基于遥感导数处理和最优光谱指数的土壤盐渍化监测模型
Soil salinization monitoring model based on remote sensing derivative processing and optimal spectral index
唐子茹 1吴彤 1谭世林 1岳胜如1
作者信息
- 1. 塔里木大学水利与建筑工程学院,新疆 阿拉尔 843300
- 折叠
摘要
利用Landsat-8遥感数据,基于原始光谱、一阶导数、二阶导数3种处理,分析了波段反射率、2D指数、3D指数与土壤电导率相关性.选择最优光谱指数作为神经网络算法输入参数,基于MATLAB构建土壤盐渍化预测模型.结果表明,2D、3D光谱指数与土壤的电导率相关性高于原始光谱,二阶导数处理后构建的2D、3D指数与土壤电导率整体相关性优于一阶导数处理和原始光谱.原始光谱下选择B1至B7作为神经网络算法输入参数所建模型精度最优,训练集、验证集、测试集和整体的相关系数分别为0.732 4、0.716 4、0.444 5、0.691 9,所构建模型对土壤电导率在1 000 μS/cm附近时预测精度较高.
Abstract
Using Landsat-8 remote sensing data,the correlation of band reflectance,2D and 3D indices with soil conductivity was an-alyzed based on three treatments:Raw spectra,first-order derivatives and second-order derivatives.The optimal spectral index was selected as the input parameter of the neural network algorithm,and the soil salinization prediction model was constructed based on MATLAB.The results showed that the 2D and 3D spectral indices had a higher correlation with the conductivity than the original spec-tra,and the overall correlation between the 2D and 3D indices constructed after the second-order derivative treatment and soil conduc-tivity was better than that of the first-order derivative treatment and the original spectra.The accuracy of the model constructed by choosing B1 to B7 as the input parameters of the neural network algorithm under the original spectra was optimal,the correlation coeffi-cients of the training set,validation set,test set and the whole were 0.732 4,0.716 4,0.444 5,0.691 9,respectively,and the con-structed model had high prediction accuracy when the soil conductivity was around 1 000 μS/cm.
关键词
土壤盐渍化/Landsat-8/遥感导数处理/最优光谱指数/神经网络算法Key words
soil salinization/Landsat-8/remote sensing derivative processing/optimal spectral index/neural network algorithm引用本文复制引用
基金项目
塔里木大学校长基金项目(TDZKQN201816)
国家级大学生创新创业项目(202210757033)
国家级大学生创新创业项目(202110757033)
出版年
2024
NETL
NSTL
万方数据