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:
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.
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
From July 21 to 26, SMOS-BEC host at ICM the 17th meeting of the Ocean Observation Panel for Climate (OOPC) and the 3rd meeting of the Global Ocean Observing System (GOOS). The mission of OOPC is to develop recommendations for a sustained global observation of the oceans in relation to climate, while GOOS is a permanent global system for observations, modeling and analysis of marine and ocean variables to support operational ocean services worldwide. GOOS provides accurate descriptions of the present state of the oceans, including living resources; continuous forecasts of the future conditions of the sea as far ahead as possible, and the basis for forecasts of climate change. GOOS is made of many observation platforms including 3000 Argo floats, 1250 drifting buoys, 350 embarked systems on commercial or cruising yachts, 100 research vessels, 200 marigraphs, and more than 200 moorings in open sea.
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).
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.
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.
New reprocessed Sea Surface Salinity products at 0.25 degrees grid spacing are available online. A complete set of products (weighted averaged, optimally interpolated and fused maps) corresponding to the year 2013 has been generated. With the reprocessing of these data, BEC provides the SMOS users with a uniform set of SSS maps for most of the current operating life of SMOS (period 2010-2013).
A new authentication method has been implemented to access the SMOS data generated at BEC. Until now, all data users had the same username and password. From now on, every user will have her/his own username and password. This new implementation requires a personal re-registration of the current users. This procedure is necessary in order to properly manage the amount of users and future services.
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.
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.