The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data

被引:24
作者
Tavakol, Ameneh [1 ]
McDonough, Kelsey R. [2 ,3 ]
Rahmani, Vahid [4 ]
Hutchinson, Stacy L. [4 ]
Hutchinson, J. M. Shawn [5 ]
机构
[1] Univ Calif Davis, Dept Civil & Environm Engn, Davis, CA 95616 USA
[2] Univ Newcastle, Ctr Water Secur & Environm Sustainabil CWSES, Callaghan, NSW 2308, Australia
[3] Univ Newcastle, Sch Engn, Callaghan, NSW 2308, Australia
[4] Kansas State Univ, Dept Biol & Agr Engn, 1016 Seaton Hall,920 N 17thSt, Manhattan, KS 66506 USA
[5] Kansas State Univ, Dept Geog & Geospatial Sci, 1002 Seaton Hall,920 N 17th St, Manhattan, KS 66506 USA
关键词
Remote sensing; Model-based soil moisture retrieval; In situ observation; SMAP; NLDAS; GLDAS; AMSR; SMOS; North American Soil moisture database; International soil moisture network; ESA CCI; ASCAT; ASAR; Resolution; Soil moisture application; LAND INFORMATION-SYSTEM; WIRELESS SENSOR NETWORK; HEIHE RIVER-BASIN; IN-SITU; AMSR-E; DATA ASSIMILATION; UNITED-STATES; HYDROLOGICAL MODEL; MICROWAVE RADIOMETER; SUBGRID VARIABILITY;
D O I
10.1016/j.rsase.2021.100649
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil moisture is a critical component of the climate system due to the link it provides between atmospheric and terrestrial energy, water, and carbon cycles. A better understanding of floods, droughts, and heatwaves, as well as weather forecasting, is highly dependent on the knowledge of soil moisture variations and its impacts. Ground-based soil moisture datasets are crucial for climatological analysis, as are model- and satellite-based data. However, ground-based data are sparse in spatial and temporal coverage, and often include missing data. Since ground-based observations are limited in both time and space, model- and satellite-based measurements often serve as alternatives. Here in this paper, the interest is to provide an overview of the state-of-theart open-access soil moisture datasets at various spatial and temporal scales. Despite the recent progress in producing model-based and satellite-based data, there are many potentials to improve the quality of the data. These sets of data can be used for forecasting weather and climate variability, monitoring the influence of climate change on an ecosystem, drought monitoring and prediction, water resources management, agricultural production, and more.
引用
收藏
页数:24
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