Deriving vegetation leaf water content from spectrophotometric data with orthogonal signal correction-partial least square regression

Abstract

As an important element for the growth of plants, water is of great significance for real-time understanding of vegetation status, especially in the field of agricultural drought monitoring and forest fire prediction. Currently, vegetation leaf water content (LWC) estimation approaches employing remote sensing mainly include monitoring of spectral indices based on vegetation moisture feature bands and processing of continuous spectral data gained from hyperspectral remote sensing. In this article, a new approach, orthogonal signal correction-partial least square regression (OSC-PLSR), is introduced, to extract LWC from laboratory-measured reflectance spectra. Equivalent water thickness (EWT) and fuel moisture content (FMC) (based on both fresh and dry weight; FMCw and FMCd) were selected as indicators of LWC. Using the Leaf Optical Properties EXperiment (LOPEX) data set, first the relationships between LWC (EWT, FMCw and FMCd) and the spectral features of original reflectance were examined, via simple PLSR. Next, the OSC-PLSR was applied to evaluate its performance for estimating LWC (EWT, FMCw and FMCd) from reflectance spectra. Then, the vegetation moisture feature bands were derived from the analysis of OSC-PLSR latent variable loadings. According to the results, there are three major conclusions. (1) OSC-PLSR shows good performance for predicting all LWC indicators, and using only one latent variable, the OSC-PLSR model's complexity is greatly reduced compared with simple PLSR models. (2) Using both one and an optimal number of latent variables in OSC-PLSR models, FMCw sees the best prediction, followed by EWT and lastly FMCd; this order is opposite to the order of LWC data variation. (3) Through loading analysis of one-latent-variable OSC-PLSR models, the vegetation moisture feature bands can be retrieved. For EWT, the moisture-sensitive bands lie within one near-infrared (NIR) and two shortwave-infrared (SWIR) regions; for FMCw and FMCd, the moisture-sensitive bands lie within two SWIR regions. Also, the vegetation moisture-insensitive bands for EWT, FMCw and FMCd estimation are respectively acquired at around 1340, 1150 and 1330 nm.