深度学习模型的比较,用于LAI模拟和可解释的热水电合在洛斯高原
Junpo Yu1, Yajun Si2, Wen Zhao3
1College of Resources and Environment, Yangtze University, Wuhan 430100, China.
Plants (Basel, Switzerland)
|August 14, 2025
概括
深度学习模型使用土壤水分和温度准确模拟洛斯高原的叶面积指数 (LAI). 结果揭示了植被动态的热水调节的空间和季节性变化.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 洛斯高原的植被动态对于区域生态系统的运行和水热平衡至关重要.
- 叶面积指数 (LAI) 是植被健康的关键指标,但准确的模拟仍然具有挑战性.
- 了解LAI的环境控制对于有效的土地管理至关重要.
研究的目的:
- 通过使用深度学习模型,准确地模拟Loess高原的LAI.
- 研究土壤湿度和温度对LAI动态的时空影响.
- 阐明半干旱生态系统中的热水调节机制.
主要方法:
- 使用人工神经网络 (ANN),长期短期记忆 (LSTM) 和可解释多变量 (IMV) -LSTM深度学习模型.
- 输入数据仅包括LAI模拟的土壤湿度和温度.
- 使用注意力分析来确定LAI变化的关键环境驱动因素.
主要成果:
- 深度学习模型在捕捉不同植被类型的LAI变化方面表现优于传统方法.
- 土壤湿度主要影响干旱地区 (西北) 的LAI,而湿地区 (东南) 的温度占主导地位.
- 季节性分析显示,春季/夏季 (草原,耕地) 土壤水分的重要性,秋季/冬季温度的重要性;森林表现出长期的温度敏感性.
结论:
- 深度学习模型有效地模拟了植被气候相互作用,为水热调节提供了洞察力.
- 结果突出了土壤水分和温度对LAI在不同地区,季节和植被类型的不同影响.
- 该研究为了解和预测半干旱环境中植被对气候因素的反应提供了一个强大的框架.
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