Detection of ozone stress in rice cultivars using spectral reflectance

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...

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Autores principales:	Ambikapathi Ramya, Periyasamy Dhevagi, S.S. Rakesh, M. Maheswari, Subburamu Karthikeyan, R Saraswathi, C.N. Chandrasekhar, S Venkataramani
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Elevated ozone Physiological stress Remote sensing technology Normalized difference spectral index Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/749089b4b2cb4b3e92b439cd780082fe
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spelling	oai:doaj.org-article:749089b4b2cb4b3e92b439cd780082fe2021-12-02T05:04:14ZDetection of ozone stress in rice cultivars using spectral reflectance2666-765710.1016/j.envadv.2021.100129https://doaj.org/article/749089b4b2cb4b3e92b439cd780082fe2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666765721001009https://doaj.org/toc/2666-7657Ozone 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.Ambikapathi RamyaPeriyasamy DhevagiS.S. RakeshM. MaheswariSubburamu KarthikeyanR SaraswathiC.N. ChandrasekharS VenkataramaniElsevierarticleElevated ozonePhysiological stressRemote sensing technologyNormalized difference spectral indexEnvironmental sciencesGE1-350ENEnvironmental Advances, Vol 6, Iss , Pp 100129- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Elevated ozone Physiological stress Remote sensing technology Normalized difference spectral index Environmental sciences GE1-350
spellingShingle	Elevated ozone Physiological stress Remote sensing technology Normalized difference spectral index Environmental sciences GE1-350 Ambikapathi Ramya Periyasamy Dhevagi S.S. Rakesh M. Maheswari Subburamu Karthikeyan R Saraswathi C.N. Chandrasekhar S Venkataramani Detection of ozone stress in rice cultivars using spectral reflectance
description	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.
format	article
author	Ambikapathi Ramya Periyasamy Dhevagi S.S. Rakesh M. Maheswari Subburamu Karthikeyan R Saraswathi C.N. Chandrasekhar S Venkataramani
author_facet	Ambikapathi Ramya Periyasamy Dhevagi S.S. Rakesh M. Maheswari Subburamu Karthikeyan R Saraswathi C.N. Chandrasekhar S Venkataramani
author_sort	Ambikapathi Ramya
title	Detection of ozone stress in rice cultivars using spectral reflectance
title_short	Detection of ozone stress in rice cultivars using spectral reflectance
title_full	Detection of ozone stress in rice cultivars using spectral reflectance
title_fullStr	Detection of ozone stress in rice cultivars using spectral reflectance
title_full_unstemmed	Detection of ozone stress in rice cultivars using spectral reflectance
title_sort	detection of ozone stress in rice cultivars using spectral reflectance
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/749089b4b2cb4b3e92b439cd780082fe
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Detection of ozone stress in rice cultivars using spectral reflectance

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