Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model
Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horiz...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006411 |
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| author | Xun Zhao Jianbo Qi Jingyi Jiang Shangbo Liu Haifeng Xu Simei Lin Zhexiu Yu Linyuan Li Huaguo Huang |
| author_facet | Xun Zhao Jianbo Qi Jingyi Jiang Shangbo Liu Haifeng Xu Simei Lin Zhexiu Yu Linyuan Li Huaguo Huang |
| author_sort | Xun Zhao |
| collection | DOAJ |
| description | Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval. |
| format | Article |
| id | doaj-art-68dcdd9b2c694958b6cb4cd7fd16555d |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-68dcdd9b2c694958b6cb4cd7fd16555d2024-12-19T10:52:53ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-12-01135104285Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer modelXun Zhao0Jianbo Qi1Jingyi Jiang2Shangbo Liu3Haifeng Xu4Simei Lin5Zhexiu Yu6Linyuan Li7Huaguo Huang8Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaFaculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Corresponding author.State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaGuangzhou Institute of Forestry and Landscape Architecture, Guangzhou 510405, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaState Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing 100083, ChinaLeaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.http://www.sciencedirect.com/science/article/pii/S1569843224006411High-resolution data3D radiative transfer modelRetrievalLeaf chlorophyll content |
| spellingShingle | Xun Zhao Jianbo Qi Jingyi Jiang Shangbo Liu Haifeng Xu Simei Lin Zhexiu Yu Linyuan Li Huaguo Huang Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model International Journal of Applied Earth Observations and Geoinformation High-resolution data 3D radiative transfer model Retrieval Leaf chlorophyll content |
| title | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model |
| title_full | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model |
| title_fullStr | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model |
| title_full_unstemmed | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model |
| title_short | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model |
| title_sort | fine scale retrieval of leaf chlorophyll content using a semi empirically accelerated 3d radiative transfer model |
| topic | High-resolution data 3D radiative transfer model Retrieval Leaf chlorophyll content |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006411 |
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