Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model

Zhang Yangyang; Yang Jian; Liu Xiuguo; Du Lin; Shi Shuo; Sun Jia; Chen Biwu

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Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model

机译：不同回归算法在基于展望模型的基于所选特征波长的估计叶参数上的影响

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In this study, the characteristic wavelengths of leaf biochemical parameters (including carotenoid content, chlorophyll a + b content, dry matter content, equivalent water thickness, and leaf structure parameter) were obtained through a sensitivity analysis based on a physical model. Then, performance of the selected characteristic wavelengths for monitoring leaf biochemical contents (LBC) was analyzed by using the following six popular regression algorithms: random forest, backpropagation neural network, support vector regression, radial basic function neural network, partial least-squares regression, and Gaussian process regression of different parameter values/kernel functions/training functions. In addition, the optimal parameters of each regression algorithm for estimating LBC were determined. Lastly, the effect of different regression algorithms on the accuracy of LBC estimation using four different data sets was also discussed. The results demonstrated that the selected 10 characteristic wavelengths combined with the Gaussian process regression model can be efficiently applied in estimating LBC. (C) 2019 Optical Society of America

机译：在该研究中，通过基于物理模型的灵敏度分析，获得了通过基于物理模型的灵敏度分析获得了叶生化参数（包括类胡萝卜素含量，叶绿素A + B含量，干物质含量，等效水厚度和叶片结构参数）的特征波长。然后，通过使用以下六个流行回归算法分析了用于监测叶生化内容（LBC）的所选特征波长的性能：随机森林，背部衰退神经网络，支持向量回归，径向基本函数神经网络，部分最小二乘回归，和高斯进程对不同参数值/内核函数/训练函数的回归。另外，确定用于估计LBC的每个回归算法的最佳参数。最后，还讨论了不同回归算法对使用四种不同数据集的LBC估计精度的影响。结果表明，可以有效地应用于估计LBC的所选10个与高斯过程回归模型的特征波长。（c）2019年光学学会

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《Applied optics》 |2019年第36期|共10页
作者
Zhang Yangyang; Yang Jian; Liu Xiuguo; Du Lin; Shi Shuo; Sun Jia; Chen Biwu;
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作者单位

China Univ Geosci Sch Geog &

Informat Engn Wuhan 430074 Hubei Peoples R China;

China Univ Geosci Sch Geog &

Informat Engn Wuhan 430074 Hubei Peoples R China;

China Univ Geosci Sch Geog &

Informat Engn Wuhan 430074 Hubei Peoples R China;

China Univ Geosci Sch Geog &

Informat Engn Wuhan 430074 Hubei Peoples R China;

Wuhan Univ State Key Lab Infonnat Engn Surveying Mapping &

R Wuhan 430079 Hubei Peoples R China;

Wuhan Univ State Key Lab Infonnat Engn Surveying Mapping &

R Wuhan 430079 Hubei Peoples R China;

Wuhan Univ State Key Lab Infonnat Engn Surveying Mapping &

R Wuhan 430079 Hubei Peoples R China;

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正文语种 eng
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Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model

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