土壤有机物质的超谱反转基于改进的集体学习方法
Junjie Liu1, Yongsheng Hong2, Bifeng Hu3
1College of Agriculture, Tarim University, Alar 843300, China.
概括
使用高光谱学优化土壤有机物 (SOM) 检测的整体模型,涉及改进基础学习者重量和计数. 一个多指数评估和 12 个基础学习者的堆叠方法显著提高了 SOM 检测的准确性.
科学领域:
- 土壤科学 土壤科学
- 遥感 遥感 遥感 遥感
- 频谱学是一种光谱学.
背景情况:
- 土壤有机物 (SOM) 对于土壤健康和碳封存至关重要.
- 超光谱学为SOM检测提供了一种高效且具有成本效益的方法.
- 集成模型 (EM) 在土壤光谱学中表现有前途,但需要优化基础学习者选择和权重.
研究的目的:
- 开发创新的基础学习者重量分配方法,用于SOM检测中的EM.
- 为了确定不同EM技术 (WA,混合,堆叠) 的最佳基础学习者数量.
- 为了提高EM的性能,使用Vis-NIR光谱来进行定量SOM评估.
主要方法:
- 在塔林河流域704个土壤样本上使用Vis-NIR光谱学.
- 研究了各种权重系数分配方法 (包括R2,RMSE,MAE) 和基本学习者数量.
- 在权重平均 (WA),混合和堆叠框架内评估EM性能.
主要成果:
- 多指数权重分配 (R2,RMSE,MAE) 显著改善了EM表现,而不是传统方法.
- 最佳基础学习者数量因组合技术而异;堆叠和混合达到12的峰值,WA在15的峰值.
- 堆叠证明了卓越的精度,达到R2的0.889,RMSE的0.957克公斤-1和MAE的0.803克公斤-1.1.
结论:
- 优化基础学习者数量和采用多指数综合评估来分配权重对于EM表现至关重要.
- 使用12个基础学习者和多指数加权的堆叠方法是SOM超谱反转的最佳策略.
- 这种精细的方法提高了通过超光谱检测土壤有机物检测的准确性和可靠性.
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