A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data
Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10818736/ |
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| author | Zhanghua Xu Chaofei Zhang Songyang Xiang Lingyan Chen Xier Yu Haitao Li Zenglu Li Xiaoyu Guo Huafeng Zhang Xuying Huang Fengying Guan |
| author_facet | Zhanghua Xu Chaofei Zhang Songyang Xiang Lingyan Chen Xier Yu Haitao Li Zenglu Li Xiaoyu Guo Huafeng Zhang Xuying Huang Fengying Guan |
| author_sort | Zhanghua Xu |
| collection | DOAJ |
| description | Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (<italic>Phyllostachys pubescens</italic>) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared with the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R<sup>2</sup> values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation. |
| format | Article |
| id | doaj-art-c7339be5c2a748178bc3e447dacbe47a |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c7339be5c2a748178bc3e447dacbe47a2025-01-21T00:00:31ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183125314310.1109/JSTARS.2024.352277410818736A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI DataZhanghua Xu0https://orcid.org/0000-0001-9017-6920Chaofei Zhang1Songyang Xiang2Lingyan Chen3Xier Yu4Haitao Li5Zenglu Li6Xiaoyu Guo7Huafeng Zhang8Xuying Huang9https://orcid.org/0000-0002-6774-8174Fengying Guan10College of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaCollege of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaCollege of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaCollege of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaCollege of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaCollege of Environment and Safety Engineering and the Academy of Digital China, Fuzhou University, Fuzhou, ChinaFujian Provincial Key Laboratory of Resources and Environment Monitoring and Sustainable Management and Utilization, Sanming, ChinaFujian Provincial Key Laboratory of Resources and Environment Monitoring and Sustainable Management and Utilization, Sanming, ChinaXiamen Administration Center of Afforestation, Xiamen, ChinaInstitute of Agricultural Economics and Information, Guangdong Academy of Agricultural Sciences, Guangzhou, ChinaInternational Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, ChinaLeaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (<italic>Phyllostachys pubescens</italic>) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared with the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R<sup>2</sup> values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation.https://ieeexplore.ieee.org/document/10818736/Canopy chlorophyll content (CCC)hybrid methodleaf area index (LAI)Moso bamboo forestsPROSAIL RTM |
| spellingShingle | Zhanghua Xu Chaofei Zhang Songyang Xiang Lingyan Chen Xier Yu Haitao Li Zenglu Li Xiaoyu Guo Huafeng Zhang Xuying Huang Fengying Guan A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Canopy chlorophyll content (CCC) hybrid method leaf area index (LAI) Moso bamboo forests PROSAIL RTM |
| title | A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data |
| title_full | A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data |
| title_fullStr | A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data |
| title_full_unstemmed | A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data |
| title_short | A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (<italic>Phyllostachys pubescens</italic>) Forests From Sentinel-2 MSI Data |
| title_sort | hybrid method of prosail rtm for the retrieval canopy lai and chlorophyll content of moso bamboo italic phyllostachys pubescens italic forests from sentinel 2 msi data |
| topic | Canopy chlorophyll content (CCC) hybrid method leaf area index (LAI) Moso bamboo forests PROSAIL RTM |
| url | https://ieeexplore.ieee.org/document/10818736/ |
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