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收稿：2017-07-29，
修回：2017-9-25，
纸质出版：2018-05-16
移动端阅览
目的
2
叶面积指数（LAI）是重要的植被生物理化参数，对农作物长势和产量预测具有重要研究意义。基于物理模型和经验模型的LAI估算方法被认为是当前最常用的方法，但两种方法的估算效率和精度有限。近年来，机器学习算法在遥感监测领域广泛应用，算法具有描述非线性数据拟合、融合更多辅助信息的能力，为了评价机器学习算法在玉米LAI遥感估算中的适用性，本文分析比较了随机森林和BP神经网络算法估算玉米LAI的能力，并与传统经验模型进行了比较。
方法
2
以河北省怀来县东花园镇为研究区，基于野外实测玉米LAI数据，结合同时期国产高分卫星（GF1-WFV影像），首先分析了8种植被指数与LAI的相关性，进而采用保留交叉验证的方式将所有样本数据分为两部分，65%的数据作为模型训练集，35%作为验证集，重复随机分为3组，构建以8种植被指数为自变量，对应LAI值为因变量的RF模型、BP神经网络模型及传统经验模型。采用决定系数
$$ R^2$$
和均方根误差（RMSE）作为模型评价指标。
结果
2
8种植被指数与LAI的相关性分析表明所有样本数据中，实测LAI值与各植被指数均在（
$$ P$$
＜0.01）水平下极显著相关，且相关系数均高于0.5；将3组不同样本数据在随机森林、BP神经网络算法中多次训练，并基于验证数据集进行估算精度检验，经验模型采用训练数据集建模，验证数据集检验，结果表明，RF模型表现出了较强的预测能力，LAI预测值与实测值
$$ R^2$$
分别为0.681、0.757、0.701，均高于BP模型（0.504、0.589、0.605）和经验模型（0.492、0.557、0.531），对应RMSE分别为0.264、0.292、0.259；均低于BP模型（0.284、0.410、0.283）和经验模型（0.541、0.398、0.306）。
结论
2
研究表明，RF算法能更好地进行玉米LAI遥感估算，为快速准确进行农作物LAI遥感监测提供了技术参考。
Objective
2
Leaf area index (LAI) is an important biological and physical parameter of vegetation
and it plays an important role in predicting crop growth and yield. A number of LAI estimation methods have been developed from remotely sensed data
each of which presents unique advantages and limitations. The empirical regression and physical models are the most widely used among these methods. The empirical regression model can reduce the effect of background noise on the spectral reflectance of plant canopies
and the physical model simulates the radiative transfer process in vegetation and describes the canopy spectral variation as a function of canopy
leaf
and soil background characteristics. However
the efficiency and accuracy of the two methods are limited. In recent years
machine learning algorithms have been widely used in remote sensing monitoring
and they can describe nonlinear data fitting and fuse more auxiliary information. This study evaluates the applicability of machine learning algorithms in maize LAI remote sensing estimation.
Method
2
In this study
the east garden of Huailai County in Hebei Province was used as the study area. Eight kinds of vegetation indices based on the GF1 WFV satellite images were calculated
and the correlation between the same-period measured LAI and the vegetation index was analyzed. Then
all the in situ measured corn LAI and corresponding eight vegetation indices were randomly divided into a training dataset and an independent model validation dataset (65% and 35% of the data
respectively). These datasets were randomly divided into three groups repeated three times. The training dataset was used to establish models to predict corn LAI
and the validation dataset was employed to test the quality of each prediction model. Finally
utilizing random forest
backpropagation (BP) neural network algorithm
and the traditional empirical model
the LAI inverting model was established based on previous work. This study compared the estimation accuracy of the three models for each sample group on the basis of the coefficient of determination (
$$ R^2$$
) and root mean square error (RMSE) to evaluate the estimation accuracy of each model and to compare the performances of the three models further.
Result
2
Results showed that the LAI values were significantly correlated with the vegetation index at the
$$ P$$
<
0.01 level in all the sample data and that the correlation coefficients were higher than 0.5. Three groups of different sample data were trained in random forest and BP neural network for many times
and the accuracy of estimation was checked based on the validation dataset. The empirical model was established by training dataset and verified by validation dataset. The results show that the RF model outperformed BP and the traditional empirical model in each group of sample data. For the RF models
$$ R^2$$
of the estimated and measured LAI values were 0.681
0.757
and 0.701 in contrast to the RMSE of 0.264
0.292
and 0.259
respectively. For the BP model
$$ R^2$$
for the three groups was 0.504
0.589
and 0.605
and the corresponding RMSE was 0.284
0.410
and 0.283
respectively. However
for the traditional empirical model
$$ R^2$$
for the three groups was 0.492
0.557
and 0.531
and the corresponding RMSE was 0.541
0.398
and 0.306
respectively.
Conclusion
2
The RF algorithm provides an effective approach to improve the prediction accuracy of corn LAI and provides a technical reference for the rapid and accurate monitoring of crop LAI remote sensing.
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