基于遥感多参数和VMD-GRU的冬小麦单产估测

Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and VMD-GRU

为充分挖掘时间序列遥感参数的时序信息和趋势信息,并进一步提升冬小麦估产精度,以陕西省关中平原为研究区域,选取与冬小麦长势密切相关的生育时期尺度的条件植被温度指数(VTCI)、叶面积指数(LAI)和光合有效辐射吸收比率(FPAR)作为遥感参数,构建耦合变分模态分解(VMD)与门控循环单元(GRU)神经网络的估产模型。应用VMD算法将各个时间序列遥感参数分解为多组平稳的本征模态函数(IMF)分量,选取与原始时间序列遥感参数高度相关的IMF分量进行特征重构,并将重构特征作为GRU网络的输入,以构建冬小麦组合估产模型。结果表明,VMD-GRU组合估产模型决定系数为0.63,均方根误差为448.80 kg/hm^(2),平均相对误差为8.14%,相关性达到极显著水平(P<0.01),其精度优于单一估产模型精度,表明该组合估产模型能够提取非平稳时间序列数据的多尺度、多层次特征,并充分挖掘冬小麦各生育时期遥感参数间的内在联系,获得准确单产估测结果的同时提升了估产模型的可解释性。

In order to fully exploit the time-series information and trend information of time-series remotely sensed parameters and further improve the yield estimation accuracy of winter wheat,vegetation temperature condition index(VTCI),leaf area index(LAI)and fraction of photosynthetically active radiation(FPAR),which were closely related to the growth and development of winter wheat,were selected as remotely sensed parameters,and a neural network was constructed based on variational mode decomposition(VMD)and gated recurrent unit(GRU).The VMD algorithm was applied to decompose each remotely sensed parameter series into multiple sets of intrinsic mode function(IMF)components,and the IMF components that were highly correlated with the original remotely sensed parameter series were selected for feature reconstruction,and the reconstructed features were used as the input of the GRU network to develop a combined model for yield estimation of winter wheat.The results showed that the VMD-GRU model for yield estimation had a coefficient of determination of 0.63,root mean squared error of 448.80 kg/hm^(2),and mean relative error of 8.14%,with a highly significant correlation level(P<0.01),and its accuracy was better than that of the single model for yield estimation,indicating that the combined model for yield estimation can extract multi-scale and multi-level features of non-stationary time series and fully explore the internal linkage between remotely sensed parameters in each growth stage of winter wheat to obtain accurate yield estimation results and improve interpretability of model for yield estimation.