Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

Abstract

Abstract. 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 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 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 10 %. 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 −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.

A hydrological model for root zone water storage simulation on a global scale

“…But 20 it also emphasizes the importance and necessity of this work from the following two aspects: 1) remote sensing based approach, e.g. NDII, is so far one of the best available method for root zone information retrieving (Tobin et al, 2017). However, it still limited by the real value reflection.…”

WAYS v1: a hydrological model for root zone water storage simulation on a global scale

“…A global parameter optimization algorithm (Tolson and Shoemaker, 2007), dynamically dimensioned search (DDS), has been applied in this study for model parameter calibration. DDS is designed for computationally expensive optimization problems and has been used in many studies related to distributed hydrological model calibration at global and regional scales (Moore et al, 2010;Kumar et al, 2013;Rakovec et al, 2016;Nijzink et al, 2018;Smith et al, 2018).…”

“…Since the reference data, i.e., ISLSCP II UNH/GRDC data, are at a monthly temporal scale, the runoff simulated by WAYS in the calibration period (1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995) is also averaged to the monthly scale for consistency. The criterion of fit for calibration is the Nash-Sutcliffe efficiency (NSE) coefficient, and the DDS optimization algorithm is run with 2000 iterations for each grid cell for parameter estimation, as suggested by the author of DDS (Tolson and Shoemaker, 2007).…”

Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations

“…The T parameter had been calculated in different ways depending on the application, study area, and sensor used [33]. The SWI has been applied to in situ SSM databases in several studies to obtain field scale RZSM [34][35][36] as well as to active and passive SSM observations to generate several satellite-based RZSM estimates, such as those derived from the European Remote Sensing (ERS) scatterometer [37], the Advanced Scatterometer (ASCAT) [38][39][40], the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) [39,41], the SMOS [42,43], and the Climate Change Initiative (CCI) soil moisture database [41,44].…”