草业学报 ›› 2022, Vol. 31 ›› Issue (12): 41-51.DOI: 10.11686/cyxb2021468
收稿日期:
2021-12-13
修回日期:
2022-01-28
出版日期:
2022-12-20
发布日期:
2022-10-17
通讯作者:
乔宝晋
作者简介:
E-mail: qiaobaojin@zzu.edu.cn基金资助:
Heng-liang GUO1(), Xiao LI2, Yu FU3, Bao-jin QIAO2()
Received:
2021-12-13
Revised:
2022-01-28
Online:
2022-12-20
Published:
2022-10-17
Contact:
Bao-jin QIAO
摘要:
准确估算叶面积指数(LAI)在生态、环境和气候变化研究方面具有重要作用。依靠卫星遥感技术能够获取大范围LAI产品,但其空间分辨率较低且依赖大量地面实测数据,难以满足高精度、大范围研究的需求。本研究基于30 m空间分辨率地表反射率数据,在不依赖大量地面实测数据的情况下,提出基于核岭回归算法的PROSAIL物理模型反演LAI,首先对PROSAIL模型的输入参数进行敏感性分析,以确定输入参数并生成模拟数据集,从而建立模拟反射率与LAI之间的核岭回归反演模型,进行高空间分辨率LAI反演,并与基于多层感知机的PROSAIL模型、基于随机森林回归的PROSAIL模型进行对比分析。结果表明:基于核岭回归的PROSAIL模型获得了最高的LAI反演精度,模型决定系数(
郭恒亮, 李晓, 付羽, 乔宝晋. 基于核岭回归算法的PROSAIL模型反演高空间分辨率叶面积指数[J]. 草业学报, 2022, 31(12): 41-51.
Heng-liang GUO, Xiao LI, Yu FU, Bao-jin QIAO. High-resolution leaf area index inversion based on the Kernel Ridge Regression algorithm and PROSAIL model[J]. Acta Prataculturae Sinica, 2022, 31(12): 41-51.
模型输入参数Model input parameter | 取值范围Value range |
---|---|
叶面积指数Leaf area index (LAI) | 0.01~3.00 |
叶绿素含量Chlorophyll content (Cab) | 10~80 |
平均叶倾角Average leaf angle (ALA) | 20~70 |
水分含量Water content (Cw) | 0.004~0.040 |
叶片结构参数Leaf structure parameters (N) | 1~2 |
土壤湿度Soil moisture (Psoil) | 0.1~1.0 |
表1 PROSAIL模型参数取值范围
Table 1 Parameter ranges of PROSAIL model
模型输入参数Model input parameter | 取值范围Value range |
---|---|
叶面积指数Leaf area index (LAI) | 0.01~3.00 |
叶绿素含量Chlorophyll content (Cab) | 10~80 |
平均叶倾角Average leaf angle (ALA) | 20~70 |
水分含量Water content (Cw) | 0.004~0.040 |
叶片结构参数Leaf structure parameters (N) | 1~2 |
土壤湿度Soil moisture (Psoil) | 0.1~1.0 |
非参数回归模型 Non-parametric regression model | 训练精度Training accuracy | 预测精度Prediction accuracy | ||
---|---|---|---|---|
决定系数 | 均方根误差RMSE | 决定系数 | 均方根误差RMSE | |
多层感知机Multilayer perceptron | 0.8014 | 0.2711 | 0.7921 | 0.2570 |
随机森林回归Random forest regression | 0.9675 | 0.1142 | 0.7893 | 0.2582 |
核岭回归Kernel ridge regression | 0.7879 | 0.2805 | 0.7878 | 0.2593 |
表2 反演模型构建精度对比
Table 2 Comparison of inversion model construction accuracy
非参数回归模型 Non-parametric regression model | 训练精度Training accuracy | 预测精度Prediction accuracy | ||
---|---|---|---|---|
决定系数 | 均方根误差RMSE | 决定系数 | 均方根误差RMSE | |
多层感知机Multilayer perceptron | 0.8014 | 0.2711 | 0.7921 | 0.2570 |
随机森林回归Random forest regression | 0.9675 | 0.1142 | 0.7893 | 0.2582 |
核岭回归Kernel ridge regression | 0.7879 | 0.2805 | 0.7878 | 0.2593 |
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