HYDRUS模型与遥感集合卡尔曼滤波同化提高土壤水分监测精度
发布时间：2019-04-29 19:53
【摘要】：精确地估测干旱区土壤水分含量,对该区域的农业发展与水土保持具有重要意义。该文以MODIS与Landsat TM数据为数据源,利用其反演获得的条件温度植被指数(temperature-vegetation drought Index,TVDI)作为观测算子,将集合卡尔曼滤波(ensemble Kalman filter,En-KF)同化方法应用于水文模型(HYDRUS-1D),进行干旱区表层土壤水分的模拟。结果表明:遥感数据反演土壤水分所构建的二维特征空间TVDI与表层土壤水分有较好的一致性;En-KF同化方法对模型变量与观测算子的更新,与单纯使用HYDRUS模型相比,获得的表层土壤水分含量精度有了明显提高,其均方根误差缩小了1个百分点,平均误差缩小了5个百分点。可见,基于多源遥感数据对表层土壤水分的En-KF同化模拟在干旱区具有较大的潜力,是提高干旱区土壤水分含水量监测精度的有效手段。
[Abstract]:Accurate estimation of soil moisture content in arid area is of great significance for agricultural development and soil and water conservation in this region. In this paper, the MODIS and Landsat TM data are used as data sources, and the conditional temperature vegetation index (temperature-vegetation drought Index,TVDI) obtained from the inversion is used as the observation operator. The set Kalman filter (ensemble Kalman filter, is used in this paper. En-KF) assimilation method was applied to the hydrological model (HYDRUS-1D) to simulate the surface soil moisture in arid areas. The results show that the two-dimensional characteristic space TVDI constructed by retrieving soil moisture from remote sensing data is in good agreement with that of surface soil moisture. Compared with the simple use of HYDRUS model, the accuracy of surface soil moisture content obtained by En-KF assimilation method is obviously improved, and its root mean square error is reduced by 1 percentage point, compared with the model variables and observation operators, and the root mean square error (RMS) is reduced by 1 percentage point. The average error was reduced by 5 percentage points. Therefore, the En-KF assimilation simulation of surface soil moisture based on multi-source remote sensing data has great potential in arid areas, and it is an effective means to improve the monitoring accuracy of soil moisture content in arid areas.
【作者单位】： 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;
【基金】：国家自然科学基金(U1303381、41261090) 自治区重点实验室专项基金(2016D03001) 自治区科技支疆项目(201591101) 教育部促进与美大地区科研合作与高层次人才培养项目 新疆大学优秀博士生科技创新项目(XJUBSCX-2016014)
【分类号】：S152.7
[Abstract]:Accurate estimation of soil moisture content in arid area is of great significance for agricultural development and soil and water conservation in this region. In this paper, the MODIS and Landsat TM data are used as data sources, and the conditional temperature vegetation index (temperature-vegetation drought Index,TVDI) obtained from the inversion is used as the observation operator. The set Kalman filter (ensemble Kalman filter, is used in this paper. En-KF) assimilation method was applied to the hydrological model (HYDRUS-1D) to simulate the surface soil moisture in arid areas. The results show that the two-dimensional characteristic space TVDI constructed by retrieving soil moisture from remote sensing data is in good agreement with that of surface soil moisture. Compared with the simple use of HYDRUS model, the accuracy of surface soil moisture content obtained by En-KF assimilation method is obviously improved, and its root mean square error is reduced by 1 percentage point, compared with the model variables and observation operators, and the root mean square error (RMS) is reduced by 1 percentage point. The average error was reduced by 5 percentage points. Therefore, the En-KF assimilation simulation of surface soil moisture based on multi-source remote sensing data has great potential in arid areas, and it is an effective means to improve the monitoring accuracy of soil moisture content in arid areas.
【作者单位】： 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;
【基金】：国家自然科学基金(U1303381、41261090) 自治区重点实验室专项基金(2016D03001) 自治区科技支疆项目(201591101) 教育部促进与美大地区科研合作与高层次人才培养项目 新疆大学优秀博士生科技创新项目(XJUBSCX-2016014)
【分类号】：S152.7
【参考文献】
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