改进了多变量建模,用于估计土壤有机物质含量,使用超光谱指数和特征带
Ming-Song Zhao1,2,3, Tao Wang1,2,3, Yuanyuan Lu4,5
1School of Geomatics, Anhui University of Science and Technology, Huainan, Anhui, 232001, China.
PloS one
|June 14, 2023
概括
这项研究发现,使用竞争性适应性重权取样 (CARS) 选择的特征频段比光谱指数更提高了土壤有机物 (SOM) 预测准确度. 最好的模型,CARS-CR-SVR,在预测SOM内容方面取得了很高的准确性.
科学领域:
- 土壤科学 土壤科学
- 遥感 遥感 遥感 遥感
- 数据分析 数据分析
背景情况:
- 土壤有机物 (SOM) 对土壤肥力和农业生产力至关重要.
- 超频谱数据提供了丰富的信息,但需要处理以减少冗余并提高预测准确性.
- 频谱指数和特征带是超频谱分析中特征提取的常见方法.
研究的目的:
- 为了比较CARS选择的光谱指数 (SI) 和特征带在改善土壤有机物 (SOM) 预测模型中的有效性.
- 评估不同的光谱转换技术和用于SOM预测的机器学习算法.
- 通过使用超光谱数据确定用于准确SOM估计的最佳模型.
主要方法:
- 采集了178个表土样本,并测量了可见和近红外 (VNIR) 反射频谱.
- 应用的光谱转换 (LR,CR,FDR) 和计算的最佳光谱指数.
- 使用CARS算法选择的特征带.
- 使用随机森林,SVR,DNN和PLSR开发了SOM预测模型,具有SI和CARS特征.
主要成果:
- 基于SI的模型准确地预测了SOM (R2: 0.80-0.87,RPD: 2.14-2.52).
- 基于CARS的模型通常显示出更高的准确性,CARS-CR-SVR模型实现了最佳性能 (R2: 0.92,RMSE: 1.91 g/kg,RPD: 3.23).
- 模型的准确性随着光谱转换而显著变化,基于CARS的模型总体上优于基于SI的模型.
结论:
- 使用CARS的特征带选择比光谱指数更有效地提高了从超光谱数据中SOM预测的准确性.
- 汽车-CR-SVR模型在SOM估计方面表现出卓越的性能.
- 频谱转换和建模方法显著影响预测的准确性,强调了仔细处理数据和选择模型的重要性.
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