TY - DATA T1 - Supplement 1. The resulting PLSR model coefficients (and their uncertainties) for predicting foliar traits using leaf-level dried and ground spectral reflectance data. PY - 2016/08/04 AU - Shawn P. Serbin AU - Aditya Singh AU - Brenden E. McNeil AU - Clayton C. Kingdon AU - Philip A. Townsend UR - 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 DO - 10.6084/m9.figshare.3519872.v1 L4 - https://ndownloader.figshare.com/files/5588585 L4 - https://ndownloader.figshare.com/files/5588588 KW - remote sensing KW - reflectance spectroscopy KW - forests KW - plant functional traits KW - partial least-squares regression, PLSR KW - foliar chemistry KW - Environmental Science KW - Ecology N2 - File List PLSR_Model_Coefficients.csv (MD5: f08d202ddec2d8250d8153cc56ffa0ab) Description 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. ER -