气候变化领域集成服务门户(CCPortal): Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

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DOI	10.1016/j.rse.2020.112028
	Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index
	Xu L.; Abbaszadeh P.; Moradkhani H.; Chen N.; Zhang X.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system. © 2020 Elsevier Inc.
英文关键词	Data assimilation; Drought monitoring; Land surface model; Particle filter; Soil moisture
语种	英语
scopus关键词	Drought; Forecasting; Kalman filters; Markov chains; Mean square error; Monitoring; Monte Carlo methods; Probability distributions; Radiometers; Remote sensing; Soil moisture; Markov Chain Monte-Carlo; Moderate resolution imaging spectroradiometer; Multivariate probability distributions; North american land data assimilation systems; Satellite remote sensing; Soil moisture active passive (SMAP); United states climate reference networks; Variable infiltration capacities; Soil surveys; data assimilation; drought stress; ensemble forecasting; evapotranspiration; flash flood; integrated approach; Kalman filter; MODIS; potential evapotranspiration; remote sensing; satellite altimetry; soil moisture; United States; Triticum aestivum
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179146
作者单位	Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering, The University of Alabama, United States; State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China; Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China
推荐引用方式 GB/T 7714	Xu L.,Abbaszadeh P.,Moradkhani H.,et al. Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index[J],2020,250.
APA	Xu L.,Abbaszadeh P.,Moradkhani H.,Chen N.,&Zhang X..(2020).Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index.Remote Sensing of Environment,250.
MLA	Xu L.,et al."Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index".Remote Sensing of Environment 250(2020).