干旱区生态环境知识资源中心(Arid): 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

Arid

	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
其他题名	Improving monitoring precision of soil moisture by assimilation of HYDRUS model and remote sensing-based data by ensemble Kalman filter
	丁建丽; 陈文倩; 王璐
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2017
卷号	33 期号:14 页码:166-172
中文摘要	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LandsatTM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。
英文摘要	Soil moisture as an important part of hydrology, atmosphere and land surface, is an essential link of surface water and groundwater, and it is also a key parameter to describe the exchange of energy for land, atmosphere and vegetation. Therefore, it is of great significance to accurately estimate soil moisture content in arid area due to its huge value for food security and water and soil conservation. This study investigated the feasibility of soil moisture estimation by assimilating HYDRUS model and remote sensing-based data using ensemble Kalman filter. The study area is located in the Weigan River and Kuqa River Delta in the southern Xinjiang region of Xinjiang Uygur Autonomous, developed by the Kuqa River and the Weigan River, which is the most representative arid oasis in the southern Xinjiang. Temperature-vegetation drought index(TVDI) was adopted as an observation operator, and ensemble Kalman filter (En-KF) method was applied to one-dimensional hydrological model (HYDRUS-1D) to simulate surface soil moisture. Soil samples from 39 points were collected for soil moisture measurement. The main conclusions included: 1) According to the TVDI feature space, the soil moisture was higher in the middle area (agricultural irrigation area) with high vegetation coverage, while in the oasis and desert transitional zone, soil moisture was low with low vegetation. In order to verify the error between the remote sensing image and the measured data, 10 samples were randomly selected from the 39 soil samples to simulate the soil moisture based on the TVDI feature space. The relative error between measured data and the remote sensing data was 13.06%, indicating that the soil moisture estimated by remote sensing was reliable and the estimated value could be considered as the measured data when the measured data were not available for some reasons; 2) Because the remote sensing inversion was mostly effective for the surface soil, the data for only 0-10 cm surface soil was used for the further assimilation analysis. The change in 0-10 cm soil moisture estimated by assimilation method and HYDRUS mod el from September 3~(rd) to December 9~(th) in 2013, a total of 98 days, showed that there was obvious difference between the HYDRUS model simulated results and the the measured data, especially before 18 day; 3) Verifying the assimilation results using the other 29 soil samples showed that the relative error between the assimilated results and measured results were 8% and that between the HYDRUS model simulated results with the measured results was 13%. The root mean square error between the measured results and assimilated and HYDRUS model simulated results was 9% and 10%, respectively. The accuracy of the assimilation result was higher than that of the HYDRUS model simulation. Compared with using HYDRUS-1D model alone, the estimating accuracy of surface soil moisture improved significantly by the integration of HYDRUS 1D model and Kalman Filter methods. The root mean square error and average relative error were decreased by 1 and 5 percent points, respectively. Thus, the En-KF algorithm can be used to simulate the dynamic changes of soil moisture in the model. Our experiments demonstrated the great potential of multi-source remote sensing data for the data assimilation of surface soil moistures. It is an effective method of improving the estimating accuracy of soil moisture in arid area.
中文关键词	土壤水分 ; 遥感 ; 同化 ; HYDRUS模型 ; TVDI特征空间
英文关键词	En-KF soil moisture remote sensing assimilation HYDRUS model ensemble Kalman filter feature space TVDI
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:6043993
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/2XILL650/236317
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, 乌鲁木齐, 新疆 830046, 中国
推荐引用方式 GB/T 7714	丁建丽,陈文倩,王璐. 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[J]. 新疆大学,2017,33(14):166-172.
APA	丁建丽,陈文倩,&王璐.(2017).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.农业工程学报,33(14),166-172.
MLA	丁建丽,et al."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".农业工程学报 33.14(2017):166-172.