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Abstract: The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4GHz, from which global soil moisture and vegetation optical depth (τ) products are retrieved. This paper presents a study of SMOS' τ product in 2010 and 2011 for crop zones of the USA. Retrieved τ values for 504 crop nodes were compared to optical/IR vegetation indices from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and a Normalised Difference Water Index (NDWI) product. τ values were observed to increase during the growing season and decrease during senescence in these areas, as did MODIS vegetation indices. SMOS' τ values generally peaked later than MODIS LAI values, with an estimated time difference of about 19days. A linear regression between τ and the MODIS products was carried out for each node and values of the determination coefficient, R 2, slope, b′ and intercept, b″ were found. The average R 2 value varied from 0.32 to 0.35 for the different vegetation indices. The linear regression between LAI and τ produced an average slope of b′=0.06, and an average intercept of b″=0.14. The effects of crop fraction and dominant crop type were investigated and crop fraction was found to have a low effect on R 2 values. R 2values appeared to be lower for wheat and hay and higher for corn. b′ and b″ values had higher standard deviations for wheat but were generally close to the mean values for corn, soybean and hay. [Copyright &y& Elsevier]

In this paper, a numerical modeling approach was used to evaluate the semiempirical H–Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H–Q model uses four parameters, HR, QR, NRH, and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights \sigma and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H –Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H–Q model. It was found that the retrieved HR could be related directly to Zs = \sigma^2/Lc and that QR, NRV, and NRH were dependent on HR. HR was found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H–Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of \sim\! 0.03\ \m^3/\m^3, which was within the desired 0.04-\m^3/\m^3 error margin for the SMOS mission. [ABSTRACT FROM AUTHOR]

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters: HR , QR, and NR. In many studies, the two parameters QR and NR are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that QR could be set to zero. Second, a refinement of the L-MEB soil model, considering values of NR for both polarizations (namely, NRV and NRH), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters HR and (NRH - NRV) to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions. [ABSTRACT FROM AUTHOR]

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff. [ABSTRACT FROM AUTHOR]

The flood forecasting component of the Copernicus Emergency Management Service Early Warning System (CEMS-EWS Floods), run at the European Centre for Medium Range Weather Forecasts (ECMWF), provides medium range flood forecasts at both the European and global scales. Forecast accuracy correlates strongly with accurately representing the initial status of hydrological variables including soil moisture. Initial soil moisture status within CEMS-EWS Floods is currently obtained by a hydrological analysis of point scale temperature and precipitation observations. Remotely sensed soil moisture from SMOS may provide a better representation of the initial soil moisture status than the current methodology.ECMWF, in partnership with the European Space Agency (ESA), are undertaking an 18 month project to demonstrate the potential use of SMOS soil moisture data within CEMS-EWS Floods. This will use the level 1 Near Real Time (NRT) SMOS soil moisture Neural Network product trained on the ECMWF land surface scheme. The work will firstly perform a long term comparison against existing re-analysis datasets, including the CEMS-EWS Floods analysis in Europe and ERA5 globally, to ascertain spatial and temporal correlations. Data assimilation experiments will then be conducted within the European and global components of CEMS-EWS Floods. The resulting changes in streamflow accuracy will be analysed to conclude about the benefits offered by assimilating SMOS soil moisture data.We will present the outline of this project as well as some of the initial results from the inter-comparison with other soil moisture re-analysis products. [ABSTRACT FROM AUTHOR]

In the above paper (ibid., vol. 49, no. 4, pp. 1177-1189, Apr. 2011), there is an error in equation (7). The explanation and corrected equation are presented here. [ABSTRACT FROM PUBLISHER]

Abstract: The SMOS (Soil Moisture and Ocean Salinity) mission was launched on November 2, 2009. Over the land surfaces, simultaneous retrievals of surface soil moisture (SM) and vegetation characteristics made from the multi-angular and dual polarization SMOS observations are now available from Level-2 (L2) products delivered by the European Space Agency (ESA). Therefore, first analyses evaluating the SMOS observations in terms of Brightness Temperatures (TB) and L2 products (SM and vegetation optical depth TAU) can be carried out over several calibration/validation (cal/val) sites selected by ESA over all continents. This study is based on SMOS observations and in situ measurements carried out in 2010 over one of the main SMOS cal/val sites in Europe: the VAS (Valencia Anchor Station) site in the region of Utiel–Requena, close to Valencia, Spain. The main vegetation types in the region are vineyards, orchards and natural Mediterranean vegetation. The SMOS observations were analyzed in conjunction with those carried out by the L-band ELBARA-II radiometer over a vineyard which is considered as representative of the main land use of the VAS site. Time series of TB and retrievals of SM and TAU based on both the SMOS (L2 products) and the ELBARA-II observations were compared and evaluated against in situ measurements. A good agreement was found between the time variations in TB and in the retrieved SM values computed over the site from the SMOS and ELBARA-II observations (the determination coefficient R2 was >0.88 for the TB values and R2 >0.64 for the retrieved SM values). However, it was found that the SMOS L2 SM products underestimated the SM values retrieved from ELBARA-II by ~0.2m3/m3. It is likely this offset can be partially explained by differences between the observed scenes: while the ELBARA-II footprint includes a single vineyard, the heterogeneous SMOS footprint includes not only a large number of vineyards but also a natural Mediterranean vegetation with persistent leaves overlaying rocky soils. The time variations in TAU retrieved from the ELBARA-II observations were found to be closely related to those of the NDVI (Normalized Difference Vegetation Index) vegetation index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) (R2 =0.61) and revealed the vegetation cycle over the year. Conversely, the time variations in the SMOS Level-2 TAU product did not reveal any trends in relation to the vegetation development over the site. [Copyright &y& Elsevier]