Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula
- 1Laboratoire de Météorologie Dynamique du CNRS, IPSL, École Polytechnique, Université Paris-Saclay, France
- 2European Centre for Medium-Range Weather Forecasts, Reading, UK
- 3Barcelona Expert Center, Institute of Marine Sciences, CSIC, Pg. Marítim de la Barceloneta 37-49, 08003 Barcelona, Spain
- 4CNRM-GAME (Météo-France, CNRS), Toulouse, France
- 5Centre National de la Recherche Scientifique (CNRS)
- acurrently at: Image Processing Lab (IPL), Universitat de València, C/Catedrático José Beltran 2, 46980 Valencia, Spain
Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM.
Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies.