International audienceThe Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5$^{circ}$, flags and quality indices, and other parameters o- interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises

No abstract

Disaggregation based on Physical And Theoretical 4 scale Change (DisPATCh) is an algorithm dedicated to the dis-5 aggregation of soil moisture observations using high-resolution 6 soil temperature data. DisPATCh converts soil temperature fields 7 into soil moisture fields given a semi-empirical soil evaporative 8 efficiency model and a first-order Taylor series expansion around 9 the field-mean soil moisture. In this study, the disaggregation 10 approach is applied to soil moisture and ocean salinity (SMOS) 11 data over the 500 km by 100 km AACES (Australian Airborne 12 Calibration/validation Experiments for SMOS) area. The 40-km 13 resolution SMOS surface soil moisture pixels are disaggregated 14 at 1-km resolution using the soil skin temperature derived from 15 moderate resolution imaging spectroradiometer (MODIS) data, 16 and subsequently compared with the AACES intensive ground 17 measurements aggregated at 1-km resolution. The objective is to 18 test DisPATCh under various surface and atmospheric conditions. 19 It is found that the accuracy of disaggregation products varies 20 greatly according to season: while the correlation coefficient be-21 tween disaggregated and in situ soil moisture is about 0.7 during 22 the summer AACES, it is approximately zero during the winter 23 AACES, consistent with a weaker coupling between evaporation 24 and surface soil moisture in temperate than in semi-arid climate. 25 Moreover, during the summer AACES, the correlation coefficient 26 between disaggregated and in situ soil moisture is increased from 27 0.70 to 0.85, by separating the 1-km pixels where MODIS temper-28 ature is mainly controlled by soil evaporation, from those where 29 MODIS temperature is controlled by both soil evaporation and 30 vegetation transpiration. It is also found that the 5-km resolution 31 atmospheric correction of the official MODIS temperature data 32 has a significant impact on DisPATCh output. An alternative at-33 mospheric correction at 40-km resolution increases the correlation 34 coefficient between disaggregated and in situ soil moisture from 35 0.72 to 0.82 during the summer AACES. Results indicate that

et al.. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, Elsevier, 2012, Abstract A disaggregation algorithm is applied to 40 km resolution SMOS (Soil Moisture and Ocean Salinity) surface soil moisture using 1 km resolution MODIS (MODerature resolution Imaging Spectroradiometer), 90 m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), and 60 m resolution Landsat-7 data. DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived soil evaporative efficiency (ratio of actual to potential evaporation) and near-surface soil moisture. The objective is three-fold: (i) evaluating DISPATCH at a range of spatial resolutions using readily available multi-sensor thermal data, (ii) deriving a robust calibration procedure solely based on remotely sensed data, and (iii) testing the linear or nonlinear behaviour of soil evaporative efficiency. Disaggregated soil moisture is compared with the 0-5 cm in situ measurements collected each month land area in Catalunya, Spain. The target downscaling resolution is set to 3 km using MODIS data and to 100 m using ASTER and Landsat data. When comparing 40 km SMOS, 3 km disaggregated and 100 m disaggregated data with the in situ measurements aggregated at corresponding resolution, results indicate that DISPATCH improves the spatio-temporal correlation with in situ measurements at both 3 km and 100 m resolutions. A yearly calibration of DISPATCH is more efficient than a daily calibration. Assuming a linear soil evaporative efficiency model is adequate at kilometric resolution. At 100m resolution, the very high spatial variability in the irrigated area makes the linear approximation poorer. By accounting for non-linearity effects, the slope of the linear regression between disaggregated and in situ measurements is increased from 0.2 to 0.5. Such a multi-sensor remote sensing approach has potential for operational multi-resolution monitoring of surface soil moisture and is likely to help parameterize soil evaporation at integrated spatial scales.

This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m 3 /m 3 to 0.046 m 3 /m 3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.

No abstract

International audienceGlobal Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes ("tropical humid", "temperate humid", etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ("desert temperate", "desert tropical", etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI ≤ 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI >1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI = 6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10 years, which can be used to study land-atmosphere processes

No abstract