An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets:high sensitivity of L-VOD to above-ground biomass in Africa

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (LVOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse (similar to 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals

Spatial distribution and possible sources of SMOS errors at the global scale

SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)-LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia

SMOS Level 2 soil moisture from observation to products: how to make full use of the available information

National audienc

The prototype SMOS soil moisture algorithm

International audienc

SMOS: First in flight results

International audienceThe SMOS mission aims at allowing a frequent and global monitoring of two geophysical parameters: Soil Mois- ture and sea surface Salinity. These two quantities are key variables for climate studies, weather forecast and water resources management. To achieve this goal, a two dimensional interferometer operating at L band was developed and eventually launched on November 2 2009. The SMOS mission aims at providing a global frequent coverage of the globe with a spatial resolution of 43 km on average (27 km max)

SMOS calibration and validation over the salar de uyuni

International audienc

L-band RFI Detected by SMOS and Aquarius

Ocean salinity and soil moisture are key parameters for understanding the global water cycle, weather, and climate. These parameters are being measured with spaceborne radiometers operating in the L-band window at 14001427 MHz. Although man-made activity in this band is prohibited, radio frequency interference (RFI) is still a problem over significant portions of the earth. This paper reports a comparison of the RFI environment in this window as observed by two L-band radiometer systems, Aquarius and Soil Moisture and Ocean Salinity. The observed RFI environment depends on the sources and also on the characteristics of the instrument. Comparing the observations provides insight into the extent of the problem (actual sources), the influence of the instrument on the observation of RFI, and on potential ways of mitigating the effects. As this report shows, the global distribution of RFI is largely consistent between the two instruments, but the details, especially at low levels of RFI, depend on the characteristics of the instrument

SMOS and AMSR-E brightness temperature cross-validation over the Salar de Uyuni

National audienc

Calibration and validation of SMOS brightness emperature over the Salar de Uyuni

International audienceThe Salar de Uyuni is the largest salt flat in the world. It is located in the Bolivian altiplane at a height of about 3700 m between latitudes 19o45' S and 20o40' S and between longitudes 68o17'W and 66o45'W. Its surface is about 9 600 km2 (several tenths the SMOS footprint). It is located in a rather uninhabited area where no RFI is to be expected. Potosi is the closest village. Salar's climate is cold and dry, being characterized by low temperatures, low relative humidity levels and low precipitation. The rainfall is very low and concentrated from December to March. During the austral summer (from December to March), the surface can be covered by a thin water layer. This water layer disappears in the dry season, from April to November, leaving the Salar surface extremely flat and smooth. Analysis of AMSR-E data at 6.9, 10 and 18 GHz shows that microwave emissivity over the Salar is spatially homogeneous. Temporal analysis of brightness temperature shows that at vertical polarization is basically dependent on soil temperature and that emissivity remains high and constant during the dry period (emissivity 0.93-0.94). Furthermore brightness temperature at vertical polarization was shown to be very similar at 6.9 and 10.7 GHz, indicating a very low penetration depth, similar behavior is to be expected for emissivity at 1.4 GHz. Vertically polarised brightness temperature at 6.9GHz was simulated with annual rmse of 1.1K. Further study is needed to characterize H-pol. A piece of the Salar has been taken for its dielectric constant characterisation (scheduled November 2009). The salar de Uyuni is therefore an excellent site for the calibration and validation of the SMOS brightness temperature. In this presentation, SMOS brightness temperatures measured over the Salar will be shown and compared to those of models and also compared to AMSR-E data