Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions

Salah El-Hendawy; Salah Elsayed; Nasser Al-Suhaibani; Majed Alotaibi; Muhammad Usman Tahir; Muhammad Mubushar; Ahmed Attia; Wael M Hassan

doi:10.3390/plants10010101

Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions

Plants (Basel). 2021 Jan 6;10(1):101. doi: 10.3390/plants10010101.

Authors

Salah El-Hendawy^{1

2}, Salah Elsayed³, Nasser Al-Suhaibani¹, Majed Alotaibi¹, Muhammad Usman Tahir¹, Muhammad Mubushar¹, Ahmed Attia⁴, Wael M Hassan^{5

6}

Affiliations

¹ Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
² Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt.
³ Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt.
⁴ Agronomy Department, Faculty of Agriculture, Zagazig University, Zagazig 44519, Egypt.
⁵ Department of Biology, College of Science and Humanities at Quwayiyah, Shaqra University, Riyadh 19257, Saudi Arabia.
⁶ Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt.

Abstract

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 (Chla), chlorophyll b (Chlb), and chlorophyll a+b (Chlt) 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 (p ≤ 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^-1) 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 Chla and Chlt content than of SDW and Chlb under both salinity levels. They also provided a more accurate estimation of SDW than of Chl 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 Chlb 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.

Keywords: biomass; contour maps; leaf pigments; multiple linear regression; phenotyping; salinity stress; spectral reflectance indices.

Grants and funding

RG-1435-032/Deanship of Scientific Research, King Saud University