基于遥感多参数和IPSO-WNN的冬小麦单产估测

Yield Estimation of Winter Wheat Based on Remotely Sensed Multi-parameters and IPSO-WNN

冬小麦是我国的主要粮食作物之一。为进一步准确地估测冬小麦产量,以陕西省关中平原为研究区域,选取冬小麦主要生育期与水分胁迫和光合作用等密切相关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)作为遥感特征参数,采用改进的粒子群算法优化小波神经网络(IPSO-WNN)以改善梯度下降方法易陷入局部最优的缺陷,并构建冬小麦产量估测模型。结果表明,IPSO-WNN模型的决定系数R2为0.66,平均绝对百分比误差(MAPE)为7.59%,相比于BPNN(R2=0.46,MAPE为11.80%)与WNN(R2=0.52,MAPE为9.80%),IPSO-WNN能够进一步提高模型的精度、增强模型的鲁棒性。采用灵敏度分析的方法探究对冬小麦产量影响较大的输入参数,结果发现,抽穗-灌浆期的FPAR对冬小麦产量影响最大,其次拔节期的VTCI、抽穗-灌浆期和乳熟期的LAI以及返青期和拔节期的FPAR对冬小麦产量的影响较大。通过IPSO-WNN输出获取冬小麦综合监测指数I,构建I与统计单产之间的估产模型以估测关中平原冬小麦单产,结果显示,估测单产与统计单产之间的R2为0.63,均方根误差(RMSE)为505.50 kg/hm^(2),相比于前人的研究较好地解决了估产模型存在的“低产高估”的问题,因此,本文基于IPSO-WNN构建的估产模型能够较准确地估测关中平原冬小麦产量。

Wheat is one of the major food crops in China.To further estimate the yield of winter wheat accurately,Guanzhong Plain in Shaanxi Province was used as the study area,vegetation temperature condition index(VTCI),leaf area index(LAI)and fraction of photosynthetically active radiation(FPAR),which were closely related to water stress and photosynthesis at the main growth stage were selected as remotely sensed characteristic parameters,and the improved particle swarm optimized wavelet neural network(IPSO-WNN)was used to improve the shortcomings of gradient descent method which tended to fall into local optimum and construct winter wheat yield estimation model.The results showed that the IPSO-WNN model had a coefficient of determination(R2)of 0.66 and a mean absolute percentage error(MAPE)of 7.59%.Compared with the BPNN(R2=0.46,MAPE was 11.80%)and WNN(R2=0.52,MAPE was 9.80%),the IPSO-WNN can further improve the accuracy of the yield estimation and enhance the robustness of the model.It was explored by sensitivity analysis that the input parameters had a strong influence on winter wheat yield,and it was found that FPAR at the heading-filling stage had the greatest effect on winter wheat yield,followed by VTCI at the jointing stage,LAI at the heading-filling and milk maturity stages and FPAR at the green-up and jointing stages.The I index of winter wheat was obtained from IPSO-WNN output,and a yield estimation model between I and statistical yield was constructed to estimate the yield of winter wheat in the Guanzhong Plain.The results showed that the R2 between estimated yield and statistical yield was 0.63 and root mean square error(RMSE)was 505.50 kg/hm^(2),and the problem of“low yield and high estimation”of the yield estimation model was solved.Therefore,the yield estimation model constructed based on IPSO-WNN can estimate the yield of winter wheat in the Guanzhong Plain more accurately.