DOI: 10.5194/hess-21-4403-2017
Scopus记录号: 2-s2.0-85029030797
论文题名: Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS
作者: Tobin J ; K ; , Torres R ; , Crow T ; W ; , Bennett E ; M
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 9 起始页码: 4403
结束页码: 4417
语种: 英语
Scopus关键词: Atmospheric radiation
; Bandpass filters
; Climate change
; Constrained optimization
; Digital storage
; Mean square error
; Moisture
; NASA
; Passive filters
; Satellites
; Soil moisture
; Soils
; Space flight
; Atmospheric radiation measurement programs
; Nash-Sutcliffe coefficient
; Normalized difference vegetation index
; Root zone soil moistures
; Satellite soil moisture
; Us climate reference networks
; US Department of Energy
; Volumetric soil moistures
; Soil surveys
; accuracy assessment
; AMSR-E
; data set
; NDVI
; optimization
; remote sensing
; rhizosphere
; satellite data
; satellite sensor
; soil moisture
英文摘要: This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T ). We examined five different eras (1997-2002; 2002-2005; 2005-2008; 2008-2011; 2011-2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25g cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25g cm), Soil Climate Analysis Network (SCAN; 20.32g cm), SNOwpack TELemetry (SNOTEL; 20.32g cm), and the US Climate Reference Network (USCRN; 20g cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10g %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash-Sutcliffe coefficients (NSs) for ARM ranged from g'0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2-0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1-0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02-0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05-0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions. © Author(s) 2017. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79070
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Texas AandM International University, Center for Earth and Environmental Studies, Laredo, TX, United States; United States Department of Agriculture, Agricultural Research Service Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
Recommended Citation:
Tobin J,K,, Torres R,et al. Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS[J]. Hydrology and Earth System Sciences,2017-01-01,21(9)