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一种混合模型,通过整合多个环境因素来完善毛初级生产率估计
Zhilong Li1,2, Ziti Jiao1,2,3, Zheyou Tan1,2
1State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China.
MethodsX
|January 1, 2025
概括
本研究引入了一种混合模型,将基于过程和数据的方法结合起来,以改进总初级生产率 (GPP) 估计. 通过整合环境压力因素和季节性树冠结构,该模型提高了全球GPP预测的准确性.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 估计总初级生产率 (GPP) 受到挑战环境因素的复杂性在光使用效率 (LUE) 模型.
- 现有的物理公式在最大LUE (εmax) 上难以捕捉各种环境约束.
研究的目的:
- 开发一个混合模型,整合生态压力因素和季节性树冠结构,以改进GPP估计.
- 通过结合机器学习技术,提高光使用效率模型的准确性.
主要方法:
- 开发了一种混合模型 (TL-CRF),将双叶LUE (TL-LUE) 模型与随机森林 (RF) 技术相结合.
- 季节性聚合指数 (CI) 变化在不同的植被类型和叶子生命周期阶段的全球估计.
- 在TL-LUE模型中使用RF子模块集成了生态压力因素.
主要成果:
- 该TL-CRF模型有效地纳入复杂的环境变量,以将理论 εmax 扩展到实际 ε.
- 混合方法利用了基于过程和数据驱动的模型的优势.
- 在全球GPP估计中取得了显著的改进.
结论:
- 拟议的TL-CRF模型为全球GPP估计提供了更强大的方法.
- 将机器学习与基于过程的模型集成,可以增强生态建模能力.
- 准确的GPP估计对于了解碳循环和气候变化影响至关重要.
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