Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing

The frequent occurrence of waterlogging stress in winter wheat not only seriously affects regional food security and ecological security, but also threatens social and economic stability and sustainable development. In order to identify the waterlogging stress level of winter wheat, a waterlogging s...

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Autores principales:	YANG Feifei, LIU Shengping, ZHU Yeping, LI Shijuan
Formato:	article
Lenguaje:	EN ZH
Publicado:	Editorial Office of Smart Agriculture 2021
Materias:	hyperspectral remote sensing waterlogging stress vegetation index spectral derivative difference entropy winter wheat Agriculture (General) S1-972 Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/5f1fb70f616544b0b98baba5c3c7b6a2
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spelling	oai:doaj.org-article:5f1fb70f616544b0b98baba5c3c7b6a22021-11-17T07:52:11ZIdentification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing2096-809410.12133/j.smartag.2021.3.2.202105-SA001https://doaj.org/article/5f1fb70f616544b0b98baba5c3c7b6a22021-06-01T00:00:00Zhttp://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-2-35.shtmlhttps://doaj.org/toc/2096-8094The frequent occurrence of waterlogging stress in winter wheat not only seriously affects regional food security and ecological security, but also threatens social and economic stability and sustainable development. In order to identify the waterlogging stress level of winter wheat, a waterlogging stress gradient pot experiment was set up in this research. Three factors were controlled: waterlogging stress level (control, slight waterlogging, severe waterlogging), stress duration (5 days, 10 days, 15 days) and wheat variety (YF4, JM31, JM38). Leaf and canopy hyperspectral data were measured by using ASD Field Spec3 and Gaiasky-mini2 imaging spectrometer, respectively. The data were collected from the first waterlogging day of winter wheat. The sunny and windless weather was selected and measured every 7 days until the wheat was mature. Combined with vegetation index, normalized mean distance and spectral derivative difference entropy, if winter wheat was under waterlogging stress was monitored and stress level was identified. The results showed that: 1) the spectral response characteristics of winter wheat under waterlogging stress changed significantly in RW, RE, NIR and 1650－1800 nm region, which may be due to the sensitivity of these regions to physiological parameters affecting the spectral response characteristics, such as pigment, nutrient, leaf internal structure, etc; 2) the simple ratio pigment index SRPI was the optimal vegetation index for identifying the waterlogging stress of winter wheat. The excellent performance of this vegetation index may come from its extreme sensitivity to the epoxidation state and photosynthetic efficiency of the xanthophyll cycle pigment; 3) the red light absorption valley (RW: 640－680 nm) region was the optimal region for identifying waterlogging stress level. In RW region, waterlogging stress level of winter wheat could be determined by the spectral derivative difference entropy at heading, flowering and filling stages. The greater the level of waterlogging stress, the greater the spectral derivative difference entropy. This may be due to the fact that the RW region was more sensitive to pigment content, and the spectral derivative difference entropy could reduce the effects of spectral noise and background. This study could provide a new method for monitoring waterlogging stress, and would have a good application prospect in the precise prevention and control of waterlogging stress. There are still shortcomings in this study, such as the difference between the pot experiment and the actual field environment, the lack of independent experimental verification, etc. Next research could add pot and field experiments, combine with cross-validation, to further verify the feasibility of this research method.YANG FeifeiLIU ShengpingZHU YepingLI ShijuanEditorial Office of Smart Agriculturearticlehyperspectral remote sensingwaterlogging stressvegetation indexspectral derivative difference entropywinter wheatAgriculture (General)S1-972Technology (General)T1-995ENZH智慧农业, Vol 3, Iss 2, Pp 35-44 (2021)
institution	DOAJ
collection	DOAJ
language	EN ZH
topic	hyperspectral remote sensing waterlogging stress vegetation index spectral derivative difference entropy winter wheat Agriculture (General) S1-972 Technology (General) T1-995
spellingShingle	hyperspectral remote sensing waterlogging stress vegetation index spectral derivative difference entropy winter wheat Agriculture (General) S1-972 Technology (General) T1-995 YANG Feifei LIU Shengping ZHU Yeping LI Shijuan Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
description	The frequent occurrence of waterlogging stress in winter wheat not only seriously affects regional food security and ecological security, but also threatens social and economic stability and sustainable development. In order to identify the waterlogging stress level of winter wheat, a waterlogging stress gradient pot experiment was set up in this research. Three factors were controlled: waterlogging stress level (control, slight waterlogging, severe waterlogging), stress duration (5 days, 10 days, 15 days) and wheat variety (YF4, JM31, JM38). Leaf and canopy hyperspectral data were measured by using ASD Field Spec3 and Gaiasky-mini2 imaging spectrometer, respectively. The data were collected from the first waterlogging day of winter wheat. The sunny and windless weather was selected and measured every 7 days until the wheat was mature. Combined with vegetation index, normalized mean distance and spectral derivative difference entropy, if winter wheat was under waterlogging stress was monitored and stress level was identified. The results showed that: 1) the spectral response characteristics of winter wheat under waterlogging stress changed significantly in RW, RE, NIR and 1650－1800 nm region, which may be due to the sensitivity of these regions to physiological parameters affecting the spectral response characteristics, such as pigment, nutrient, leaf internal structure, etc; 2) the simple ratio pigment index SRPI was the optimal vegetation index for identifying the waterlogging stress of winter wheat. The excellent performance of this vegetation index may come from its extreme sensitivity to the epoxidation state and photosynthetic efficiency of the xanthophyll cycle pigment; 3) the red light absorption valley (RW: 640－680 nm) region was the optimal region for identifying waterlogging stress level. In RW region, waterlogging stress level of winter wheat could be determined by the spectral derivative difference entropy at heading, flowering and filling stages. The greater the level of waterlogging stress, the greater the spectral derivative difference entropy. This may be due to the fact that the RW region was more sensitive to pigment content, and the spectral derivative difference entropy could reduce the effects of spectral noise and background. This study could provide a new method for monitoring waterlogging stress, and would have a good application prospect in the precise prevention and control of waterlogging stress. There are still shortcomings in this study, such as the difference between the pot experiment and the actual field environment, the lack of independent experimental verification, etc. Next research could add pot and field experiments, combine with cross-validation, to further verify the feasibility of this research method.
format	article
author	YANG Feifei LIU Shengping ZHU Yeping LI Shijuan
author_facet	YANG Feifei LIU Shengping ZHU Yeping LI Shijuan
author_sort	YANG Feifei
title	Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
title_short	Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
title_full	Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
title_fullStr	Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
title_full_unstemmed	Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing
title_sort	identification and level discrimination of waterlogging stress in winter wheat using hyperspectral remote sensing
publisher	Editorial Office of Smart Agriculture
publishDate	2021
url	https://doaj.org/article/5f1fb70f616544b0b98baba5c3c7b6a2
work_keys_str_mv	AT yangfeifei identificationandleveldiscriminationofwaterloggingstressinwinterwheatusinghyperspectralremotesensing AT liushengping identificationandleveldiscriminationofwaterloggingstressinwinterwheatusinghyperspectralremotesensing AT zhuyeping identificationandleveldiscriminationofwaterloggingstressinwinterwheatusinghyperspectralremotesensing AT lishijuan identificationandleveldiscriminationofwaterloggingstressinwinterwheatusinghyperspectralremotesensing
_version_	1718425861090181120

Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing

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