Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model |
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| Institution: | 1. Centre dEtudes Spatiales de la BIOsphre (CESBIO), UMR5126, BPI 2801, 31401 Toulouse Cedex 9, France;2. INRA, UR1263 ISPA, F-33140 Villenave d''Ornon, Centre INRA Bordeaux, Aquitaine, France;1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya (UPC), IEEC/UPC and SMOS Barcelona Expert Center (SMOS-BEC), 08034 Barcelona, Spain;3. Agrosphere Institute, Forschungszentrum Jülich, 52428 Jülich, Germany;4. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA;1. Consultant to ESA, Sweden;2. Institut National de la Recherche Agronomique, France;3. Transmissivity, The Netherlands;4. European Centre for Medium-Range Weather Forecasts, United Kingdom;5. Centre d''Etudes Spatiales de la Biosphere, France;6. European Space Agency (ESA-ESTEC), The Netherlands;7. VU University Amsterdam, The Netherlands;1. Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium;2. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA;3. Centre d''Etudes Spatiales de la Biosphère, Toulouse, France;4. European Space Agency, Noordwijk, The Netherlands;5. Department of Civil Engineering, Monash University, Victoria, Australia;6. Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany;7. Land Surface Hydrology Group, Princeton University, Princeton, NJ, USA;8. Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD, USA;1. INRA, UMR1391 ISPA, Villenave d''Ornon, France;2. CESBIO, CNES/CNRS/IRD/UPS, UMR 5126, Toulouse, France;3. Faculty of Earth and Life Sciences, VU University Amsterdam (VUA), Amsterdam, Netherlands;4. Transmissivity B.V., Space Technology Center, Noordwijk, Netherlands;5. Sorbonne Universités, UMR 7619 METIS, UPMC/CNRS/EPHE, Paris, France |
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| Abstract: | 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 (θ in m3 m−3) against ground measurements from the OzNet sites. In this study, the LPRM parameterization is globally updated for SMOS against modelled θ 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 (ω) and the roughness (h). The Pearson's coefficient of correlation (r) increased rapidly when increasing the ω 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 θ and varied slightly for the different angle bins, with maximum values of 1.1–1.3 as the angle changes from 42.5° to 57.5°.This resulted in an average r of 0.51 and 0.47, with a bias (m3 m−3) of −0.02 and −0.01 and an unbiased root mean square error (ubrmse in m3 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) θ and available in situ measurements from the International Soil Moisture Network (ISMN). The comparison with L3 shows that the LPRM θ 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 r over 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 L3) 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 θ retrievals with a similar quality as the SMOS L3 product. |
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| Keywords: | Remote sensing Passive microwave radiometry Soil moisture Soil moisture and ocean salinity (SMOS) Land Parameter Retrieval Model (LPRM) |
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