A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery
Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of R...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9034180/ |
id |
doaj-1416290e46ac425f8fd8fb0a7408d0ec |
---|---|
record_format |
Article |
spelling |
doaj-1416290e46ac425f8fd8fb0a7408d0ec2021-03-30T02:11:03ZengIEEEIEEE Access2169-35362020-01-018521815219110.1109/ACCESS.2020.29803109034180A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral ImageryLinyi Liu0https://orcid.org/0000-0003-4587-2489Yingying Dong1https://orcid.org/0000-0002-2865-5020Wenjiang Huang2Xiaoping Du3Binyuan Ren4Linsheng Huang5Qiong Zheng6Huiqin Ma7Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaNational Agricultural Technology Extension and Service Center, Beijing, ChinaAnhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei, ChinaKey Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaRapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R<sup>2</sup> = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R<sup>2</sup> = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R<sup>2</sup> and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB.https://ieeexplore.ieee.org/document/9034180/Red-edgewheat fusarium head blightSentinel-2spectral analysisdisease index |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Linyi Liu Yingying Dong Wenjiang Huang Xiaoping Du Binyuan Ren Linsheng Huang Qiong Zheng Huiqin Ma |
spellingShingle |
Linyi Liu Yingying Dong Wenjiang Huang Xiaoping Du Binyuan Ren Linsheng Huang Qiong Zheng Huiqin Ma A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery IEEE Access Red-edge wheat fusarium head blight Sentinel-2 spectral analysis disease index |
author_facet |
Linyi Liu Yingying Dong Wenjiang Huang Xiaoping Du Binyuan Ren Linsheng Huang Qiong Zheng Huiqin Ma |
author_sort |
Linyi Liu |
title |
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery |
title_short |
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery |
title_full |
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery |
title_fullStr |
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery |
title_full_unstemmed |
A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery |
title_sort |
disease index for efficiently detecting wheat fusarium head blight using sentinel-2 multispectral imagery |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R<sup>2</sup> = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R<sup>2</sup> = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R<sup>2</sup> and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB. |
topic |
Red-edge wheat fusarium head blight Sentinel-2 spectral analysis disease index |
url |
https://ieeexplore.ieee.org/document/9034180/ |
work_keys_str_mv |
AT linyiliu adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT yingyingdong adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT wenjianghuang adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT xiaopingdu adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT binyuanren adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT linshenghuang adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT qiongzheng adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT huiqinma adiseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT linyiliu diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT yingyingdong diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT wenjianghuang diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT xiaopingdu diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT binyuanren diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT linshenghuang diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT qiongzheng diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery AT huiqinma diseaseindexforefficientlydetectingwheatfusariumheadblightusingsentinel2multispectralimagery |
_version_ |
1724185597159931904 |