5: f08d202ddec2d8250d8153cc56ffa0ab)</p>
</div><h2>Description</h2><div>
<p>The PLSR_Model_Coefficients.csv file is a comma-delimited file. It contains the resulting PLSR model coefficients (and their uncertainties) for predicting foliar traits using leaf-level dried and ground spectral reflectance data. The full model represents the coefficients generated using the full calibration data set (i.e., all samples) while the mean and standard deviation (S.D.) are derived from the 1000× jackknife models using a 70/30 split of the full calibration data set. Cells without numbers show where wavelengths were not utilized in the PLSR modeling.</p>
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