Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
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DOI：
10.2135/cropsci2017.01.0007
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发表时间：
2017-09-01
期刊：
影响因子：
2.3
通讯作者：
de los Campos, Gustavo
中科院分区：
文献类型：
--
作者：
Aguate, Fernando M.;Trachsel, Samuel;de los Campos, Gustavo
高光谱相机可以提供数百个波长的反射数据。这些信息可用于推导与农艺和生理性状相关的植被指数（维斯）。然而，高光谱相机生成的数据比VI中可以总结的数据更丰富。因此，在这项研究中，我们研究了使用高光谱图像数据的预测方程是否可以导致更好的预测性能比可以实现使用维斯的谷物产量。对于高光谱预测方程，我们考虑了三种估计方法：普通最小二乘法，偏最小二乘法（降维方法），贝叶斯收缩和变量选择程序。我们还研究了结合不同时间点收集的反射率数据的好处。通过CIMMYT在11个玉米（Zea mays L.）2014年在高温和干旱胁迫下进行的产量试验。我们的研究结果表明，使用62个波段的数据导致更高的预测精度比可以实现使用个人维斯。总的来说，收缩和变量选择方法是性能最好的方法。在使用单个时间点数据的模型中，使用开花后28 d收集的反射率的模型给出了最高的预测精度。与使用单个时间点数据相比，组合在多个时间点收集的图像数据导致预测准确性的增加。
Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.