Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field

Authors: MIERNECKIA, M. - University Of Hamburg; WIGNERON, J. - French National Institute For Agricultural Research; LOPEZ-BAEZA, E. - University Of Valencia; KERR, Y. - Collaborator; DE JEU, R.A.M. - Collaborator; DE LANNOY, G. - National Aeronautics And Space Administration (NASA); Jackson, Thomas; O’NEILL, PEGGY E. - National Aeronautics And Space Administration (NASA); MORAN, R. - University Of Valencia; BIRCHER, S. - Collaborator; LAWRENCE, H. - European Centre For Medium-Range Weather Forecasts (ECMWF); MIALON, A. - Collaborator; AL BITARD, A. - Collaborator; RICHAUME, P. - Collaborator

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2014
Publication Date: 12/30/2014
Publication URL: http://handle.nal.usda.gov/10113/61471
Citation: Mierneckia, M., Wigneron, J., Lopez-Baeza, E., Kerr, Y., De Jeu, R., De Lannoy, G., Jackson, T.J., O’Neill, P., Moran, R., Bircher, S., Lawrence, H., Mialon, A., Al Bitard, A., Richaume, P. 2014. Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field. Remote Sensing of Environment. 154:89-101.

Interpretive Summary: An inter-comparison of seven soil moisture retrieval methods that can be used with satellite-based passive L-band microwave observations was conducted over a vineyard site in Spain using three years of data collected with a tower mounted instrument. The results obtained with the current baseline algorithm developed for the Soil Moisture Active Passive satellite were in very good agreement with the reference soil moisture data set. This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, this algorithm can provide results very close to those obtained from more complex observing systems for these study conditions. These results provide a more robust verification of the algorithm and may result in implementation of the satellite products in agricultural hydrology.

Technical Abstract: The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°-60°). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 = 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 = 0.93, RMSE = 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 = 0.75, RMSE = 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.