DOI: 10.5061/dryad.g4f4qrg32
title: Hyperspectral reflectance-based partial least squares regression models for predicting cotton leaf physiological traits

description: Alterations in the mechanistic drivers of photosynthesis have the
 potential to improve crop productivity, but their measurement is
 inherently time-consuming using traditional methods. High-throughput
 approaches to estimate photosynthesis using hyperspectral reflectance
 could be developed by leveraging variation in cotton (Gossypium
 hirsutum L.) leaf traits generated through nitrogen
 management, synthetic growth regulation strategies, and leaf position
 within the canopy. Currently, no such models exist for cotton, and
 interactions among the aforementioned factors are relatively unexplored
 for cotton leaf traits.  This study aimed to (1) evaluate the
 effects of N application rate, mepiquat chloride (MC) management, and leaf
 position within the canopy on photosynthesis and its components, and (2)
 develop and validate hyperspectral reflectance-based partial least squares
 regression (PLSR) models for predicting cotton leaf physiological traits.
 N rate and leaf position interacted to affect net photosynthetic rate
 (AN), electron transport rate, chlorophyll a, and chlorophyll b, while
 mepiquat chloride influenced only leaf pigments and specific leaf weight
 (SLW). AN reductions under N deficiency were driven by declines in the
 maximum rate of Rubisco carboxylation (Vc,max) and
 ribulose-1,5-bisphosphate regeneration (Jmax​), whereas high N had no
 effect on AN. PLSR models exhibited good to high predictive accuracy, with
 R² values ranging from 0.62 to 0.87 for most traits, except for SLW. These
 findings enhance our understanding of the physiological responses to N
 rate, MC strategy, and leaf position and highlight the potential of
 hyperspectral reflectance-based PLSR as a high-throughput tool for
 predicting leaf physiological traits to improve photosynthetic efficiency
 in cotton.
description: # Hyperspectral reflectance-based partial least squares regression models
 for predicting cotton leaf physiological traits ## PLSR Model Coefficients
 for Cotton Leaf Traits This folder contains Partial Least Squares
 Regression (PLSR) model coefficients and jackknife coefficient files for
 estimating cotton leaf traits from reflectance spectra (500–2400 nm). ##
 File Naming Convention * **[Trait]PLSR_Coefficients[#comp].csv** Standard
 coefficients for estimating traits using the specified number of PLSR
 components. * **[Trait]_Jackkife_PLSR_Coefficients.csv** Jackknife
 coefficients used to estimate the uncertainty (standard deviation and
 confidence intervals) of trait predictions. **Example:** *
 `A_PLSR_Coefficients_15comp.csv` * `A_Jackkife_PLSR_Coefficients.csv` ##
 Column Descriptions To assist users unfamiliar with spectral modeling,
 below is a description of the variables (column headers) in the CSV files:
 ### 1. Standard Coefficients Files (`[Trait]PLSR_Coefficients[#comp]`) *
 **Unnamed: 0** Predictor variable label. This includes the intercept term
 `(Intercept)` and spectral band identifiers (e.g., `Wave_500`, `Wave_501`,
 ...). * **[Trait].[#comp].comps** PLSR coefficient value for the specified
 trait using the given number of PLSR components. * Units: Dimensionless
 regression weights. * Interpretation: These coefficients, when multiplied
 by the predictor values (reflectance), contribute to estimating the trait.
 Units of the final prediction match the trait being modeled (e.g.,
 chlorophyll content, nitrogen concentration, water). ### 2. Jackknife
 Coefficients Files (`[Trait]_Jackkife_PLSR_Coefficients`) * **Iteration**
 Index of the jackknife resampling iteration. Each row represents one
 jackknife sample used to estimate variability in coefficients. *
 **Intercept** Intercept coefficient for that jackknife iteration. *
 **Wave_[λ] (e.g., Wave_500, Wave_501, … Wave_2400)** Coefficients
 corresponding to reflectance at each wavelength (in nanometers, nm). *
 Wavelengths cover the **500–2400 nm spectral range**. * Units:
 Dimensionless regression weights. * Usage: Apply within the PLSR model to
 generate predictions; variation across iterations provides uncertainty
 estimates (e.g., standard deviation, confidence intervals). ##
 Instructions for Users 1. **Download the dataset** * Obtain the full file:
 **`PLSR_Coefficients_cotton.xlsx`** (hosted on Dryad under CC0). 2.
 **Prepare the coefficient files** * Open the Excel file and save each
 worksheet as an individual CSV file. * Make sure each CSV filename exactly
 matches the names referenced in the R script (e.g.,
 `A_PLSR_Coefficients_15comp.csv`, `A_Jackkife_PLSR_Coefficients.csv`). 3.
 **Organize your local working environment** * Place all CSV files inside a
 directory named `data/` within your local project folder. * Maintain the
 directory structure expected by the script (i.e.,
 `data/plsr_coefficients/`). 4. **Run the R script** * Download the script
 (**`Estimate_Cotton_Leaf_Traits_PLSR.R`** or the PDF version for
 documentation) from **Zenodo** (MIT License). * Execute the script to
 generate trait estimates. * Output files will include predicted trait
 values with associated uncertainty metrics (standard deviation, 5% and 95%
 confidence bounds). ## Contact For questions or technical support, please
 contact: **Ved Parkash**