使用机器学习和超光谱数据,精确估计在不同水应力水平下作物含水量
Emre Tunca1, Eyüp Selim Köksal2, Elif Öztürk3
1Department of Biosystem Engineering, Faculty of Agriculture, Düzce University, Düzce, Turkey. emretunca@duzce.edu.tr.
Environmental monitoring and assessment
|June 23, 2023
概括
这项研究表明,机器学习模型使用水应力下的光谱数据准确估计作物含水量 (CWC). 随机森林和XGBoost模型表现最好,帮助精准农业.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 水应激显著影响作物生理学和光谱反射率.
- 准确估计作物含水量 (CWC) 对精准农业和水资源管理至关重要.
- 超光谱数据为作物监测提供了详细的光谱信息.
研究的目的:
- 研究水应激对子光谱信息和叶面积指数 (LAI) 的影响.
- 评估三种机器学习 (ML) 算法 (XGBoost,RF,SVM) 的性能,用于使用超光谱数据估计CWC.
- 为了确定最佳的光谱特征,并评估植被指数 (VIs) 对CWC估计的有用性.
主要方法:
- 收集了在不同水应激水平下对的高光谱测量和LAI数据.
- 应用递归特征消除 (RFE) 进行最佳波长选择和主要组件分析 (PCA) 进行维度减小.
- 经过训练和验证的极端梯度提升 (XGBoost),随机森林 (RF) 和支向量机 (SVM) 回归模型来估计CWC.
主要成果:
- 光谱反射率和LAI因生长阶段和水应力而异,每次处理都显示出一致的模式.
- 使用选定的波长,RF模型实现了R2=0.90;XGBoost在PCA减小的数据中表现出色 (r=0.96,RMSE=45.77).
- 某些植被指数 (例如,CL_Rededge,EVI) 在CWC估计中比其他指数 (例如,NDVI,MSAVI) 更有效.
结论:
- 机器学习算法,特别是XGBoost与PCA,为提供准确可靠的CWC估计.
- 频谱信息和LAI是糖果水压的敏感指标.
- 这些发现支持在精准农业中应用超谱遥感和ML,以优化灌和资源管理.
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