Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions

Abstract

Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chlarea, μg cm−2), concentration per plant (Chlplant, mg plant−1), and SPAD value (ChlSPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl area and Chlplant and performed poorly with Chl SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl SPAD, while they exhibited a strong relationship with Chlplant followed by Chl area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73–79%, 80–84%, and 39–43% of the total variability in Chlarea, Chl plant, and Chl SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (μg cm−2) or concentration (mg plant−1) can be accurately estimated at canopy level using spectral reflectance data.

Comparative analysis of physiological variations and genetic architecture for cold stress response in soybean germplasm

“…Analysis of variance depicted that these traits had high heritability which indicates that these traits are genetically controlled and any difference in these varieties is purely due to their genetics and there is little influence of environment under controlled experiment (Figure 1B). The selected photosynthesis traits qL, Fv/Fm, NPQt, SPAD and leaf injury index (Zeng et al, 2021) in our study have been efficiently exploited for the screening and evaluation of drought, salt (Cho et al, 2021;El-Hendawy et al, 2022), and cold (Wang et al, 2016a;He et al, 2019), stresstolerant crop germplasm and for genome-wide association studies which help identification of important genomic loci/ QTLs, and genes. To better correlate these traits, we performed correlation analysis which indicates the higher and significant negative correlation among qL, Fv/Fm and leaf injury index traits which indicates that, indeed, cold stress halted the photosynthesis and caused a cellular injury which was displayed at recovery conditions in the form of leaf injury.…”

“…Under stress, protection of Chlorophyll (Chl) content reflects the plant's photosynthetic capacity and stress tolerance capability. The Chl content was quantified as SPAD value and depended on stress levels, genotypes and their growing environment (El-Hendawy et al, 2022). The abiotic stresses such as cold stress led to the overproduction of reactive oxygen species (ROS) which reduced Chl synthesis and accelerated Chl degradation in sensitive cultivars (Wang et al, 2016a;Acosta-Motos et al, 2017).…”

Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress

“…Plant pigments, such as chlorophyll and carotenoids, absorb a lot of visible light, especially blue and red light. Furthermore, the diffusion and scattering of radiation as a result of dry matter and leaf tissues has a significant impact on canopy reflectance in the NIR range [29][30][31][32][33].…”

Multi-index fuzzy comprehensive evaluation model with information entropy of alfalfa salt tolerance based on LiDAR data and hyperspectral image data

“…The spectral absorption and reflection characteristics of plants can be used to characterize their physiological and biochemical characteristics ( Tucker, 1977 ). Spectral indices can highlight phenotypic traits such as plant biomass, leaf water content, pigment content and salt stress index through band combination, and reduce the negative impact of spectral redundancy on trait extraction ( El-Hendawy et al., 2022 ). The RGB and multispectral sensors have lower spectral resolution, fewer bands, and discontinuous spectral coverage, which results in a limited availability of spectral index features.…”