SMOS Soil Moisture data available in Near real Time !!

Category : L2

We would like to inform you that we have now released the SMOS soil moisture product available in near-real time (NRT) based on a neural network approach.

The netcdf product is disseminated within four hours from sensing. For the moment it is available from ESA’s SMOS data portal (https://smos-ds-02.eo.esa.int/oads/access/). From mid-April this product will also be available through EUMETCAST and GTS.

For further information on the product and access to data please see https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/smos/news/-/article/smos-soil-moisture-product-in-nrt-based-on-neural-network-is-now-available

Kind regards,
Susanne Mecklenburg

SMOS is getting closer to the mark

Category : Cal/Val, L2

SMOS is currently undergoing Cal Val Activities and in this framework at CESBIO we are busy-with colleagues everywhere to compare SMOS data with ground data.

In particular, with colleagues in the US we have looked at the SCAN data and several very interesting results are emerging. Below a comparison done by Ahmad AlBitar at CESBIO – CATDS between SMOS and SCAN network data.

SMOS ECMWF and SCAN

This figure shows a comparison between 3 sources of measurements. In the top panel Ahmad has plotted the absolute soil moisture from ECMWF (blue) Scan (site 2001 in green) and SMOS (red). SMOS still underestimates the others but we can see that the temporal behaviour is very well captured. This is very promising for some applications for SMOS.

The middle panel shows the retrieved vegetation opacity. It starts in May only as it was then that the proper algorithm was implemented at ESAC.

Note also, in the lower panel where the temperatures plotted the evolution from the first days to today. This clearly emphasizes the improvements carried out on the calibration etc (mainly at level 1 thus). Of course the temperature retrieval is not very often carried out (see ATBD) so only a few points are present but all those for July august are completely embedded in the ECMWF and SCAN curves.

Just to show the SMOS ability to capture dry downs, Ahmad has stretched the scales of the three data sets beween 0 and 1 (the so called wetness index!) as shown below. With this trick, we see how well SMOS captures the events.

Absolute (top) and strectched (bottom) soil moisture / wetness indices

Absolute (top) and strectched (bottom) soil moisture / wetness indices

Of course we can still see the improvements a time goes since level 1 and level 2 teams did work a lot during commissioning phase and after to improve the calibration and algorithms. So the real part to be studied is from end of May to today.

I must also say that this example was obtained after selecting SCAN SNOTEL sites (Northern US as we are also looking at freeze thaw/ snow) which were « nice » (i.e., rather flat and homogeneous, minimum forest cover, low RFI), and – of course – I selected one of the best of Ahmad’s plots!

Meilleure calibration = meilleure SM ?

Category : Commissioning phase, L2

Avec la première GMatrix générée en orbite, le L1PP commence à fournir des données mieux focalisées et calibrées. J’en ai profité pour alimenter le L2 avec un de ces produits, avec les résultats suivants:

SM retrieved 19/11 14:13SM initial 19/11 14:13

A gauche l’humidité inversée, à droite l’humidité fournie par ECMWF. On constate plusieurs choses:
L’humidité est clairement sensible aux RFI (en Afghanistan) qui polluent jusqu’en Arabie. Les zones humides autour de la Caspienne et la cote Est de Madagascar sont pas mal rendues.

En ce qui concerne les biais de loc, l’utilisation de ces étalonnages à fait diminuer le biais en dessous de 7km, ce qui le ramène dans un domaine plus conforme avec ce que j’attendais.

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