1. The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E
- Author
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Matthias Drusch, Nemesio Rodriguez-Fernandez, Susanne Mecklenburg, Y. H. Kerr, R.A.M. de Jeu, Jean-Pierre Wigneron, R. van der Schalie, Robert Parinussa, Amen Al-Yaari, Faculty of Earth and Life Sciences, VU University Amsterdam, Space Technology Center, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), European Space Agency (ESA), European Space Research and Technology Centre (ESTEC), VU University, Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (ISPA), Earth and Climate, and Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU)
- Subjects
010504 meteorology & atmospheric sciences ,Mean squared error ,télédétection ,[SDV]Life Sciences [q-bio] ,0208 environmental biotechnology ,Soil Science ,acquisition de données ,02 engineering and technology ,radiomètre ,01 natural sciences ,Normalized Difference Vegetation Index ,remote sensing ,Radiative transfer ,humidité du sol ,Precipitation ,Computers in Earth Sciences ,Water content ,0105 earth and related environmental sciences ,Remote sensing ,Radiometer ,Single-scattering albedo ,capteur smos ,Geology ,15. Life on land ,radiometer ,020801 environmental engineering ,réseau de mesures ,ndvi ,Environmental science ,Scale (map) ,soil moisture and ocean salinity - Abstract
This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a significant increase (from 0.48 to 0.55) in temporal correlation coefficient over 16 in situ networks. This finding was confirmed through a large scale (50°N–50°S) precipitation based verification technique, the so-called Rvalue, which shows a superior performance of the newly developed AMSR-E LPRMN product. Additionally, the linear scaling of AMSR-E LPRMN to the SMOS LPRM leads to further reducing the ubrmse from 0.09 to 0.06 m3 m− 3 and the average bias from 0.14 to 0.00 m3 m− 3 over these stations. The AMSR-E LPRMN was furthermore compared against the top layer of two re-analysis models (i.e. from the Modern-Era Retrospective analysis for Research and Applications-Land and ERA-Interim/Land models) generally demonstrating increased correlation coefficients and reduced ubrmse with the exception of the challenging areas. As a result, this study shows the significant potential of SMOS LPRM to be a successful integrator to build a long term soil moisture record based on multiple passive microwave sensors.
- Published
- 2017