基于无人机多光谱影像的土壤盐分反演模型  

Soil salinity inversion model based on the multispectral images of UAV

作　　者：赵文举[1] 马芳芳 马宏[1] 周春 Zhao Wenju;Ma Fangfang;Ma Hong;Zhou Chun(College of Energy and Power Engineering,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学能源与动力工程学院,兰州730050

出　　处：《农业工程学报》2022年第24期93-101,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51869010)。

摘　　要：为探究不同作物覆盖下不同深度的土壤盐分快速反演模型,该研究采集苜蓿、玉米覆盖下0~15、>15~30、>30~50 cm层深度的土壤盐分含量,基于无人机多光谱影像数据,提取各地块采样点的光谱反射率,在此基础上引入红边波段计算光谱指数作为特征变量,采用支持向量机递归特征消除算法(Support Vector Machine-Recursive Feature Elimination,SVM-RFE)以筛选光谱指数及未经过筛选的全指数组作为模型输入组,共构建出36个基于随机森林(Random Forest,RF)、极限学习机(Extreme Learning Machine,ELM)、BP神经网络(Back Propagation Neural Network)等机器学习模型,确定不同作物覆盖下的最佳土壤盐分反演模型。结果表明:SVM-RFE算法筛选光谱指数构建模型精度优于未进行筛选构建的模型。对于苜蓿和玉米覆盖土壤,整体上,RF反演效果优于ELM模型和BPNN模型,反演结果能体现真实土壤盐分含量,在0~15和>30~50 cm土层上,RF模型反演效果优于其他模型,苜蓿样地验证集决定系数R_(p)^(2)分别为0.71、0.58,验证集均方根误差RMSEp分别为0.026、0.033,玉米样地R_(p)^(2)分别为0.67、0.64,RMSEp分别为0.111、0.094,在>15~30 cm土层上ELM反演效果较好,苜蓿样地R_(p)^(2)为0.58,RMSEp为0.039,玉米样地R_(p)^(2)为0.68,RMSEp为0.059。0~15 cm是作物覆盖下的土壤含盐量最佳反演深度,验证集平均决定系数R^(2)为0.65,均方根误差RMSE为0.084。研究结果可为土壤盐分的快速反演提供理论依据。Soil salinization has posed a serious threat to the growth and yield of crops in the national food security.Among them,the Taolai River basin with the widely distributed saline-alkali land has been one of the most important planting areas in northwest China.It is a high demand for the timely acquisition of soil salinity information during the salinization control.In this study,a representative sampling area of soil salinization was taken as the Bianwan Farm in Suzhou District,Jiuquan City,Gansu Province,China.A rapid inversion model of soil salinity was proposed at the soil depths of 0-15,15-30,and 30-50 cm under the crop cover of alfalfa and corn in the phenological period.The multi-spectral image data of the Unmanned Aerial Vehicle(UAV)was also collected at the same time.The reflectance of the spectral band was extracted in the different acquisition points of plots.The red edge band was also introduced to calculate the spectral index,in order to effectively improve the inversion accuracy.A total of 58 spectral indices were involved in the modeling.The Support Vector Machine-Recursive Feature Elimination(SVM-RFE)was selected to screen the spectral index.Specifically,the SVM was used to sort the feature variables,and then evaluate the importance of each feature variable.The variables with low importance were removed,according to the backward iteration.As such,a better performance was achieved to effectively remove the redundant features for the high running speed of the model.A total of 36 models were constructed to evaluate the accuracy and inversion effect of the models,including Random Forest(RF),Extreme learning machine(ELM),and Back-propagation neural network(BPNN).The model input was taken as the unfiltered full and filtered new variable group.Finally,the best soil-salinity inversion model was determined for the optimal inversion depth under crop coverage.The results show that the SVM-RFE variable selection significantly improved the accuracy of each soil-salinity inversion model.A better performance was ac

关 键 词：无人机 土壤 盐分 多光谱 SVM-RFE 反演模型 

分 类 号：S127[农业科学—农业基础科学]