Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau
As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made...
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2025-08-01
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| author | Junpo Yu Yajun Si Wen Zhao Zeyu Zhou Jiming Jin Wenjun Yan Xiangyu Shao Zhixiang Xu Junwei Gan |
| author_facet | Junpo Yu Yajun Si Wen Zhao Zeyu Zhou Jiming Jin Wenjun Yan Xiangyu Shao Zhixiang Xu Junwei Gan |
| author_sort | Junpo Yu |
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| description | As the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-0064d489ab274115b539fda48a49e8ca2025-08-20T03:36:22ZengMDPI AGPlants2223-77472025-08-011415239110.3390/plants14152391Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess PlateauJunpo Yu0Yajun Si1Wen Zhao2Zeyu Zhou3Jiming Jin4Wenjun Yan5Xiangyu Shao6Zhixiang Xu7Junwei Gan8College of Resources and Environment, Yangtze University, Wuhan 430100, ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, ChinaLanzhou Institute of Arid Meteorology, China Meteorological Administration/Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climate Change and Reducing Disaster, China Meteorological Administration, Lanzhou 730020, ChinaCollege of Resources and Environment, Yangtze University, Wuhan 430100, ChinaCollege of Resources and Environment, Yangtze University, Wuhan 430100, ChinaLanzhou Institute of Arid Meteorology, China Meteorological Administration/Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climate Change and Reducing Disaster, China Meteorological Administration, Lanzhou 730020, ChinaCollege of Resources and Environment, Yangtze University, Wuhan 430100, ChinaCollege of Resources and Environment, Yangtze University, Wuhan 430100, ChinaCollege of Resources and Environment, Yangtze University, Wuhan 430100, ChinaAs the world’s largest loess deposit region, the Loess Plateau’s vegetation dynamics are crucial for its regional water–heat balance and ecosystem functioning. Leaf Area Index (LAI) serves as a key indicator bridging canopy architecture and plant physiological activities. Existing studies have made significant advancements in simulating LAI, yet accurate LAI simulation remains challenging. To address this challenge and gain deeper insights into the environmental controls of LAI, this study aims to accurately simulate LAI in the Loess Plateau using deep learning models and to elucidate the spatiotemporal influence of soil moisture and temperature on LAI dynamics. For this purpose, we used three deep learning models, namely Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Interpretable Multivariable (IMV)-LSTM, to simulate LAI in the Loess Plateau, only using soil moisture and temperature as inputs. Results indicated that our approach outperformed traditional models and effectively captured LAI variations across different vegetation types. The attention analysis revealed that soil moisture mainly influenced LAI in the arid northwest and temperature was the predominant effect in the humid southeast. Seasonally, soil moisture was crucial in spring and summer, notably in grasslands and croplands, whereas temperature dominated in autumn and winter. Notably, forests had the longest temperature-sensitive periods. As LAI increased, soil moisture became more influential, and at peak LAI, both factors exerted varying controls on different vegetation types. These findings demonstrated the strength of deep learning for simulating vegetation–climate interactions and provided insights into hydrothermal regulation mechanisms in semiarid regions.https://www.mdpi.com/2223-7747/14/15/2391LAIdeep learningwater–heat couplingvegetation dynamics simulationLoess Plateau |
| spellingShingle | Junpo Yu Yajun Si Wen Zhao Zeyu Zhou Jiming Jin Wenjun Yan Xiangyu Shao Zhixiang Xu Junwei Gan Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau Plants LAI deep learning water–heat coupling vegetation dynamics simulation Loess Plateau |
| title | Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau |
| title_full | Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau |
| title_fullStr | Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau |
| title_full_unstemmed | Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau |
| title_short | Comparison of Deep Learning Models for LAI Simulation and Interpretable Hydrothermal Coupling in the Loess Plateau |
| title_sort | comparison of deep learning models for lai simulation and interpretable hydrothermal coupling in the loess plateau |
| topic | LAI deep learning water–heat coupling vegetation dynamics simulation Loess Plateau |
| url | https://www.mdpi.com/2223-7747/14/15/2391 |
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