Next Article in Journal
A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation
Next Article in Special Issue
Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information
Previous Article in Journal
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Previous Article in Special Issue
Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model
Article Menu
Issue 2 (January-2) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(2), 197; https://doi.org/10.3390/rs11020197

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

1
and
1,2,*
1
Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
2
Research Institute of Green Science and Technology, Shizuoka University, Shizuoka 422-8529, Japan
*
Author to whom correspondence should be addressed.
Received: 18 December 2018 / Revised: 12 January 2019 / Accepted: 17 January 2019 / Published: 20 January 2019
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
Full-Text   |   PDF [1798 KB, uploaded 20 January 2019]   |  

Abstract

Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters. View Full-Text
Keywords: hyperspectral reflectance; PLSR; band selection; PROSPECT; mechanism hyperspectral reflectance; PLSR; band selection; PROSPECT; mechanism
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Jin, J.; Wang, Q. Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance. Remote Sens. 2019, 11, 197.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top