Environmental Advances (Dec 2021)
Detection of ozone stress in rice cultivars using spectral reflectance
Abstract
Ozone stress identification in crops through remote sensing is a promising technology that has a major role in predicting stress-related physiological response. The spectral reflectance approach to phenotyping of rice cultivars is a non-destructive and accurate method to assess ozone stress. In this study, the leaf spectral reflectance of 15 rice cultivars was explored to evaluate the photosynthetic capacity under elevated ozone stress. Ozone injury percentage (OIP), a common indicator to identify ozone stress of rice leaves combined with leaf spectral signatures was used to identify the best fit spectral indices. The normalized difference spectral index (NDSI) was used to determine significant wavebands that showed the best correlation with leaf ozone injury. The correlation coefficient (r) between NDSI and OIP were ranged from -0.79 to 1. Normalized difference spectral index, NDSI[R564,R568], and NDSI[R558,R573] were identified as the best Pearson's correlation with leaf ozone injury percentage with correlation coefficient more than 0.96. The evaluated NDSI in visible regions were identified as a sensitive region to ozone injury. Additionally, results were compared with previously established indices and also evaluated for correlation with plant physiological traits. The correlation coefficient of determination (R2) > 0.5 was observed for newly developed spectral indices, NDSI[R564,R568] and NDSI[R558,R573] with physiological traits compared to previously established 20 indices. The highest R2 of 0.674 were observed for the spectral index, NDSI[R558,R573] with photosynthetic rate and R2 of 0.673 for NDSI[R564,R568] with relative chlorophyll content. As a result, the current research has implications for the selection of the best capable spectral regions for early detection of ozone stress in rice cultivars without destructing the crop. The outcome of the present work suggests that newly developed spectral indices could assist in remote sensing technology in monitoring large scale ozone stress and crop modeling studies to ensure food security.
