无人机多光谱遥感反演不同深度土壤盐分

Soil salinity inversion at different depths using improved spectral index with UAV multispectral remote sensing

快速、精准获取作物覆盖下的土壤盐分信息,可以提高区域土壤盐渍化治理的有效性。该研究在内蒙古河套灌区沙壕渠灌域内试验地获取无人机多光谱遥感图像数据,并同步采集不同深度的土壤盐分数据。通过遥感图像数据提取光谱反射率并计算传统光谱指数,在此基础上引入红边波段建立新的光谱指数,同时使用Elastic-net算法(ENET)对光谱变量进行筛选,并将筛选后的光谱变量分为原始光谱变量组和改进光谱变量组;运用BP神经网络(Back Propagation Neural Networks,BPNN)、支持向量机(Support Vector Machine,SVM)和极限学习机(Extreme Learning Machine,ELM)3种机器学习方法,构建作物覆盖下不同土壤深度的土壤盐分反演模型,并基于最佳反演模型绘制试验区不同深度土壤盐分反演图。结果表明,使用ENET变量选择方法可以有效筛选出最优光谱变量,且基于改进光谱变量组构建的反演模型精度均高于原始光谱变量组;ELM模型反演效果优于SVM模型和BPNN模型,其验证集的决定系数为0.783,均方根误差为0.141,一致性相关系数为0.875;研究区域内,作物覆盖下的土壤盐分最佳反演深度为10~20 cm;在不同土壤深度下,基于改进光谱变量组构建的最佳反演模型绘制的土壤盐分反演图可以较为真实地反映试验区内的盐渍化程度,这说明引入红边波段构建光谱指数可以用于土壤盐分的反演。该研究为无人机多光谱遥感监测农田土壤盐渍化以及农田盐渍化治理提供了一种新途径。

Quick and accurate acquisition of soil salinity information with vegetation cover is critical to prevent environmental deterioration especially in arid and semi-arid areas.The UAV multispectral remote sensing systems has widely been expected to apply for monitoring the soil salinity,due to its low cost,high resolution,as well as resistance to weather and terrain.This study aims to obtain the soil salinity at various depths under the crop cover,using the improved spectral index.The UAV multispectral remote sensing images were captured at four test sites with different salinization degrees,including 0.065%-0.275%,0.194%-0.828%,0.220%-1.239%,0.594%-3.112%,in Shahaoqu Irrigation Area,Inner Mongolia,China(40°52′-41°00′N,107°05′-107°10′E,elevation 1030 m),from July 16 to 20 in 2019.Simultaneously,the soil salinity data were collected with various depths at 0-10 cm,10-20 cm,and 20-40 cm.Firstly,a six-rotor UAV equipped with a Micro-MCA multispectral camera was used to acquire the images,where the traditional spectral index was calculated using the extracted spectral reflectance with remote sensing images.A Rededge band based on the traditional spectral index was introduced to establish a new spectral index,serving as an improved spectral index.Next,an Elastic-net algorithm(ENET)was selected such spectral variables as spectral band,traditional spectral index,and modified spectral index(established by introducing Rededge band).The screened spectral variables were divided into the original spectral variable group and the improved spectral variable group.Finally,three machine learning algorithms,such as BP Neural Network(BPNN),Support Vector Machine(SVM),and Extreme Learning Machine(ELM),were combined with the ENET to construct the soil salinity inversion model at different soil depths.The maps of soil salt inversion were drawn at the test sites using the optimal inversion model that constructed by the improved spectral variable group,after evaluating the model performance.The results showed that:1)The variable selection method ENET can be used to effectively screen the optimal spectral variables.The performance of inversion models that constructed by three variable selection methods was superior to those without screening variables;2)The optimal inversion depth of soil salinity with vegetation cover was>10-20 cm.The model performance of ELM was better than that of SVM and BPNN.The ENET-ELM inversion model performed better,where the determination coefficients(RC 2)of calibration dataset were 0.785,the root mean square error(RMSEC)were 0.128,the consistency correlation coefficients(CC1)were 0.879,the determination coefficients(RP 2)of validation dataset were 0.783,the root mean square error(RMSEP)were 0.141,and the consistency correlation coefficients(CC2)were 0.875.3)At different soil depths,the soil salinity inversion map that drawn by the optimal inversion model using the improved spectral variables can effectively elucidate the degree of salinization in the test area,indicating that the introduction of Rededge band to construct the spectral index can be used for the soil inversion of salinity.This finding can provide a promising way for using UAV multi-spectral remote sensing to monitor and prevent soil salinization of farmland.