光学与微波遥感协同反演藏北表层土壤水分研究  

Cooperative Inversion of Topsoil Moisture Based on Optical and Microwave Remote Sensing Data in Northern Tibet

作　　者：王梅霞 冯文兰[1] 扎西央宗[2] 王永前[1] 牛晓俊[2] WANG Meixia;FENG Wenlan;ZHAXI Yangzong;WANG Yongqian;NIU Xiaojun(College of Environmental and Resource Science,Chengdu University of Information Technology,Chengdu 610225,China;Tibet Institute of Plateau Atmospheric and Environmental Science,Lhasa 850000,China)

机构地区：[1]成都信息工程大学资源环境学院,成都610225 [2]西藏高原大气环境科学研究所,拉萨850000

出　　处：《土壤》2019年第5期1020-1029,共10页Soils

基　　金：国家自然科学基金项目(41465006,41631180);四川省教育厅项目(16TD0024,18ZA0110)资助

摘　　要：表层土壤水分是定量干旱监测的重要参量,对干旱区生态环境具有十分重要的意义。在采用归一化植被指数阈值法划分地表覆盖类型的基础上,利用MODIS数据选择适用的光学遥感算法估算土壤水分基准值,以及利用风云三号B星搭载的微波成像仪(Fengyun-3B/Microware Radiation Imagery,FY3B/MWRI)数据采用微波遥感算法反演土壤水分日变化量,最后构建藏北表层土壤水分协同反演的遥感模型并应用于区域土壤水分的估算。结果表明:光学遥感与微波遥感协同反演的土壤水分含量与实测数据呈显著相关,决定系数达到0.89,均方根误差为0.97,协同反演模型具有较高的反演精度,并且协同反演的结果优于单一遥感源的反演结果。该模型可以较好地适用于藏北地区表层土壤水分的动态监测。Topsoil moisture is an important parameter to quantitatively monitor drought,and it plays an important role in the ecological environment in arid areas.The normalized difference vegetation index(NDVI)of threshold methods were used to classify land vegetation types,MODIS data were used to select optical remote sensing algorithm feasible to calculate the benchmark of soil moisture,FY3B/MWRI radiance data and microwave remote sensing algorithm were used to invert daily variation of top soil moisture,and finally the two techniques were combined to setup a cooperative inversion model of soil moisture for northern Tibet.The results showed a significant correlation between the inversed and in situ soil moistures,the determination coefficient was 0.89 and RMSE was 0.97,which indicated the cooperative inversion model was more accurate than the inversion model derived from single remote sensing data,and it is suitable for inversing topsoil moisture in northern Tibet.

关 键 词：土壤水分 FY3B/MWRI MODIS 协同反演 藏北地区 

分 类 号：TP722[自动化与计算机技术—检测技术与自动化装置] TP79[自动化与计算机技术—控制科学与工程]