Ecological Indicators (Jul 2022)
Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease
Abstract
Hyperspectral imaging-derived vegetation indices (VIs) have rarely been developed to estimate leaf chlorophyll content of mangrove forests under pest and disease stress. Moreover, the optimal newly-developed hyperspectral VI is generally chosen through comparison of model accuracy alone with all possible VI combinations, which might render the conclusion one-sided. With SOC710 hyperspectral images of 119 mangrove leaf samples, this study aimed to develop a new hyperspectral VI sensitive to leaf relative chlorophyll content (SPAD value) by comprehensive comparison from five aspects (estimation accuracy, sensitivity, anti-noise performance, application to simulated EnMAP and PRISMA sensors, and spatial visualization quality). Eight types of newly-developed VIs were constructed from the sensitive wavebands selected by successive projection algorithm (SPA) method, and simple linear regression model was established using each VI. The results showed that the three-band VI ((λ757.9-λ709.4)/(λ709.4-λ708.1)) was the optimal for leaf SPAD estimation, because it had stronger correlation with SPAD, higher model accuracy of SPAD estimation using leaf and simulated hyperspectral imageries, stronger resistance to Gaussian noise, more sensitivity to extremely high chlorophyll content, and reasonable spatial visualization of SPAD. The four types of three-band VIs had higher model accuracy than the four types of two-band VIs, while two-band VIs had stronger resistance to higher Gaussian noise. Moreover, the wavelengths in the red edge region were efficient to develop hyperspectral VIs sensitive to leaf SPAD, and leaf SPAD could be more accurately estimated with pest and disease severity of 15–25%. We concluded that three-band VI consisting wavebands in the red edge region derived from leaf hyperspectral images is effective in capturing the changes of leaf chlorophyll content, which could provide great potentials for early warning of mangrove pest and disease with fine visualization details of chlorophyll content.
