Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

Abstract

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) OR-CHIDEE 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.

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

“…Modeled soil moisture is generally highly sensitive to the meteorological forcing data used and the land surface model encoded physics [47,48]. This makes comparison of absolute values of observed and modeled SM very challenging, and therefore studies generally focus on the comparison of SM temporal anomalies (e.g., [19,20]), or even in the comparison of observed and modeled brightness temperatures directly (e.g., [49]). The differences found between modeled and observed SM and also among different models support the idea proposed here of leveraging from the natural soil moisture variability captured by satellite observations as a reference for harmonizing SM climate data records so that they become model-independent.…”

SMOS brightness temperature assimilation into the Community Land Model

“…Possible land units consist of vegetation, wetlands, lakes, glaciers and urban areas. Vegetated land units have a single set of soil properties but can be populated by several plant functional types (PFTs), again defined by their percentage of coverage with respect to the entire grid cell (Bonan et al, 2002). We have updated the model PFTs with information from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 (version 5) land cover products, provided at 500 m resolution in sinusoidal projection and containing a classification of each grid cell describing the dominant plant functional type.…”

“…The simulated top-of-atmosphere signal thereby depends on both static and dynamic ancillary data based on input and output of a specific land surface model, e.g. for SMOS retrievals the European Centre for Medium-Range Weather Forecasts HTESSEL land surface model (Balsamo et al, 2009). When using a modified or different land surface model, it can be beneficial to directly assimilate the brightness temperatures in order to use consistent auxiliary information for the land surface model and the radiative transfer model.…”

Validation of SMOS L3 AND L4 Soil Moisture Products In The Remedhus (SPAIN) AND CEMADEN (BRAZIL) Networks

“…Consequently, several approaches have been developed to retrieve land surface soil moisture using satellite measurements at different scales. Nevertheless, validation of these soil moisture products is difficult, mainly due to its dynamic nature, the heterogeneity of the land surface, and the scarce number of available in-situ measurements (Schmugge et al, 1974;Jackson et al, 1984;Entekhabi et al, 1994;Reichle et al, 2004;Jackon et 2012;Albergel et al, 2012;Piles et al, 2014;Gonzáles-Zamora et al, 2016;Gumuzzio et al, 2016;Barella-Ortiz et al, 2017;Sabater et al, 2017;Mousa e Shu, 2020;Portal et al, 2020). SMOS (Soil Moisture and Ocean Salinity) mission, launched on November the 2nd 2009 by the European Space Agency (ESA), obtains frequent soil moisture and ocean salinity global maps using microwave radiometry at L band.…”