DOI: 10.5194/hess-20-4191-2016
Scopus记录号: 2-s2.0-84992110541
论文题名: Combining satellite observations to develop a global soil moisture product for near-real-time applications
作者: Enenkel M ; , Reimer C ; , Dorigo W ; , Wagner W ; , Pfeil I ; , Parinussa R ; , De Jeu R
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 10 起始页码: 4191
结束页码: 4208
语种: 英语
Scopus关键词: Climate change
; Flood control
; Floods
; Meteorological instruments
; Moisture
; Radiometers
; Satellites
; Soils
; Weather forecasting
; Advanced microwave scanning radiometer
; European Space Agency
; Global correlation
; In-situ observations
; Individual strength
; Operational applications
; Satellite observations
; Surface soil moisture
; Soil moisture
; AMSR-E
; calibration
; data processing
; data set
; global perspective
; MetOp
; real time
; satellite data
; scatterometer
; sensor
; soil moisture
; spatial resolution
; uncertainty analysis
; France
; Kenya
; Senegal
; Spain
英文摘要: The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-realtime (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting. © 2016 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78708
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Vienna University of Technology, Department of Geodesy and Geoinformation, Vienna, Austria; Columbia University, International Research Institute for Climate and Society, New York, NY, United States; UNSW Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia; VanderSat B.V, Noordwijk, Netherlands
Recommended Citation:
Enenkel M,, Reimer C,, Dorigo W,et al. Combining satellite observations to develop a global soil moisture product for near-real-time applications[J]. Hydrology and Earth System Sciences,2016-01-01,20(10)