一种基于悬浮颗粒物分类和不同的机器学习的新型 - 检索模型
Chong Fang1, Changchun Song2, Zhidan Wen1
1Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
Environmental research
|October 22, 2023
概括
根据悬浮颗粒物 (SPM) 度对内陆水域进行分类,可显著改善使用卫星数据的甲基 (Chla) 估计. 随机森林回归模型实现了高精度,为水生生态系统监测提供了有价值的工具.
科学领域:
- 对内陆水生生态系统的遥感.
- 使用卫星数据进行水质评估.
- 机器学习在环境科学中的应用.
背景情况:
- 叶绿素-a (Chla) 是评估内陆水的健康状况和启用早期藻类繁殖警告的关键光学参数.
- MOD09卫星产品提供高时间和空间分辨率,对于水色遥感至关重要.
- 准确的Chla度检索对于有效的水生生态系统管理至关重要.
研究的目的:
- 开发一个高精度的机器学习模型,用MOD09产品估计内陆水中的Chla度.
- 调查根据悬浮颗粒物 (SPM) 度对水体进行分类是否可以提高Chla检索的准确性.
- 为了比较10个常见的机器学习模型对Chla估计的性能.
主要方法:
- 开发了一种用于Chla估计的机器学习模型,采用了一种基于SPM度对水体进行分类的新方法.
- 评估了十种机器学习模型,包括随机森林回归器 (RFR),深度神经网络 (DNN),极端梯度增强 (XGBoost) 和卷积神经网络 (CNN).
- 过了41个基本频段和820个频段比,根据它们与Ln (Chla) 的相关性,并选择了模型输入的频段. 识别了用于0.9准确度的SPM分类的特定频段 (B3,B20,B32).
主要成果:
- 根据SPM度对水体进行分类,显著改善了光谱带/比率和Ln(Chla之间的相关性,将模型验证R2从0.41提高到0.83.
- 随机森林回归器 (RFR) 模型表现出卓越的性能,由较高的R2,较低的RMSE和较低的MAPE表明.
- 波段B3显示,在不同的SPM分类组中,对Chla估计的贡献最高.
结论:
- SPM分类是提高内陆水域基于卫星的Chla度检索准确性的有效策略.
- 使用选定的光谱频段的RFR模型为Chla估计提供了强大而准确的方法.
- 开发的模型显示出在其他内陆水体中应用的巨大潜力,并作为未来研究的宝贵参考.
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