Yu Junpo, Si Yajun, Zhao Wen, Zhou Zeyu, Jin Jiming, Yan Wenjun, Shao Xiangyu, Xu Zhixiang, Gan Junwei
College of Resources and Environment, Yangtze University, Wuhan 430100, China.
College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China.
Plants (Basel). 2025 Aug 2;14(15):2391. doi: 10.3390/plants14152391.
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.
作为世界上最大的黄土沉积区，黄土高原的植被动态对其区域水热平衡和生态系统功能至关重要。叶面积指数（LAI）是连接冠层结构和植物生理活动的关键指标。现有研究在模拟LAI方面取得了显著进展，但准确模拟LAI仍然具有挑战性。为应对这一挑战并更深入了解LAI的环境控制因素，本研究旨在使用深度学习模型准确模拟黄土高原的LAI，并阐明土壤水分和温度对LAI动态的时空影响。为此，我们使用了三种深度学习模型，即人工神经网络（ANN）、长短期记忆网络（LSTM）和可解释多变量（IMV）-LSTM，仅以土壤水分和温度作为输入来模拟黄土高原的LAI。结果表明，我们的方法优于传统模型，并有效捕捉了不同植被类型间的LAI变化。注意力分析显示，土壤水分主要影响干旱的西北地区的LAI，而温度则是湿润的东南地区的主要影响因素。在季节方面，土壤水分在春季和夏季至关重要，尤其是在草原和农田，而温度在秋季和冬季起主导作用。值得注意的是，森林的温度敏感时期最长。随着LAI增加，土壤水分的影响变得更大，在LAI峰值时，这两个因素对不同植被类型施加了不同的控制。这些发现证明了深度学习在模拟植被-气候相互作用方面的优势，并为半干旱地区的水热调节机制提供了见解。
