5+ years of fine-scale soil moisture estimates available!!

At Barcelona Expert Center (BEC) we are able to provide a Level 4 (L4) Surface Soil Moisture (SSM) product with 1 km spatial resolution that meets the requirements of land hydrology applications. To do so, we use a downscaling method that combines highly-accurate, but low-resolution, SMOS radiometric information with high resolution, but low sensitivity, visible-to-infrared imagery to SSM across spatial scales. A sample L4 SSM map from September 1, 2014 (6 AM) is shown in Figure 1.

Fig. 1. SMOS-BEC L4 product from September 1, 2014 (6 AM).

This downscaling approach was first presented in [1] along with results of its application to a set of SMOS images acquired during the commissioning phase over the Oznet network, South-East Australia. Using reprocessed SMOS data obtained with the latest L1 and L2 processors, we have further developed and validated this technique; we now use SMOS polarimetric and multi-angular information in the downscaling method, which results in improved fine-scale soil moisture estimates [2].
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New study on the detection of cold-core rings in the Gulf Stream area using remote sensing platforms

The Gulf Stream plays a major role in the meridional transport of heat and salt across the North Atlantic Ocean. The Gulf Stream acts as a barrier between the cold (10-18 °C) and relatively fresh (salinity around 30-32 in the practical salinity scale) waters of the Labrador Current and the warm (23 °C), salty (36), clear, and unproductive waters of the Sargasso Sea. After leaving Cape Hatteras, the Gulf Stream forms large-amplitude meanders that may loop back onto themselves and break off the stream forming detached rings. Warm-core anti-cyclonic rings bring significant amounts of warm tropical water to the continental slope and shelf seas north of the Gulf Stream. Similarly, cold-core cyclonic rings bring cold, nutrient-rich shelf water, to the biologically barren Sargasso Sea waters. Detection of cold-core rings from satellite data has been quite elusive so far as the surface temperature signature rapidly disappears.

Figure 1

Sea Surface salinity on August 23, 2015 according to various SSS products with superimposed OSCAR velocities. The plot on (a) correspond to the one-degree binned Aquarius L3 map. The other three maps show the fusion of the map shown in (s) with: AVISO SSH (b); SMOS SSS (c); and AVHRR SST (d).

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Nodal sampling: removing tails and ripples from SMOS Brightness Temperatures

Since the beginning of SMOS mission, one of the problems that has strongly affected the quality of the retrieval of SSS from SMOS Brightness Temperatures (BT) is the presence of large human-generated Radio Frequency Interference (RFI) sources, as shown in the following figure:

Image acquired over a coastal area in Europe; several strong RFI sources and their trails are very noticeable

Image acquired over a coastal area in Europe; several strong RFI sources and the associated tails are very noticeable

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A review of microwave interferometric radiometry in remote sensing

Radio Science has recently published “Microwave interferometric radiometry in remote sensing: An invited historical review” by M. Martín-Neira, D. M. LeVine, Y. Kerr, N. Skou, M. Peichl, A. Camps, I. Corbella, M. Hallikainen, J. Font, J. Wu, S. Mecklenburg, and M. Drusch. The paper (Radio Science, volume 49, issue 6, pages 415–449, June 2014, DOI: 10.1002/2013RS005230) is led by Manuel Martín-Neira, the SMOS instrument (MIRAS) principal engineer, and is co-authored by three SMOS-BEC members: Adriano Camps, Ignasi Corbella and Jordi Font. We copy below the paper’s abstract:

The launch of the Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009 marked a milestone in remote sensing for it was the first time a radiometer capable of acquiring wide field of view images at every single snapshot, a unique feature of the synthetic aperture technique, made it to space. The technology behind such an achievement was developed, thanks to the effort of a community of researchers and engineers in different groups around the world. It was only because of their joint work that SMOS finally became a reality. The fact that the European Space Agency, together with CNES (Centre National d’Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnológico e Industrial), managed to get the project through should be considered a merit and a reward for that entire community. This paper is an invited historical review that, within a very limited number of pages, tries to provide insight into some of the developments which, one way or another, are imprinted in the name of SMOS.

David and Goliath

This image of the first ESA ground tests of a MIRAS demonstrator was selected for the cover of the Radio Science issue. The online version of the paper can be seen at http://onlinelibrary.wiley.com/doi/10.1002/2013RS005230/full


New service available: Singularity Analysis

Maybe you have seen the singularity exponents maps we are offering in this CP34-BEC data server. Singularity analysis is a technique for estimating, at any point, the singularity exponent of a signal. Singularity exponents, usually denoted by h, are dimensionless variables providing information about the local regularity (if positive) or irregularity (if negative) of the signal at any given point. When h is integer it means that the function has h continuous derivatives, while non-integer values indicate a more complex topological situation.

Why should we be interested in such a mathematical, abstract concept? Because if a flow exhibits horizontal turbulence – and the ocean is a quasi-2D turbulent flow at scales greater that a few kilometers – singularity exponents derived from any ocean scalar are the same and, in fact, they represent the streamlines of the flow! (Turiel et al., Physical Review Letters, 2005; Isern-Fontanet et al, Journal of Geophysical Research, 2007; Nieves et al, Geophysical Research Letters, 2007; Turiel et al., Remote Sensing of Environment, 2008; Turiel et al., Ocean Science, 2009).

Microwave OI SST map (AMSRE-E+TMI, derived by Remote Sensing Systems) corresponding to January 1st, 2005

Microwave OI SST map (AMSRE-E+TMI, derived by Remote Sensing Systems) corresponding to January 1st, 2005

Map of associated singularity exponents

Map of associated singularity exponents

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Inter-comparison of SMOS and Aquarius observations

Passive microwave remote sensing at L-band is considered to be the most suitable technique to measure soil moisture and ocean salinity from space. The ESA’s SMOS and the NASA’s Aquarius/SAC-D are the two first satellite missions, carrying L-band radiometers on-board, measuring the global Earth’s surface as brightness temperatures (TB). The two radiometers have important differences in the architecture of the instruments as well as in their operation principles. In order to verify the continuity and the consistency of the data over the entire dynamic range of observations, a comparison between one year of SMOS and Aquarius measured TB has been performed over key regions over land (Amazon rainforest and Sahara desert), ice (Dome-C in Antarctica) and sea (South Pacific ocean).

Click here to observe selected regions in Google Earth.

A global view of the comparison is shown in Fig. 1, which displays the annual mean of the two radiometers for the three Aquarius incidence angles (inner 29.36º, middle 38.49º and outer 46.29º beams). In South Pacific, Dome-C and Sahara, higher incidence angles imply lower TB at horizontal polarization and higher TB at vertical polarization. However, in the Amazon, the TB variation with incidence angle and polarization is not clear due to the vegetation scattering. As expected, there is a small difference between polarizations (TBV-TBH) for vegetation-covered soils.


Fig. 1 Aquarius TB vs. SMOS TB at (a) horizontal and (b) vertical polarizations.

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Global Land products available!

A Soil Moisture (SM) Level 3 product has been created at BEC, and it is now available online.

The Level 3 product is generated from the operational ESA Level 2 Soil Moisture User Data Product (UDP) that include geophysical parameters, a theoretical estimate of their accuracy, and a set of product flags and descriptors.

The nominal L2 SM data is first filtered in order to ensure the quality of our L3 products. Soil Moisture values are rejected if: i) no value has been retrieved for that given gridpoint; ii) the retrieval is negative; iii) the retrieval is outside the extended range; or iv) the associated Data Quality Index (DQX) is larger than 0.07 m³/m³ . Next, a weighted average is performed to bin the data to a EASE-ML grid with cells of 25 km (see documentation for additional information). Products are provided in netcdf format.

SMOS soil moisture L3-days binned maps. The plots show the soil moisture evolution during the Bosnian floods in May 2014. Heavy rains was received from 14 to 16 of May 2014

SMOS soil moisture L3-days binned maps. The plots show the soil moisture evolution during the Bosnian floods in May 2014. Heavy rains was received from 14 to 16 of May 2014

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A blending algorithm using SST data to improve SMOS SSS maps

Data fusion is a process for combining two, or more, sources of information to improve the representation of a given system. In a recent paper, data fusion has been used to remove noise from SMOS sea surface salinity (SSS) products, by fusing SMOS data with sea surface temperature (SST) fields.

Our approach is justified by the correspondence between the singularity exponents of SSS and SST. The singularity exponent is a non-dimensional measure of the regularity or irregularity of a field in a given point. The value of the singularity exponent increases with the smoothness of a field. The correspondence between the singularity exponents of SST and SSS implies the existence of a local functional dependence between these two variables. This correspondence can be illustrated using data of a numerical simulation (OFES, Ocean General Circulation Model for the Earth Simulator).

Figure 1 shows two conditioned histograms. The one in the top illustrates the histogram of SSS conditioned by each given value of SST. The conditioned histogram looks like a superposition of narrow lines. It indicates that, while strong local SSS-SST correlations exist, these relations do change from one region to the other. On the contrary, the conditioned histogram of SSS singularity exponents conditioned by the value of the singularity exponents of SST indicates that a unique correlation exists all over the world ocean. In fact, the slope of the maximum probability line is close to one, indicating an almost perfect identity between the singularity exponents of SST and SSS.


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Data assimilation of SMOS SSS data to create Level 4 salinity maps. A case study

Many approaches can be used to reduce the amount of noise present in a given set of data (observed or retrieved). In the SMOS processing chain, weighted averages are used to reduce the noise present in the sea surface salinity (SSS) data retrieved from brightness temperature measurements. This is the rationale of the existence of the higher production levels (Levels 3 and 4) of sea surface salinity and soil moisture.

Differences between SMOS level 3 (right) or FREE-run(left) and Argo data (2011). All 2011 match ups are shown in these plots.

Figure 1: Differences between SMOS level 3 (right) or FREE-run(left) and Argo data (2011). All 2011 match ups are shown in these plots.

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Are ECMWF winds a reliable auxiliary data source for SMOS salinity retrievals over rainy regions?

The European Centre for Medium-range Weather Forecasts (ECMWF) 10-m equivalent neutral wind data are used as auxiliary information in the SMOS operational Level 2 processing to improve the Sea Surface Salinity (SSS) retrievals. Errors in the auxiliary parameters (i.e., SSS climatology, Reynolds SST, and ECMWF wind) are known to propagate onto the SSS estimation in the cost function minimization step. In particular, high wind areas (above 12 m/s), where ECMWF wind uncertainty is higher, are usually flagged in the SSS retrieval. In this post, the ECMWF wind uncertainty under rainy conditions is assessed. It is shown that ECMWF does not well resolve the rain-induced wind variability and as such the wind uncertainty substantially increases in both rainy areas and their vicinity. It is therefore concluded that ECMWF winds should not be used to retrieve SSS under such conditions.

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