El-Hendawy Salah, Elsayed Salah, Al-Suhaibani Nasser, Alotaibi Majed, Tahir Muhammad Usman, Mubushar Muhammad, Attia Ahmed, Hassan Wael M
Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt.
Plants (Basel). 2021 Jan 6;10(1):101. doi: 10.3390/plants10010101.
The application of proximal hyperspectral sensing, using simple vegetation indices, offers an easy, fast, and non-destructive approach for assessing various plant variables related to salinity tolerance. Because most existing indices are site- and species-specific, published indices must be further validated when they are applied to other conditions and abiotic stress. This study compared the performance of various published and newly constructed indices, which differ in algorithm forms and wavelength combinations, for remotely assessing the shoot dry weight (SDW) as well as chlorophyll a (), chlorophyll b (), and chlorophyll a+b () content of two wheat genotypes exposed to three salinity levels. Stepwise multiple linear regression (SMLR) was used to extract the most influential indices within each spectral reflectance index (SRI) type. Linear regression based on influential indices was applied to predict plant variables in distinct conditions (genotypes, salinity levels, and seasons). The results show that salinity levels, genotypes, and their interaction had significant effects ( ≤ 0.05 and 0.01) on all plant variables and nearly all indices. Almost all indices within each SRI type performed favorably in estimating the plant variables under both salinity levels (6.0 and 12.0 dS m) and for the salt-sensitive genotype Sakha 61. The most effective indices extracted from each SRI type by SMLR explained 60%-81% of the total variability in four plant variables. The various predictive models provided a more accurate estimation of and content than of SDW and under both salinity levels. They also provided a more accurate estimation of SDW than of content for salt-tolerant genotype Sakha 93, exhibited strong performance for predicting the four variables for Sakha 61, and failed to predict any variables under control and for Sakha 93. The overall results indicate that the simple form of indices can be used in practice to remotely assess the growth and chlorophyll content of distinct wheat genotypes under saline field conditions.
利用简单植被指数的近端高光谱传感技术的应用，为评估与耐盐性相关的各种植物变量提供了一种简便、快速且无损的方法。由于大多数现有指数具有地点和物种特异性，已发表的指数在应用于其他条件和非生物胁迫时必须进一步验证。本研究比较了各种已发表和新构建的指数（算法形式和波长组合不同）在远程评估两种小麦基因型在三种盐度水平下的地上部干重（SDW）以及叶绿素a（）、叶绿素b（）和叶绿素a + b（）含量方面的性能。采用逐步多元线性回归（SMLR）从每种光谱反射率指数（SRI）类型中提取最具影响力的指数。基于有影响力指数的线性回归被应用于预测不同条件（基因型、盐度水平和季节）下的植物变量。结果表明，盐度水平、基因型及其相互作用对所有植物变量和几乎所有指数都有显著影响（≤0.05和0.01）。每种SRI类型中的几乎所有指数在估计盐度水平（6.0和12.0 dS m）下以及对盐敏感基因型Sakha 61的植物变量时表现良好。通过SMLR从每种SRI类型中提取的最有效指数解释了四种植物变量总变异性的60%-81%。各种预测模型在两种盐度水平下对和含量的估计比SDW和更准确。对于耐盐基因型Sakha 93，它们对SDW的估计也比对含量更准确，对Sakha 61的四个变量预测表现良好，而在对照和条件下对Sakha 93未能预测任何变量。总体结果表明，指数的简单形式可在实际中用于远程评估盐渍田间条件下不同小麦基因型的生长和叶绿素含量。
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