大田葵花土壤含盐量无人机遥感反演研究

UAV Remote Sensing Inversion of Soil Salinity in Field of Sunflower

以内蒙古河套灌区沙壕渠灌域内大田葵花为研究对象,划分4块不同盐分梯度的试验地,利用无人机搭载六波段多光谱相机和热红外成像仪获取遥感数据,并同步采集区域内不同土壤深度处的盐分数据。利用灰色关联法对构建的光谱指数进行筛选,同时结合冠层温度数据,采用偏最小二乘回归(PLSR)、支持向量机(SVM)、反向传播神经网络(BPNN)和极限学习机(ELM)4种建模方法构建大田葵花不同生育期、不同土壤深度的盐分反演模型。结果表明,基于葵花现蕾期数据构建的盐分反演模型整体效果优于开花期,以优选盐分指数和光谱指数作为变量组构建的模型效果优于植被指数变量组,盐分反演效果较好的土壤深度为0~20 cm和20~40 cm。不同建模方法对比结果表明,机器学习盐分反演模型的效果优于偏最小二乘回归模型,其中在葵花现蕾期0~20 cm土壤深度处,以光谱指数作为变量组构建的BPNN盐分模型反演效果最好,建模集和验证集R2分别达到0.773和0.718,验证集RMSE、CC分别达到0.062%和0.813。本研究成果可为无人机遥感在大田葵花土壤盐分监测方面的应用及相关研究提供参考。

It is of great significance to obtain soil salt information timely and accurately for guiding rational irrigation,ensuring normal growth and development of crops,and realizing high yield.Sunflowers of four kinds of croplands with different salinizations in Shahaoqu District of Hetao Irrigation Area were set as the study object,remote sensing data were obtained by using multi-spectral camera and thermal infrared imager,meanwhile,the soil salt data at different soil depths in the region were collected.The soil salinity inversion models were constructed for sunflower field in different growth stages and soil depths with four regression methods,including partial least squares regression(PLSR),support vector machine(SVM),back propagation neural network(BPNN)and extreme learning machine(ELM),which were based on canopy temperature,and spectral index was screened by grey correlation method.The result showed that the effect of salt inversion model constructed based on the data of sunflower budding stage was better than that of flowering stage on the whole,the model constructed with the preferred salt index and spectral index as the variable group was better than that of vegetation index variable group and the soil depth with good salinity inversion was 0~20 cm and 20~40 cm.The comparison showed that the effect of machine learning salt inversion model was better than partial least squares regression model,BPNN salt model constructed with spectral index as variable group had the best inversion effect at the depth of 0~20 cm soil in sunflower germination stage,in which the modeling R2 and validation R2 were 0.773 and 0.718,and the RMSE and CC of validation reached 0.062%and 0.813,respectively.The research result provided a reference for the application of UAV remote sensing in sunflower field soil salinity monitoring and related research.