Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees
文献类型：期刊论文
| 作者 | Gao, Chengxi; Wang, Huanjiong; Ge, Quansheng |
| 刊名 | AGRICULTURAL AND FOREST METEOROLOGY
 |
| 出版日期 | 2023-09-15 |
| 卷号 | 340页码:109623 |
| 关键词 | Leaf senescence date Autumn phenology Process-based model Machine learning Climate change |
| ISSN号 | 0168-1923 |
| DOI | 10.1016/j.agrformet.2023.109623 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Predicting tree phenology accurately is essential for assessing the impact of climate change on ecosystems. However, the current process-based models are still hard to fully explain and quantify the effects of multiple biotic and environmental factors (e.g., spring phenology, local adaptation, productivity, climatic variables) on autumn phenology. Using leaf senescence data (1980-2015) of 232766 site-year records (3020 sites and 6 deciduous tree species) in Europe, we first calibrated and evaluated 4 process-based models developed by previous studies. Subsequently, by using different machine learning (ML) algorithms (RF, EBM, and GAMI-Net), we developed 3 ML-based models for predicting the leaf senescence date of the same species and quantifying the importance and response function of 63 biotic or environmental variables. We found that the root mean square error (RMSE) of process-based models (averaged from all species) for the test dataset ranged from 11.97 to 12.91 days. The ML-based models outperformed process-based models for all species, with RMSE ranging from 10.01 to 10.58 days. For most species, the recently developed ML algorithms (EBM and GAMI-Net) are more effective than the classical RF algorithm developed in the early 21th century. Besides the temperature and photoperiod in autumn, the geographic factors (especially elevation, longitude, and latitude) were identified as the most important variables in the ML-based models, implying that leaf senescence date is an adaptive trait. Furthermore, for most species investigated, earlier leaf-out dates tended to advance the leaf senescence date. Our results highlight that the ML algorithms not only could effectively improve the performance of the process-based models for predicting the leaf senescence date, but also help to understand the nonlinear and interactive effects of multiple driving factors on autumn phenology. |
| WOS关键词 | GROWTH CESSATION ; CLIMATE-CHANGE ; DORMANCY INDUCTION ; PHENOLOGY ; TEMPERATURE ; AUTUMN ; PHOTOPERIOD ; SEASON ; MODEL ; DIFFERENTIATION |
| WOS研究方向 | Agriculture ; Forestry ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001046742100001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194504]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Institute of Geographic Sciences & Natural Resources Research, CAS 2.Chinese Academy of Sciences 3.University of Chinese Academy of Sciences, CAS |
| 推荐引用方式 GB/T 7714 | Gao, Chengxi,Wang, Huanjiong,Ge, Quansheng. Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees[J]. AGRICULTURAL AND FOREST METEOROLOGY,2023,340:109623. |
| APA | Gao, Chengxi,Wang, Huanjiong,&Ge, Quansheng.(2023).Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees.AGRICULTURAL AND FOREST METEOROLOGY,340,109623. |
| MLA | Gao, Chengxi,et al."Interpretable machine learning algorithms to predict leaf senescence date of deciduous trees".AGRICULTURAL AND FOREST METEOROLOGY 340(2023):109623. |
入库方式： OAI收割
来源：地理科学与资源研究所
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