Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level
Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging system...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/24/5025 |
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| author | Kaiyi Bi Zheng Niu Shunfu Xiao Jie Bai Gang Sun Ji Wang Zeying Han Shuai Gao |
| author_facet | Kaiyi Bi Zheng Niu Shunfu Xiao Jie Bai Gang Sun Ji Wang Zeying Han Shuai Gao |
| author_sort | Kaiyi Bi |
| collection | DOAJ |
| description | Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R<sup>2</sup> > 0.6) than the single spectral index (R<sup>2</sup> > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R<sup>2</sup> = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture. |
| first_indexed | 2024-03-10T03:11:27Z |
| format | Article |
| id | doaj.art-442c6553b0db444ab06e85655f627b3c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:11:27Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-442c6553b0db444ab06e85655f627b3c2023-11-23T10:23:52ZengMDPI AGRemote Sensing2072-42922021-12-011324502510.3390/rs13245025Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-LevelKaiyi Bi0Zheng Niu1Shunfu Xiao2Jie Bai3Gang Sun4Ji Wang5Zeying Han6Shuai Gao7The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100193, ChinaThe State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaThe State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaThe State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaAdvanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R<sup>2</sup> > 0.6) than the single spectral index (R<sup>2</sup> > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R<sup>2</sup> = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.https://www.mdpi.com/2072-4292/13/24/5025nitrogenupscalebiomasshyperspectral LiDARmaize |
| spellingShingle | Kaiyi Bi Zheng Niu Shunfu Xiao Jie Bai Gang Sun Ji Wang Zeying Han Shuai Gao Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level Remote Sensing nitrogen upscale biomass hyperspectral LiDAR maize |
| title | Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level |
| title_full | Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level |
| title_fullStr | Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level |
| title_full_unstemmed | Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level |
| title_short | Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level |
| title_sort | non destructive monitoring of maize nitrogen concentration using a hyperspectral lidar an evaluation from leaf level to plant level |
| topic | nitrogen upscale biomass hyperspectral LiDAR maize |
| url | https://www.mdpi.com/2072-4292/13/24/5025 |
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