Optimal Strategy for Designing a Multitask Learning-Based Hybrid Model to Predict Wheat Leaf Nitrogen Content
文献类型：期刊论文
| 作者 | Chen, Pengfei; Ma, Xiao |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2023 |
| 卷号 | 20页码:5 |
| 关键词 | Leaf nitrogen content (LNC) multitask learning-based hybrid model (ML-HM) optimal modeling strategy unmanned aerial vehicle (UAV) |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2023.3320154 |
| 通讯作者 | Chen, Pengfei(pengfeichen@igsnrr.ac.cn) |
| 英文摘要 | By combining a multitask deep learning method and a nitrogen PROSPECT and scattering by arbitrarily inclined leaves (N-PROSAILs) model, we proposed a multitask learning-based hybrid model (ML-HM) for leaf nitrogen content (LNC) prediction in a previous study. To provide an optimal ML-HM design for LNC prediction, this study focused on analyzing how factors, such as the simulated data distribution and sample size and the simulated and measured data batch sizes, affect the ML-HM accuracy. For this purpose, different scenarios for the above three factors were generated. ML-HMs were designed under these scenarios, and the performance was evaluated. The results showed that the simulated data distribution affects the ML-HM inversion accuracy, and it is better to use a priori knowledge to set the range and sampling strategy for the N-PROSAIL input variables to obtain a generated simulated data distribution that is similar to that of the measured data. The ML-HM accuracy increases with increasing measured sample size, but it does not change in an obvious manner once a certain threshold is reached. Thus, it is better to apply the sample size determination method based on simple random sampling to calculate the required sample size. The simulated and measured data batch sizes significantly affect the ML-HM accuracy, and we recommended creating a model for ML-HM accuracy prediction based on a certain number of batch size scenarios and using it to estimate suitable batch sizes of simulated and measured data to design an ML-HM. |
| WOS关键词 | RADIATIVE-TRANSFER ; AREA |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001161681500002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202802]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Pengfei |
| 作者单位 | Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Pengfei,Ma, Xiao. Optimal Strategy for Designing a Multitask Learning-Based Hybrid Model to Predict Wheat Leaf Nitrogen Content[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023,20:5. |
| APA | Chen, Pengfei,&Ma, Xiao.(2023).Optimal Strategy for Designing a Multitask Learning-Based Hybrid Model to Predict Wheat Leaf Nitrogen Content.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,20,5. |
| MLA | Chen, Pengfei,et al."Optimal Strategy for Designing a Multitask Learning-Based Hybrid Model to Predict Wheat Leaf Nitrogen Content".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20(2023):5. |
入库方式： OAI收割
来源：地理科学与资源研究所
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