Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data
Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We...
| Main Authors: | , , , , , , , , , |
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| 格式: | 文件 |
| 语言: | English |
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Taylor & Francis Group
2023-09-01
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| 丛编: | Geo-spatial Information Science |
| 主题: | |
| 在线阅读: | https://www.tandfonline.com/doi/10.1080/10095020.2023.2251540 |
| _version_ | 1827070340106289152 |
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| author | Xiaowen Guo Rong Wang Jing M. Chen Zhiqiang Cheng Hongda Zeng Guofang Miao Zhiqun Huang Zhenxiong Guo Jianjie Cao Jinhui Niu |
| author_facet | Xiaowen Guo Rong Wang Jing M. Chen Zhiqiang Cheng Hongda Zeng Guofang Miao Zhiqun Huang Zhenxiong Guo Jianjie Cao Jinhui Niu |
| author_sort | Xiaowen Guo |
| collection | DOAJ |
| description | Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data. |
| first_indexed | 2024-03-12T02:24:42Z |
| format | Article |
| id | doaj.art-0e1a99aa01c941e489db2eafb972f849 |
| institution | Directory Open Access Journal |
| issn | 1009-5020 1993-5153 |
| language | English |
| last_indexed | 2025-03-20T00:20:57Z |
| publishDate | 2023-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj.art-0e1a99aa01c941e489db2eafb972f8492024-10-10T11:52:38ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532023-09-0111410.1080/10095020.2023.2251540Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing dataXiaowen Guo0Rong Wang1Jing M. Chen2Zhiqiang Cheng3Hongda Zeng4Guofang Miao5Zhiqun Huang6Zhenxiong Guo7Jianjie Cao8Jinhui Niu9School of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaSchool of Geographical Sciences, Fujian Normal University, Fuzhou, ChinaIndividual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.https://www.tandfonline.com/doi/10.1080/10095020.2023.2251540Unmanned Aerial Vehicle (UAV)Leaf Area Index (LAI)Leaf Chlorophyll Content (LCC)synergetic inversionempirical model |
| spellingShingle | Xiaowen Guo Rong Wang Jing M. Chen Zhiqiang Cheng Hongda Zeng Guofang Miao Zhiqun Huang Zhenxiong Guo Jianjie Cao Jinhui Niu Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data Geo-spatial Information Science Unmanned Aerial Vehicle (UAV) Leaf Area Index (LAI) Leaf Chlorophyll Content (LCC) synergetic inversion empirical model |
| title | Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data |
| title_full | Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data |
| title_fullStr | Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data |
| title_full_unstemmed | Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data |
| title_short | Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data |
| title_sort | synergetic inversion of leaf area index and leaf chlorophyll content using multi spectral remote sensing data |
| topic | Unmanned Aerial Vehicle (UAV) Leaf Area Index (LAI) Leaf Chlorophyll Content (LCC) synergetic inversion empirical model |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2023.2251540 |
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