AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages
Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early gro...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9447219/ |
| _version_ | 1818669854869159936 |
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| author | Wenhui Wang Yapeng Wu Qiaofeng Zhang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng |
| author_facet | Wenhui Wang Yapeng Wu Qiaofeng Zhang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng |
| author_sort | Wenhui Wang |
| collection | DOAJ |
| description | Accurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted VIs (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACI<sub>red-edge</sub> showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACI<sub>red-edge</sub> and AAEVI yielded the highest accuracy (<italic>R</italic><sup>2</sup> = 0.78, RMSE = 0.26%, and rRMSE = 10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (<italic>R</italic><sup>2</sup><0.40). These findings illustrated that the AAVIs have great potential in monitoring nitrogen status at early growth stages with high-resolution aerial or satellite images in the context of precision crop management. |
| first_indexed | 2024-12-17T06:58:50Z |
| format | Article |
| id | doaj.art-676a1e2b965e4b23b38bb41d9ec91af9 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-17T06:58:50Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-676a1e2b965e4b23b38bb41d9ec91af92022-12-21T21:59:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01146716672810.1109/JSTARS.2021.30865809447219AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth StagesWenhui Wang0Yapeng Wu1Qiaofeng Zhang2https://orcid.org/0000-0003-4882-5573Hengbiao Zheng3Xia Yao4Yan Zhu5Weixing Cao6Tao Cheng7https://orcid.org/0000-0002-4184-0730National Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture, MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, and Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaAccurate and timely monitoring of leaf nitrogen concentration (LNC) in rice is crucial to optimize nitrogen fertilizer management and reduce environmental pollution. Existing vegetation indices (VIs) often perform well for high canopy cover conditions, but their performance becomes poor at early growth stages due to the significant exposure of background materials and the induced spectral mixing effect. This study proposed a novel approach to estimate the LNC at early and middle growth stages of paddy rice by using abundance adjusted VIs (AAVIs) from unmanned aerial vehicle (UAV) multispectral imagery. An AAVI was constructed by combining the traditional VI and the rice abundant from linear spectral mixture analysis of UAV imagery. Subsequently, the performance of AAVIs was evaluated in comparison with traditional VIs derived from all pixels or green pixels for individual growth stages or multiple stages. The results demonstrated that AAVIs exhibited better performance in LNC estimation, regardless of individual stages or across the entire early season. Specially, AACI<sub>red-edge</sub> showed the best performance among the AAVIs evaluated for LNC estimation. For universal modeling across early stages, the combination of AACI<sub>red-edge</sub> and AAEVI yielded the highest accuracy (<italic>R</italic><sup>2</sup> = 0.78, RMSE = 0.26%, and rRMSE = 10.4%) performed remarkably better than the traditional VIs from all pixels or green pixels (<italic>R</italic><sup>2</sup><0.40). These findings illustrated that the AAVIs have great potential in monitoring nitrogen status at early growth stages with high-resolution aerial or satellite images in the context of precision crop management.https://ieeexplore.ieee.org/document/9447219/Leaf nitrogen concentration (LNC)linear spectral mixture analysis (LSMA)unmanned aerial vehicle (UAV)vegetation indices (VIs) |
| spellingShingle | Wenhui Wang Yapeng Wu Qiaofeng Zhang Hengbiao Zheng Xia Yao Yan Zhu Weixing Cao Tao Cheng AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Leaf nitrogen concentration (LNC) linear spectral mixture analysis (LSMA) unmanned aerial vehicle (UAV) vegetation indices (VIs) |
| title | AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages |
| title_full | AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages |
| title_fullStr | AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages |
| title_full_unstemmed | AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages |
| title_short | AAVI: A Novel Approach to Estimating Leaf Nitrogen Concentration in Rice From Unmanned Aerial Vehicle Multispectral Imagery at Early and Middle Growth Stages |
| title_sort | aavi a novel approach to estimating leaf nitrogen concentration in rice from unmanned aerial vehicle multispectral imagery at early and middle growth stages |
| topic | Leaf nitrogen concentration (LNC) linear spectral mixture analysis (LSMA) unmanned aerial vehicle (UAV) vegetation indices (VIs) |
| url | https://ieeexplore.ieee.org/document/9447219/ |
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