Location: Hydrology and Remote Sensing LaboratoryTitle: An approach to constructing a homogeneous time series of soil mositure using SMOS) Author
Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer reviewed journal
Publication Acceptance Date: 4/1/2013
Publication Date: 1/2/2014
Publication URL: http://handle.nal.usda.gov/10113/59869
Citation: Leroux, D., Kerr, Y., Sahoo, A., Wood, E., Bindllish, R., Jackson, T.J. 2014. An approach to constructing a homogeneous time series of soil mositure using SMOS. IEEE Transactions on Geoscience and Remote Sensing. 52:393-405. Interpretive Summary: Overlapping soil moisture time series derived from two satellite microwave radiometers were used to generate a soil moisture time series from 2003 to 2010. Individual satellites have limited life spans and typically utilize different system configurations. Longer time series of satellite-based soil moisture are required for climate related analysis. Two statistical methodologies for generating long homogeneous time series of soil moisture were evaluated using in situ data sets, while the other one technique gave better results in terms of correlation and copulas gave better results in terms of absolute errors compared to the ground measurements. The results support the further development of these statistical methodologies to integrate more diverse satelite observations into a single record that would support applications such as data assimilation or climate change assessment that require consistent products.
Technical Abstract: Overlapping soil moisture time series derived from two satellite microwave radiometers (SMOS, Soil Moisture and Ocean Salinity; AMSR-E, Advanced Microwave Scanning Radiometer - Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, CDF matching and copulas, are based on the same statistical theory but the first makes an assumption that is not needed by the second. Both methods are calibrated in 2010 and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However CDF matching improves the correlation whereas copulas improve the root mean square error.