Explainable deep learning insights into the history and future of net primary productivity in China
文献类型：期刊论文
| 作者 | Liu, Nanjian1,2; Hao, Zhixin1,2; Zhao, Peng2,3 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 166页码:112394 |
| 关键词 | Net primary productivity Deep learning Interpretability Driving factors Climate change |
| DOI | 10.1016/j.ecolind.2024.112394 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Net primary productivity (NPP) is a crucial component of the terrestrial carbon cycle and plays a crucial role in assessing ecological security. Although machine learning models have been widely used in research on ecosystem modelling, however, the performance of deep learning models with interpretability in predicting NPP are still very limited. In this study, we developed an interpretable deep learning predictive model to uncover the historical drivers and future changes of NPP in China using remote sensing data, climate data and topography. The results show that more than three quarters (88 %) of China's regional NPP have shown an increasing trend over the past 20 years, with an overall trend of 2.46 gC/m2/yr. This extensive range of dynamic processes is a nonlinear response to climatic conditions and topographic factors. The constructed convolutional neural network (CNN) model showed good predictive skill for historical NPP, and revealed that temperature is the main controlling factor, highlighting its importance in vegetation growth in China. Compared to the historical period (2001-2020), in the context of future climate change, the risk of carbon sinks decreasing in China is highly likely in the imminent near term (2021-2039), especially under high emission scenarios. These results suggest that interpretable deep learning method is a useful tool for revealing vegetation productivity drivers and estimating vegetation productivity under a nonstationary climate background. |
| WOS关键词 | NPP ; EMISSIONS ; SATELLITE |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001286576400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206959]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Hao, Zhixin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, State Key Lab Mt Hazards & Engn Safety, Chengdu 610299, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Nanjian,Hao, Zhixin,Zhao, Peng. Explainable deep learning insights into the history and future of net primary productivity in China[J]. ECOLOGICAL INDICATORS,2024,166:112394. |
| APA | Liu, Nanjian,Hao, Zhixin,&Zhao, Peng.(2024).Explainable deep learning insights into the history and future of net primary productivity in China.ECOLOGICAL INDICATORS,166,112394. |
| MLA | Liu, Nanjian,et al."Explainable deep learning insights into the history and future of net primary productivity in China".ECOLOGICAL INDICATORS 166(2024):112394. |
入库方式： OAI收割
来源：地理科学与资源研究所
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