Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022
文献类型：期刊论文
| 作者 | Lu, Jiaying2,3; Yao, Ling1,3; Qin, Jun3; Jiang, Hou3; Zhou, Chenghu1,3 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:2473639 |
| 关键词 | Global drought aridity index climate change remote sensing ensemble learning |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2473639 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications. Due to the spatial heterogeneity of global climate and topography, there exist significant uncertainties of AI estimates in areas with sparse ground observations, and high-resolution global AI estimation remains a challenge. In this study, we propose an LSTM-based approach to model the nonlinear intra-annual relationship between satellite-derived data and AI and enhance model performance through ensemble learning by leveraging MODIS data at different observation times. A long-term annually gridded global AI dataset is generated at a resolution of 0.05 degrees x 0.05 degrees from 2003 to 2022. Validation against the Global Surface Summary of the Day database yields biases, root mean squared errors and coefficients from -0.04 to 0.02, 0.19 to 0.86, and 0.62 to 0.83 across different continents. Comparisons with AI estimates based on Climatic Research Unit or ERA5-Land datasets further demonstrate the high accuracy of our AI estimates. Preliminary analysis reveals a global wetting trend over the past two decades. This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. |
| URL标识 | 查看原文 |
| WOS关键词 | ABSOLUTE ERROR MAE ; POTENTIAL EVAPOTRANSPIRATION ; DROUGHT INDEXES ; CLIMATE-CHANGE ; TEMPERATURE ; CHINA ; VEGETATION ; DATASET ; MODIS ; WATER |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001437389200001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213192]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yao, Ling |
| 作者单位 | 1.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lu, Jiaying,Yao, Ling,Qin, Jun,et al. Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):2473639. |
| APA | Lu, Jiaying,Yao, Ling,Qin, Jun,Jiang, Hou,&Zhou, Chenghu.(2025).Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),2473639. |
| MLA | Lu, Jiaying,et al."Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):2473639. |
入库方式： OAI收割
来源：地理科学与资源研究所
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