The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E

• Published in: Remote Sensing of Environment
• Main Authors: van Der Schalie, Robin, de Jeu, Richard A. M., Kerr, Yann H., Wigneron, Jean-Pierre, Rodriguez‐fernandez, Nemesio, Al Yaari, Amen, Parinussa, Robert Mathijs, Mecklenburg, Susanne, Drusch, Matthias
• Other Authors: Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam Amsterdam (VU), Space Technology Center, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Agence Spatiale Européenne = European Space Agency (ESA), European Space Research and Technology Centre (ESTEC)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: remote sensing; radiometer; soil moisture and ocean salinity; télédétection; radiomètre; acquisition de données; capteur smos; ndvi; réseau de mesures; humidité du sol; [SDV]Life Sciences [q-bio]; Arctic; albedo
• Online Access: https://hal.science/hal-01595237; https://doi.org/10.1016/j.rse.2016.11.026

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 ...