Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022
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...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2473639 |
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| _version_ | 1849224266946445312 |
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| author | Jiaying Lu Ling Yao Jun Qin Hou Jiang Chenghu Zhou |
| author_facet | Jiaying Lu Ling Yao Jun Qin Hou Jiang Chenghu Zhou |
| author_sort | Jiaying Lu |
| collection | DOAJ |
| description | 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° × 0.05° 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. |
| format | Article |
| id | doaj-art-0e5ffb690127438ca5f1dc2edc13dbb1 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-0e5ffb690127438ca5f1dc2edc13dbb12025-08-25T11:28:49ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2473639Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022Jiaying Lu0Ling Yao1Jun Qin2Hou Jiang3Chenghu Zhou4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAridity 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° × 0.05° 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.https://www.tandfonline.com/doi/10.1080/17538947.2025.2473639Global droughtaridity indexclimate changeremote sensingensemble learning |
| spellingShingle | Jiaying Lu Ling Yao Jun Qin Hou Jiang Chenghu Zhou Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 International Journal of Digital Earth Global drought aridity index climate change remote sensing ensemble learning |
| title | Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| title_full | Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| title_fullStr | Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| title_full_unstemmed | Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| title_short | Global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| title_sort | global reconstruction of gridded aridity index and its spatial and temporal characterization from 2003 to 2022 |
| topic | Global drought aridity index climate change remote sensing ensemble learning |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2473639 |
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