Van der Schalie R., Kerr Yann, Wigneron J. P., Rodriguez-Fernandez N. J., Al-Yaari A., De Jeu R. A. M. (2016). Global SMOS soil moisture retrievals from the Land Parameter Retrieval Model. International Journal of Applied Earth Observation and Geoinformation, 45 (B), p. 125-134. ISSN 0303-2434.
Titre du document
Global SMOS soil moisture retrievals from the Land Parameter Retrieval Model
Van der Schalie R., Kerr Yann, Wigneron J. P., Rodriguez-Fernandez N. J., Al-Yaari A., De Jeu R. A. M.
Source
International Journal of Applied Earth Observation and Geoinformation, 2016,
45 (B), p. 125-134 ISSN 0303-2434
A recent study by Van der Schalie et al. (2015) showed good results for applying the Land Parameter Retrieval Model (LPRM) on SMOS observations over southeast Australia and optimizing and evaluating the retrieved soil moisture (theta in m(3) m(-3)) against ground measurements from the OzNet sites. In this study, the LPRM parameterization is globally updated for SMOS against modelled theta from MERRA-Land (MERRA) and ERA-Interim/Land (ERA) over the period of July 2010-December 2010, mainly focusing on two parameters: the single scattering albedo (omega) and the roughness (h). The Pearson's coefficient of correlation (r) increased rapidly when increasing the omega up to 0.12 and reached a steady state from thereon, no significant spatial pattern was found in the estimation of the single scattering albedo, which could be an artifact of the used parameter estimation procedure, and a single value of 0.12 was therefore used globally. The h was defined as a function of theta and varied slightly for the different angle bins, with maximum values of 1.1-1.3 as the angle changes from 42.5 degrees to 57.5 degrees. This resulted in an average r of 0.51 and 0.47, with a bias (m(3) m(-3)) of -0.02 and -0.01 and an unbiased root mean square error (ubrmse in m(3) m(-3)) of 0.054 and 0.056 against MERRA (ascending and descending). For ERA this resulted in an r of 0.61 and 0.53, with a bias of -0.03 and an ubrmse 0.055 and 0.059. The resulting parameterization was then used to run LPRM on SMOS observations over the period of July 2010-December 2013 and evaluated against SMOS Level 3 (L3) theta and available in situ measurements from the International Soil Moisture Network (ISMN). The comparison with 13 shows that the LPRM theta retrievals are very similar, with for the ascending set very high r of over 0.9 in large parts of the globe, with an overall average of 0.85 and the descending set performing less with an average of 0.74, mainly due to the negative rover the Sahara. The mean bias is 0.03, with an ubrmse of 0.038 and 0.044. In this study there are three major areas where the LPRM retrievals do not perform well: very dry sandy areas, densely forested areas and over high latitudes, which are all known limitations of LPRM. The comparison against in situ measurement from the ISMN give very similar results, with average r for LPRM of 0.65 and 0.61 (0.64 and 0.59 for 13) for the ascending and descending sets, while having a comparable bias and ubrmse over the different networks. This shows that LPRM used on SMOS observations produce theta retrievals with a similar quality as the SMOS L3 product.
