干旱区生态环境知识资源中心(Arid): 基于无人机多光谱遥感的土壤含盐量反演模型研究

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	基于无人机多光谱遥感的土壤含盐量反演模型研究
其他题名	Soil Salt Inversion Model Based on UAV Multispectral Remote Sensing
	张智韬; 魏广飞; 姚志华; 谭丞轩; 王新涛; 韩佳
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2019
卷号	50 期号:12
中文摘要	为探究无人机多光谱遥感技术快速监测植被覆盖下的土壤含盐量问题,以内蒙古河套灌区沙壕渠灌域内4块不同盐分梯度的耕地为研究区域,利用无人机搭载多光谱传感器获取2018年8月遥感影像数据,并对0~ 40 cm的土壤进行盐分测定。分别引入敏感波段组、光谱指数组、全变量组作为模型输入变量,采用支持向量机(Support vector machine,SVM)、BP神经网络(Back propagation neural network,BPNN)、随机森林(Random forest,RF)、多元线性回归(Multiple linear regression,MLR) 4种回归方法,建立基于3组输入变量下的土壤盐分反演模型,并进行精度评价,比较不同输入变量、不同回归方法对模型精度的影响,评价并优选出最佳盐分反演模型。结果表明,通过分析3个变量组的R~2和RMSE,光谱指数组在4种回归方法中均取得了最佳的反演效果,敏感波段组和全变量组在不同的回归方法中反演效果不同。4种回归方法中,3种机器学习算法反演精度明显高于MLR模型,且MLR模型中的敏感波段组和全变量组均出现了过拟合现象,RF算法在3种机器学习算法中表现最优,SVM算法和BPNN算法在基于不同变量组的模型中表现也不相同。基于光谱指数组的RF的盐分反演模型在12个模型中取得了最佳的反演效果,R~2c和R~2v分别达到了0.72和0.67,RMSEv仅为0.112%。
英文摘要	Fast acquisition of soil salt content under vegetation cover is the objective need of saline soil management and utilization. Four kinds of croplands with different salinization values in Shahaoqu District of Hetao Irrigation Area were set as the study areas. The UAV equipped with a multi-spectral camera obtained the remote sensing image data of August,meanwhile,the soil salinity with depth of 0 ~ 40 cm was tested. The sensitive band group,spectral index group and full variable group were introduced as model input variables. Four regression methods,including support vector machine (SVM), BP neural network (BPNN), random forest (RF) and multiple linear regression (MLR), were used to establish soil salinity inversion models which were based on three groups of input variables,respectively. Firstly, the model precision was evaluated,and then the effects of different input variables and different regression methods on the model precision were compared,finally the best salt inversion model was evaluated and optimized. The results indicated that comparing the R~2 and RMSE of three variable groups, the spectral index group achieved the best inversion effect between the four regression model methods, and the sensitive band group and the full variable group had advantages and disadvantages in different regression algorithms. Between the four regression methods,the inversion accuracy of three machine learning regression algorithms was significantly higher than that of the MLR model. Moreover,both the sensitive band group and the full variable group in the MLR model showed the phenomenon of overfitting. And RF algorithm performed best between the three machine learning algorithms. Besides, SVM algorithm and BPNN algorithm performed better and worse in the model with different variable groups. The RF salt inversion model based on the spectral index group achieved the best inversion effect among the 12 models,the R~2c and R~2v reached 0.72 and 0.67,respectively,and the RMSEv error was only 0.112%. The research result can provide a theoretical reference for soil salinity monitoring in arid and semi-arid areas.
中文关键词	土壤含盐量 ; 无人机 ; 多光谱遥感 ; 变量组 ; 机器学习 ; 多元线性回归
英文关键词	soil salt unmanned aerial vehicle multispectral remote sensing variable group machine learning multiple linear regression
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6628750
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353253
作者单位	张智韬, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国. 魏广飞, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国. 姚志华, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国. 谭丞轩, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国. 王新涛, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国. 韩佳, 西北农林科技大学水利与建筑工程学院;;西北农林科技大学, ;;旱区农业水土工程教育部重点实验室, 杨凌;;杨凌, 陕西;;陕西 712100;;712100, 中国.
推荐引用方式 GB/T 7714	张智韬,魏广飞,姚志华,等. 基于无人机多光谱遥感的土壤含盐量反演模型研究[J],2019,50(12).
APA	张智韬,魏广飞,姚志华,谭丞轩,王新涛,&韩佳.(2019).基于无人机多光谱遥感的土壤含盐量反演模型研究.农业机械学报,50(12).
MLA	张智韬,et al."基于无人机多光谱遥感的土壤含盐量反演模型研究".农业机械学报 50.12(2019).