基于遥感多参数和CNN-Transformer的冬小麦单产估测

Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and CNN-Transformer

为了提高冬小麦单产估测精度,改善估产模型存在的高产低估和低产高估等现象,以陕西省关中平原为研究区域,选取旬尺度条件植被温度指数(VTCI)、叶面积指数(LAI)和光合有效辐射吸收比率(FPAR)为遥感特征参数,结合卷积神经网络(CNN)局部特征提取能力和基于自注意力机制的Transformer网络的全局信息提取能力,构建CNN-Transformer深度学习模型,用于估测关中平原冬小麦产量。与Transformer模型(R^(2)为0.64,RMSE为465.40 kg/hm^(2),MAPE为8.04%)相比,CNN-Transformer模型具有更高的冬小麦单产估测精度(R^(2)为0.70,RMSE为420.39 kg/hm^(2),MAPE为7.65%),能够从遥感多参数中提取更多与产量相关的信息,且对于Transformer模型存在的高产低估和低产高估现象均有所改善。基于5折交叉验证法和留一法进一步验证了CNN-Transformer模型的鲁棒性和泛化能力。此外,基于CNN-Transformer模型捕获冬小麦生长过程的累积效应,分析逐步累积旬尺度输入参数对产量估测的影响,评估模型对于冬小麦不同生长阶段的累积过程的表征能力。结果表明,模型能有效捕捉冬小麦生长的关键时期,3月下旬至5月上旬是冬小麦生长的关键时期。

In order to improve the accuracy of winter wheat yield estimation and the phenomena of underestimation of high yield and overestimation of low yield that exist in yield estimation models,the Guanzhong Plain in Shaanxi Province,China was taken as the study area,and the vegetation temperature condition index(VTCI),leaf area index(LAI)and fraction of photosynthetically active radiation(FPAR)at the ten-day interval were selected as remotely sensed parameters,and a deep learning model was proposed for estimating winter wheat yield by combining the local feature extraction capability of convolutional neural network(CNN)and the global information extraction capability of Transformer network based on the mechanism of self-attention.Compared with the Transformer model(R^(2) was 0.64,RMSE was 465.40kg/hm^(2),MAPE was 8.04%),the CNN-Transformer model had higher accuracy in estimating winter wheat yield(R^(2) was 0.70,RMSE was 420.39kg/hm^(2),MAPE was 7.65%),which can extract more yield-related information from the multiple remotely sensed parameters,and improved the underestimation of high yield and overestimation of low yield which existed in the Transformer model.The robustness and generalization ability of the CNN-Transformer model were further validated based on the five-fold cross-validation method and the leave-one-out method.In addition,based on the CNN-Transformer model,the cumulative effect of the winter wheat growth process was revealed,the impact of gradually accumulating ten-day scale input information on yield estimation was analyzed,and the ability of the model to characterize the accumulation process of winter wheat at different growth stages was assessed.The results showed that the model can effectively capture the critical period of winter wheat growth,which was from late March to early May.