Best hyperspectral indices for assessing leaf chlorophyll content in a degraded temperate vegetation

Abstract

Extensive studies have focused on assessing leaf chlorophyll content through spectral indices; however, the accuracy is weakened by limited wavebands and coarse resolution. With hundreds of wavebands, hyperspectral data can substantially capture the essential absorption features of leaf chlorophyll; however, few such studies have been conducted on same species in various degraded vegetations. In this investigation, complete combinations of either original reflectance or first‐order derivative spectra we conducted a complete combination on either original reflectance or its first‐order derivative value from 350 to 1000 nm to quantify leaf total chlorophyll (Chll), chlorophyll‐a (Chla), and chlorophyll‐b (Chlb) contents. This was performed using three hyperspectral datasets collected in situ from lightly, moderately, and severely degraded vegetations in temperate Helin County, China. Suitable combinations were selected by comparing the numbers of significant correlation coefficients with leaf Chll, Chla, and Chlb contents. The combinations of reflectance difference (D ij), normalized differences (ND), first‐order derivative (FD), and first‐order derivative difference (FD(D)) were found to be the most effective. These sensitive band‐based combinations were further optimized by means of a stepwise linear regression analysis and were compared with 43 empirical spectral indices, frequently used in the literature. These sensitive band‐based combinations on hyperspectral data proved to be the most effective indices for quantifying leaf chlorophyll content (R 2 > 0.7, p < 0.01), demonstrating great potential for the use of hyperspectral data in monitoring degraded vegetation at a fine scale.

Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

“…Different degradation intensities demonstrated obvious variability in correlation coefficients. As reported in Peng et al 39 , under light degradation, the values of Viopt, FD525-570, MSS-DVI, SDb, and SDr were significantly negatively correlated with measured contents. However, in severe degradation, the relations are significantly positive.…”

“…However, for leaf N estimation in wheat, wavelengths of 384, 492, 695, 1339, and 508 nm and 681, 722, 960, 1264, and 1369 nm were found to perform best 17 . Among these bands, chlorophyll and carotenoids in green plants often strongly absorb the visible range 384, 492, and 508 nm; 681, 695, and 722 nm in the red range and can serves as sensitive N indicators; and the shortwave infrared range 960, 1264, 1339, and 1369 nm bands are indicators of proteins (where N is a main component) 39 . In our study, the sensitive bands for leaf N content from temperate degraded vegetation were found to be 468, 623, 624, 633, 652, 657, 668, 818, 821, 842, 937, and 938 nm, in the red and NIR regions.…”

“…We used stepwise linear regression to distinguish the optimal combination among the 22 sensitive bands selected earlier to identify the best combinations for estimating leaf nutrition for all indices. Following this, previously established methods were applied to devise high accuracy regression equations for leaf N, P, and K content 39 . The most sensitive bands finally selected for leaf N content were 468, 623, 624, 633, 652, 657, 668, 818, 821, 842, 937, and 938 nm (Table 1), which mostly lie in the red and NIR regions of the spectrum.…”

Optimization of Photoperiod and Quality Assessment of Basil Plants Grown in a Small-Scale Indoor Cultivation System for Reduction of Energy Demand

“…For this reason, the diodes mainly peak in the red, far-red, and blue part of the spectrum. These narrow bands of the spectrum boost the growth of the plants, as they satisfy the chlorophyll a (Chl a) and chlorophyll b (Chl b) requirements [5].…”

Retrieval of leaf chlorophyll content in Gannan navel orange based on fusing hyperspectral vegetation indices using machine learning algorithms

“…Therefore, traditional wet chemistry destructive method is obviously undesirable for assessing chlorophyll content in navel oranges. For this reason, hyperspectral remote-sensing technology can be applied to estimate the spatio-temporal variations in the physical and chemical parameters of vegetation, including the chlorophyll content, at a relatively low cost compared to field measurements (MUTANGA et al, 2004;ZHANG et al, 2008;HE, 2013;PU et al, 2014;PENG et al, 2018;HOEPPNER et al, 2020;JI et al, 2020;LI et al, 2020).…”