基于无人机多时相植被指数的冬小麦产量估测

通过无人机搭载多光谱相机,对不同水分亏缺条件下冬小麦多个生育期进行遥感监测,采用不同种类多光谱植被指数表征冬小麦的生长特征,分析了植被指数与冬小麦产量的相关关系,并利用多时相植被指数构建产量估测数据集,采用偏最小二乘回归、支持向量机回归和随机森林回归3种机器学习算法进行冬小麦产量估测。结果表明,随着冬小麦的生长,多个植被指数与产量的相关性不断增强,灌浆末期相关系数达到0.7,植被指数与产量的线性回归决定系数也达到最大。多时相植被指数反映了冬小麦生长的变化特征,进一步提高了冬小麦产量估测精度,采用开花期和灌浆初期的多时相植被指数进行估产比采用单个生育期的植被指数估测产量的精度高,采用偏最小二乘回归模型的估测精度R²提高约0.021,支持向量机回归模型R²提高约0.015,随机森林回归模型R²提高约0.051。采用灌浆末期的多时相植被指数,3种模型均有较高的估测精度,偏最小二乘回归模型估测精度最高时的R²、RMSE分别为0.459、1 822.746 kg/hm²,支持向量机回归模型估测精度最高时的R²、RMSE分别为0.540、1 676.520 kg/hm²,随机森林回归模型估测精度最高时的R²、RMSE分别为0.560、1 633.896 kg/hm²,本文数据集训练的随机森林回归模型估测精度最高,且稳定性更好。

Grain Yield Prediction of Winter Wheat Using Multi-temporal UAV Based on Multispectral Vegetation Index

Timely and accurate crop monitoring and grain yield prediction before harvest of winter wheat are helpful for accurate farmland management and decision-making.Aiming to explore the potential of multi-temporal vegetation indices(VIs)extracted from unmanned aerial vehicle(UAV)based multispectral images in the whole growth period of winter wheat and improve the grain yield prediction,a UAV platform carrying multispectral camera was employed to collect the high resolution images of the whole growth period of winter wheat under different water deficit states.Different kinds of multispectral VIs were used to characterize the growth characteristics of winter wheat and the correlations between VIs and winter wheat grain yield were analyzed.The multi-temporal VIs were collected to form the data set,which was used to train the machine learning algorithm.Three algorithms,including partial least squares regression(PLSR),support vector regression(SVR)and random forest regression(RFR)were used to predict the grain yield of winter wheat.The results showed that with the growth of winter wheat,the leaf area index(LAI)was changed basically as parabolic,indicating the useful of MTVI2 in remote sensing retrieval of LAI.Meanwhile,the correlation coefficient between multiple VIs and grain yield was continually increased to 0.7 at the end of the filling stage.The linear regression determination coefficient(R²)between VIs and grain yield also reached the maximum.Moreover,the accuracy of VIs forecasting grain yield was also continuously improved,because of the multi-temporal VIs reflecting the changing characteristics of winter wheat growth.The multi-temporal VIs at the flowering and early stage of filling had higher accuracy than the VIs at a single growth period.For instance,the R² of PLSR was increased by about 0.021 and the R² of SVR was increased by about 0.015 and the R² of RFR was increased by about 0.051.For the multi-temporal vegetation index at the end of filling stage,different models had high estimation accuracy.The highest R² and RMSE of PLSR were 0.459 and 1822.746 kg/hm²,the highest R² and RMSE of SVR were 0.540 and 1676.520 kg/hm² and the highest R² and RMSE of RFR were 0.560 and 1633.896 kg/hm²,respectively.So the RFR trained in this data set had the highest estimation accuracy and better stability.These findings demonstrated that the proposed approach can improve the prediction accuracy of grain yield as well as achieve an efficient monitoring of crop growth.Under water deficit conditions,long-term water deficit had a great impact on the growth of winter wheat at the filling stage,in turn leading to a decline of winter wheat grain yield.In comparison with normal quantity of irrigation water,the long-term water deficit caused a decrease in winter wheat production by about 1/2.