干旱区生态环境知识资源中心(Arid): 基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演

Arid

	基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演
其他题名	Remote Sensing Inversion of Soil Moisture in Vegetation-Sparse Arid Areas based on SVR
	王雅婷 1; 孔金玲 2; 杨亮彦 1; 李健锋 1; 张文博 1
来源期刊	地球信息科学学报
ISSN	1560-8999
出版年	2019
卷号	21 期号:8 页码:1275-1283
中文摘要	中国西北半干旱区降水稀少、蒸散强烈,土壤水分作为重要的生态因子,影响着土壤-大气界面的能量平衡。支持向量回归模型具有估算精度高、可处理非线性问题、泛化能力强等优点,近年来被应用于土壤水分反演研究中,但已有模型极少考虑地表粗糙度因素的影响,导致反演精度受到一定限制。因此,本文以内蒙古乌审旗为研究区,采用水云模型去除地表稀疏植被覆盖的影响,提取全极化Radarsat-2 SAR影像裸土后向散射系数(sigma_(soil)~0),并利用AIEM模型和Oh模型建立后向散射系数数据库,采用LUT法模拟地表有效粗糙度参数,构建基于支持向量冋归的土壤水分反演模型,并系统地对比分析了不同极化方式的后向散射系数作为数据源的土壤水分反演结果。研究结果表明:不考虑粗糙度参数的单数据源作为模型参数时,同极化数据反演结果比交叉极化具有更高的反演精度;当模型参数为考虑粗糙度的多源数据时,不同极化数据的反演精度均有所提高,其中数据源为sigma_(vv)~0和粗糙度参数时,反演结果最好(R~2=0.917,M4E=3.980%,RMSE=5.187%).研究结果可为旱区稀疏植被覆盖地表土壤水分的遥感监测提供技术支持.
英文摘要	In the arid and semi-arid regions of northwest China,the precipitation is scarce and evapotranspiration is intense.Soil moisture,as an important ecological factor,affects the energy balance of soil-atmosphere interface.In recent years,Support Vector Regression(SVR) model has been applied in soil moisture inversion for its merits including,high estimation accuracy,good ability to deal with non-linear processing and strong generalization ability.However,existing models rarely consider the influence of surface roughness,which limits of inversion accuracy.Taking the Uxin Banner of Ordos city of Inner Mongolia as a study area,this study aims to construct a suitable soil moisture inversion model through combining Radarsat-2 synthetic aperture radar (SAR) data and GF-1 data.To extract backscattering coefficient of bare soil(sigma_(soil)~0) from the full polarization Radarsat-2 SAR data,we used Water-Cloud model(WCM) to remove the influence of vegetation-sparse layer on the radar backscattering coefficient.Meanwhile,we constructed backscattering coefficient database of bare soil by using Advanced Integrated Equation Model(AIEM) and Oh Model,and used Look Up Table (LUT) method to simulate effective surface roughness parameters such as root mean square height(S) and correlation length(L).Finally,the soil moisture model was built based on support vector regression,and the soil moisture inversion results of different data sources under the backscattering coefficients of different polarization modes were systematically compared and analyzed.The results showed that the inversion accuracy of the co-polarization data (W polarization or HH polarization) was higher than that of the cross-polarization data(VH polarization or HV polarization) when the single data source without considering the roughness parameter was used as the model parameter.When the model parameter was the multi-source data with considering the roughness parameter,the inversion accuracy of different polarization data was improved.When the data source was sigma_(vv)~0 and roughness parameter,the inversion model had a higher precision,the correlation coefficient between inversion value and measured value was 0.917.The mean absolute error(MAE) and root mean square error (RMSE) were 3.980% and 5.187%,respectively.Our findings can serve as the technical support for remote sensing of surface soil moisture in vegetation-sparse arid areas.
中文关键词	AIEM模型 ; 有效粗糙度参数 ; 土壤水分反演 ; 西北半干旱区 ; 内蒙古乌审旗 ; 稀疏植被
英文关键词	SVR Radarsat-2 SVR Radarsat-2 AIEM model effective roughness parameter soil moisture inversion semi-arid regions of northwest China Uxin Banner of Inner Mongolia vegetation-sparse
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:6551384
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/239024
作者单位	1.长安大学地球科学与资源学院, 西安, 陕西 710054, 中国; 2.长安大学地质工程与测绘学院, 西安, 陕西 710054, 中国
推荐引用方式 GB/T 7714	王雅婷,孔金玲,杨亮彦,等. 基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演[J],2019,21(8):1275-1283.
APA	王雅婷,孔金玲,杨亮彦,李健锋,&张文博.(2019).基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演.地球信息科学学报,21(8),1275-1283.
MLA	王雅婷,et al."基于SVR的旱区稀疏植被覆盖下土壤水分遥感反演".地球信息科学学报 21.8(2019):1275-1283.