|TOBIN, K. - Texas A&M University|
|TORRES, R. - Texas A&M University|
|BENNETT, M.E. - Texas A&M University|
Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/7/2017
Publication Date: 9/15/2017
Citation: Tobin, K., Torres, R., Crow, W.T., Bennett, M. 2017. Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS. Hydrology and Earth System Sciences. 21:4403-4417. https://doi.org/10.5194/hess-21-4403-2017.
Interpretive Summary: Accurate soil moisture information is critical for a wide range of agricultural applications including: drought monitoring, water resource forecasting and irrigation scheduling. The need for such information is increasingly being met through the use of satellite-derived surface soil moisture estimates. However, these estimates have a significant limitation in that they provide a direct measure of soil moisture only within the top few inches of the soil column. In response to this problem, a simple mathematical algorithm has been derived which claims to estimate root-zone (zero to 40 inches in depth) soil moisture levels based solely only on a time series of (zero to 2 inches in depth) surface soil moisture retrievals. This paper explicitly tests this algorithm using long-term ground and remotely-sensed soil moisture data sets available within the United States and clarifies conditions under which the algorithm does (and does not) work. As a result, results presented here are directly relevant for on-going attempts to improve our ability to track the availability of root-zone soil water within agricultural regions of the United States.
Technical Abstract: his 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; CCI-Combined). All of these products were quarter degree and 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 U.S. 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 U.S. 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 reasonable high lagged correlation coefficient (r>0.5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RSME) and calculation based on the NDVI value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI based estimates. 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. Average Nash-Sutcliffe coefficients (NS) for ARM ranged from -0.1 to 0.3 and were similar to the results obtained from the USCRN network (0.2 to 0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1 to 0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of root mean square error (RMSE; in volumetric soil moisture) ARM values actually outperformed those from other networks (0.02 to 0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher ranging (0.05 to 0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.