干旱区生态环境知识资源中心(Arid): 干旱区绿洲植被高光谱与浅层土壤含水率拟合研究

Arid

	干旱区绿洲植被高光谱与浅层土壤含水率拟合研究
其他题名	Fitting of Hyperspectral Reflectance of Vegetation and Shallow Soil Water Content in Oasis of Arid Area
	陈文倩 1; 丁建丽1 ; 谭娇 1; 李相 2
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2017
卷号	48 期号:12 页码:229-236
中文摘要	水资源一直是制约我国西北干旱区农业发展的关键因素。以新疆渭库绿洲为研究区域,选取41个土壤含水率与干旱区绿洲植被实测高光谱样本,以植被指数为桥梁,采用支持向量机回归(SVR)方法,建立干旱区绿洲土壤含水率与植被指数之间的拟合方程模型,并与多元回归(MLSR)、偏最小二乘回归(PLS)2种模型进行对比。实验结果表明:不同模型的精度各异,拟合效果由优到劣为:改进的SVR模型、PLS模型、MLSR模型,其中基于干旱区绿洲实测的植被光谱数据改进的SVR模型对土壤含水率具有较好的拟合效果,通过最优参数的定值与最优测试集的抽取,R~2高达0.8916,RMSE仅为2.004,在干旱区绿洲的土壤含水率拟合中获得比较高的预测精度。而MLSR模型与PLS模型,R~2分别为0.6300、0.6549,RMSE分别为3.001与2.749。研究结果表明,因地制宜开展合理的土壤含水率反演模型规则制定是提高干旱区绿洲土壤浅层含水率监测精度的有效手段,也可为干旱区农业作物生长提供更精准的数据积累。
英文摘要	Water resources have become a key factor for restricting the social, economic and agricultural development of arid area in Northwest China. In recent years, agriculture in arid oasis has developed rapidly, and human activities have seriously affected balance on the regional soil moisture, resulting in a large area of salinization. Therefore, the monitoring of soil moisture is of great practical significance to the development of oasis agriculture and economy. Taking the oasis of Weiku in Xinjiang as the study area, totally 41 soil moisture samples and hyperspectral data of the oasis vegetation in arid area were collected, and the vegetation index was taken as bridge. Multiple regression (MLSR), partial least squares (PLS) regression and support vector machine regression (SVR) were used to establish the inversion model of soil water content in oasis, respectively, the regression models were tested respectively. The experimental results showed that the accuracy of different models was different. Through the optimization of parameters and extraction of optimal test set, the fitting effect from good to bad was improved SVR model, PLS model and MLSR model, which were based on the vegetation The improved SVR model had a good fitting effect, R~2 was 0.8916, RMSE was only 2.004, the analysis accuracy in the oasis of arid area reached the practical prediction accuracy. The R~2 values of MLSR model and PLS model were 0.6300 and 0.6549, and RMSE were 3.001 and 2.749, respectively. The results showed that it was an effective method to improve the monitoring accuracy of shallow soil water content in oasis, and it can also provide more data for monitoring soil moisture in arid area.
中文关键词	高光谱 ; 植被指数 ; 土壤含水率 ; 改进SVR
英文关键词	hyperspectra vegetation index soil water content improved SVR
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURAL ENGINEERING
WOS研究方向	Agriculture
CSCD记录号	CSCD:6139443
来源机构	新疆大学 ; 北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/236362
作者单位	1.新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国; 2.北京师范大学地理学与遥感科学学院, 北京 100875, 中国
推荐引用方式 GB/T 7714	陈文倩,丁建丽,谭娇,等. 干旱区绿洲植被高光谱与浅层土壤含水率拟合研究[J]. 新疆大学, 北京师范大学,2017,48(12):229-236.
APA	陈文倩,丁建丽,谭娇,&李相.(2017).干旱区绿洲植被高光谱与浅层土壤含水率拟合研究.农业机械学报,48(12),229-236.
MLA	陈文倩,et al."干旱区绿洲植被高光谱与浅层土壤含水率拟合研究".农业机械学报 48.12(2017):229-236.