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	基于光谱指数的绿洲农田土壤含水率无人机高光谱检测
其他题名	Detection of Soil Moisture Content Based on UAV-derived Hyperspectral Imagery and Spectral Index in Oasis Cropland
	王敬哲 1; 丁建丽1 ; 马轩凯 2; 葛翔宇 1; 刘博华 1; 梁静 1
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2018
卷号	49 期号:11 页码:164-172
中文摘要	选取新疆阜康绿洲小块农田为研究对象,基于无人机(Unmanned aerial vehicle,UAV)平台搭载的高光谱传感器获取的影像数据,采用Savitzky-Golay(SG)平滑后的一阶微分(First derivative,FD) 、吸光度(Absorbance, Abs) 、连续统去除(Continuum removal,CR) 3种不同预处理方法,获取了SG、SG-FD、CR、Abs及Abs-FD共计5种预处理后的高光谱影像,探索不同预处理下的差值指数(Difference index,DI) 、比值指数(Ratio index,RI) 、归一化指数(Normalization index,NDI)及垂直植被指数(Perpendicular vegetation index,PVI)与土壤含水率(Soil moisture content,SMC)的关系,在遴选出最优指数及预处理方案的基础上,构建干旱区绿洲农田SMC高光谱定量估算模型.结果表明:预处理在不同程度上提高了光谱指数与SMC的相关性,其中基于Abs预处理的PVI_((R644,R651))表现最优,相关系数为0.788,据此构建的三次拟合函数表现最优.基于不同预处理方案下,多变量SMC估算模型在消噪的基础上更深入地挖掘了光谱信息,减少了单一光谱指数造成的误差,提升了模型的定量估测效果.Abs模型预测精度亦最为突出,其建模集R_c~2和RMSE为0.84、2.16%,验证集R_p~2与RMSE为0.91、1.71%,RPD为2.41.本研究构建的SMC估算模型减少了单一变量模型的误差,在规避过拟合现象的同时,提升了模型的定量估测效果,为土壤含水率状况天地空一体化遥感监测提供了参考方案.
英文摘要	Soil moisture content ( SMC) is one of the most critical soil components for successful plant growth and land management,particularly in arid and semi-arid areas. In existing researches,it was determined by a conventional method based on oven drying of samples collected from fields. The first derivative ( FD),absorbance ( Abs) and continuum-removal ( CR) algorithm were brought into the preprocessing of hyperspectral data based on the initial Savitzky-Golay ( SG) smoothing. With SMC data and unmanned aerial vehicle ( UAV) platform derived imaging hyperspectral imagery collected from the cropland in Fukang Oasis,Xinjiang Uyghur Autonomous Region,China. Then,the raw hyperspectral reflectance data were transformed into five preprocessing,i. e.,SG,SG-FD,CR,Abs and Abs-FD. In addition,the relationships between SMC and pretreated difference index ( DI),ratio index ( RI), normalization index ( NDI) and perpendicular vegetation index ( PVI) were discussed. The correlation coefficients between each spectral index and SMC were also computed. Based on the optimal spectral index and pretreatment scheme,the hyperspectral quantitative estimating model was constructed for the dictation of SMC in oasis cropland in arid area. The result showed that the correlation between pretreated spectral index and SMC was improved to some extent,and the PVI_(( R644,R651)) based on Abs preprocessing was the best with correlation coefficient of 0.788. The cubic fitting function was optimal. On the basis of noise elimination,the multivariable SMC estimation model based on different preprocessing schemes could detect much finer spectral information from reflectance data,reduce the error caused by the single spectral index,and further improve the quantitative estimation effect of the model. The prediction accuracy of the Abs model was the most prominent,with R_c~2 of 0.84,RMSE of 2.16%,R_p~2 of 0.91 and RMSE of 1.71%. The effect of the SMC estimation model constructed was based on the preprocessing and noise elimination. The constructed SMC estimation model could reduce the error of independent single variable; and further resolve the problem of over fitting. The model could be used for hyperspectral mapping and performance estimating. The research result could provide a novel perspective and scheme for the remote sensed detection of soil water condition,especially in the arid and semi-arid areas.
中文关键词	土壤含水率 ; 高光谱 ; 无人机 ; 遥感 ; 光谱指数
英文关键词	soil moisture content hyperspectra unmanned aerial vehicles remote sensing spectral index
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6371548
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/238016
作者单位	1.新疆大学资源与环境科学学院;;新疆大学, ;;绿洲生态教育部重点实验室, 乌鲁木齐;;乌鲁木齐, ;; 830046;;830046; 2.新疆联海创智信息科技有限公司, 乌鲁木齐, 新疆 830011, 中国
推荐引用方式 GB/T 7714	王敬哲,丁建丽,马轩凯,等. 基于光谱指数的绿洲农田土壤含水率无人机高光谱检测[J]. 新疆大学,2018,49(11):164-172.
APA	王敬哲,丁建丽,马轩凯,葛翔宇,刘博华,&梁静.(2018).基于光谱指数的绿洲农田土壤含水率无人机高光谱检测.农业机械学报,49(11),164-172.
MLA	王敬哲,et al."基于光谱指数的绿洲农田土壤含水率无人机高光谱检测".农业机械学报 49.11(2018):164-172.