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Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments

机译：基于多年实验高光谱数据的机器学习方法估算水稻叶面积指数

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The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models’ key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
机译：在这项研究中，我们评估了三种机器学习方法（支持向量回归，随机森林和人工神经网络）用于估算水稻的LAI的性能。还评估了涉及优化带组合的窄带NDVI的传统单变量回归模型以及线性多元校正偏最小二乘回归模型，以进行比较。四年现场收集的数据集用于测试LAI估计模型针对时间变化的稳健性。分别在原始高光谱反射率和一阶导数上建立了偏最小二乘回归和三种机器学习方法。使用两个不同的规则来确定模型的关键参数。结果表明，红色边缘和NIR波段（766 nm和830 nm）的组合以及SWIR波段（1114 nm和1190 nm）的组合对于生产窄带NDVI是最佳的。与基于原始光谱的相应模型相比，基于一阶导数光谱的模型产生的结果更准确。正确选择的模型参数可得出与经验最优参数相当的准确性和鲁棒性，并显着降低了模型的复杂性。机器学习方法比VI方法和偏最小二乘回归更准确，更可靠。当针对独立的验证数据集验证校准模型时，VI方法得出的验证RMSE值对于NDVI（766,830）为1.01，对于NDVI（1114,1190）为1.01，而对于偏最小二乘的最佳模型，支持向量机和人工神经网络方法得出的验证RMSE值分别为0.84、0.82、0.67和0.84。建立在mtry = 10的一阶导数光谱上的RF模型显示出估算水稻LAI的最高潜力。

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