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 (Chl area, μg cm−2), concentration per plant (Chl plant, mg plant−1), and SPAD value (Chl SPAD). 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 Chl plant 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 Chl plant 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 Chl area, 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.
Highlights
The greatest challenge for the agriculture sector in arid and semiarid regions is the scarcity of freshwater, which is increasing year after year due to population rise and the negative impacts of global warming
The two genotypes did not differ in Chl SPAD under the three salinity levels
The results showed that the different predictive models for different types of spectral reflectance indices (SRIs) failed to predict any unit of Chl content under the control treatment, as well as Chl SPAD under moderate and high salinity levels, except the indices of normalized difference (ND) and modified normalized difference (MND) forms, which exhibited a weak relationship with Chl SPAD under high salinity levels (Table 6)
Summary
The greatest challenge for the agriculture sector in arid and semiarid regions is the scarcity of freshwater, which is increasing year after year due to population rise and the negative impacts of global warming. Various agronomic approaches were applied to minimize these negative impacts of salinity stress on crop growth and productivity [6,8,9]. To develop the salt tolerance of genotypes, understanding the salt tolerance mechanisms that depend on physiological and photosynthetic indicators is a very important step in achieving this objective. This is because most of these indicators reflect the response of plants to salt stress at the levels of organ, tissue, and cellular, and they make the evaluation of salt tolerance between genotypes more effective [5,6,10,11,12]
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