在亚马逊洪水平原湖泊中,二十年来洪水脉冲驱动的叶绿素a动态
Ana Clara de Lara Maia1, Anna Beatriz Silva Alves1, Hulair Braga Carneiro1
1Environmental Engineering Department, São Paulo State University (UNESP), São José dos Campos, SP, Brazil.
The Science of the total environment
|January 8, 2026
概括
我们开发了一种机器学习模型,使用卫星数据跟踪亚马逊洪水平原湖泊中的叶绿素a. 这种方法使得这些动态生态系统的长期水质监测成为可能.
科学领域:
- 水生生态学 水生生态学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 亚马逊泛滥平原的湖泊在全球范围内具有重要意义,但由于水文变化,度和光学复杂性,其监测具有挑战性.
- 有效的监测对于了解亚马逊洪水脉冲和人类压力等因素影响的水质动态至关重要.
研究的目的:
- 为了研究亚马逊大洪水平原湖Lago Grande de Curuai的叶绿素-a (Chl-a) 动态.
- 开发和验证可扩展的机器学习模型,用于使用卫星数据进行长期Chl-a监测.
主要方法:
- 与MODIS表面反射率数据相结合的现场测量 (2001-2024年).
- 采用机器学习框架,优化支持向量回归 (SVR_Optuna),并利用可解释的人工智能 (XAI) 来进行模型解释.
- 使用R2和RMSE验证模型性能,并评估像素智能的不确定性指标.
主要成果:
- 优化的SVR在预测Chl-a方面取得了很高的准确性 (R2 = 0.855).
- 重建了24年的Chl-a变化,显示在退缩和低水阶段的季节性最大值.
- XAI证实了红色和红边光谱特征对Chl-a预测的重要性.
结论:
- 开发的机器学习方法提供了一种可扩展的方法,用于在亚马逊泛滥平原湖中系统地监测Chl-a.
- 这种工具对于评估水质变化,以应对水文连接,干旱和人类影响至关重要.
- 更好地了解Chl-a动力学有助于管理这些脆弱的水生生态系统.
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