摘要
针对当前快速准确获取叶面积指数(Leaf area index,LAI)时大部分遥感预测方法将光谱信息作为模型主要特征,忽略时序变化特征的问题,利用无人机搭载五通道多光谱相机获取研究区玉米不同生育期的影像数据,基于该数据计算玉米相应生育期植被指数,然后采用植被指数建立各生育期子模型,采用Shapley理论计算子模型均方根误差对全生育期模型均方根误差的贡献度,从而确定各子模型权重,根据权重组合形成具有LAI时序变化特征的估算模型,分别基于支持向量回归(SVR)、多层感知机(MLP)、随机森林(RF)和极限梯度提升树(XGBoost)算法构建组合估算模型。结果表明:采用Shapley理论构建的组合LAI估算模型估算效果优于直接构建的全生育期LAI估算模型。相较于SVRShapley、MLPShapley以及RFShapley模型,XGBoostShapley模型的估算效果最佳(R^(2)为0.97,RMSE为0.021,RPD为6.9)。将最优模型XGBoostShapley应用于研究区LAI预测,预测结果符合不同生育期玉米长势。本研究为大田玉米长势遥感监测提供了新的思路和方法。
In view of the problem that most remote sensing prediction methods take spectral information as the main feature of the model and ignore the temporal variation characteristics when obtaining leaf area index(LAI)quickly and accurately,the UAV was equipped with a five channel multispectral camera to obtain the multispectral images of different growth periods of maize in the study area.The vegetation indices of maize in corresponding growth period were calculated based on the images.Then the sub models of each growth period were established by using the vegetation indices.The contribution of the root mean square error(RMSE)of each sub model to the RMSE of the whole growth period model was calculated based on Shapley theory.The weight of each sub model was given based on its contribution.The combination estimation model was built with LAI time-series variation characteristics according to the weight.And different combination models were built based on support vector regression(SVR)algorithm,multilayer perceptron(MLP),random forest(RF)algorithm and XGBoost algorithm for comparison.The results showed that the estimation effect of the combined LAI estimation model based on Shapley theory was better than that of the whole growth period LAI estimation model.Compared with other LAI estimation models(SVRShapley,MLPShapley and RFShapley),the XGBoostShapley model had the best estimation effect(R2 was 0.97,RMSE was 0.021,RPD was 6.9).Thus the XGBoostShapley model was applied to LAI prediction in the study area.The research results showed that the LAI change rate in different growth periods were different,and the prediction results accorded with the growth trend of maize in different growth periods.The research result can provide a method for remote sensing monitoring of field maize growth.
作者
张宏鸣
侯贵河
孙志同
杨欢瑜
韩柯城
韩文霆
ZHANG Hongming;HOU Guihe;SUN Zhitong;YANG Huanyu;HAN Kecheng;HAN Wenting(College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第7期208-216,225,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2020YFD1100601)。
