Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery
This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/6/957 |
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| author | Hengbiao Zheng Jifeng Ma Meng Zhou Dong Li Xia Yao Weixing Cao Yan Zhu Tao Cheng |
| author_facet | Hengbiao Zheng Jifeng Ma Meng Zhou Dong Li Xia Yao Weixing Cao Yan Zhu Tao Cheng |
| author_sort | Hengbiao Zheng |
| collection | DOAJ |
| description | This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season. |
| first_indexed | 2024-12-24T03:01:53Z |
| format | Article |
| id | doaj.art-65b9143b656c4912943a64030c89b345 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:01:53Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-65b9143b656c4912943a64030c89b3452022-12-21T17:18:10ZengMDPI AGRemote Sensing2072-42922020-03-0112695710.3390/rs12060957rs12060957Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral ImageryHengbiao Zheng0Jifeng Ma1Meng Zhou2Dong Li3Xia Yao4Weixing Cao5Yan Zhu6Tao Cheng7National Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaNational Engineering and Technology Center for Information Agriculture, Nanjing 210095, ChinaThis paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.https://www.mdpi.com/2072-4292/12/6/957uavmultispectral imagerytexture analysisvegetation indexn statusrice |
| spellingShingle | Hengbiao Zheng Jifeng Ma Meng Zhou Dong Li Xia Yao Weixing Cao Yan Zhu Tao Cheng Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery Remote Sensing uav multispectral imagery texture analysis vegetation index n status rice |
| title | Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
| title_full | Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
| title_fullStr | Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
| title_full_unstemmed | Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
| title_short | Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery |
| title_sort | enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle uav multispectral imagery |
| topic | uav multispectral imagery texture analysis vegetation index n status rice |
| url | https://www.mdpi.com/2072-4292/12/6/957 |
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