Soon 8 candles for SMOS!!!!!! (6/8)

Category : CATDS, L2, L3, L4, Model

After the illustrations of some striking results over oceans, we can only marvel, especially as many other aspects were not covered.  Eight years ago we did not have any of such applications and science return. Those span from rainfall estimates over oceans to wind speed retrievals for strong winds (tropical storms, hurricanes and the like) where wind scatterometers do saturate for lower wind speeds. SMOS, Aquarius and now SMAP do show that L band measurements bring forward many new science obviously but also many very practical and societal applications which are not fulfilled without them.


Caption: IRMA (2017 09 07) as seen from SMOS in terms of surface wind speed (N. Reul)

This also applies for land of course where new applications blossomed at an unprecedented rate.

It exemplifies, to me at least, how real measurements can never be replaced by proxies. The first radar for EO flew in 1977 (yes 40 years ago!), the scatterometers with Envisat have been available since 1991 but we have yet to see a real soil moisture map from these. Intrinsically active systems are more sensitive to structure that to content and radar soil moisture are at best validated only over small areas where all is known, and similarly to scatterometers, rely on change detection (yes I know I am partial but I can claim that I started fiddling with radars 40 years ago and was one of the pro SCAT over land (convincing ESA to make the sigma nought triplets available over land which was not originally planned incidentally), but to realise soon that it was no game for absolute retrievals). Which means that they have to be scaled and that the validity at point (xi,yi) and no relationship with the validity at point (xj, yj) etc … but this is another story…To make a long story short a nicely coloured map has never make an accurate map.

With L band radiometry no such issues and if properly done, you have access to the soil moisture per se. As a direct consequence, and in opposition to active systems, a few months only after the release of the data the first applications emerged. We saw the first use in food security (W Crow , USDA), the first drought indices really related to what was happening (A Al Bitar detecting the drought in California in 2011 when the official drought index was to detect it only a couple of years later) or monitoring the Mississippi  floods and levees destruction in 2011, the making of a flood risk forecasting tool demonstrator, the Spanish BEC fire risk analysis tool, etc… etc.. etc…

There isn’t enough room in a blog to document all this so I am giving only three samples.

1) high resolution soil moisture map

One of the main limitations of passive microwave is the spatial resolution. Olivier Merlin and his team developed an approach which -in many cases enables to monitor soil moisture with a 1 km resolution as shown in the example below.


Caption: 1 km soil moisture map from SMOS/ MODIS over Morocco (J. Malbeteau)

It can be successfully applied at 100 m in some cases (irrigation optimisation) as shown Catalonia (MJ Escorihuela). Other approaches rely on the use of active systems as originally planned for SMAP (N. Das) and done with SMOS (S. Tomer) or SMAP with Sentinel 1. Ideally the two approaches should be merged to my feeling.

Uses for such derived high resolution products are obvious, for irrigation and hydrology as already mentioned, but also for pest control (Locusts in Africa) or epidemiology (dengue, zika and malaria to name but a few). Moreover it can be used to derive high resolution root zone soil moisture and other passive L band products.

2) Rainfall estimates over land

It is known that rainfall mission (TRMM to GPM) are very useful tool for estimating rainfall distribution over land. It is also well known that estimating rainfall with one instantaneous measurement every so often is somewhat difficult. Sometimes and in some areas/context, the cumulated errors amount to several folds. The idea is thus to assimilate soil moisture estimates so as to « correct » the GPM rainfall estimates. Pellarin, Brocca and Crow and others demonstrated the efficiency of this approach.


Caption: Evolution of rainfall estimates after assimilating SMOS data (Pellarin, Brocca, Crow et al.)

3) Yield estimates

Soil moisture is a driven of crop yield in many areas. First shown by B. Hornebuckle with SMOS, Gibon and Pellarin went one step further by identifying which soil moisture (30 cm deep) and which period (grain filling and to a lesser extent reproductive) of vegetation growth where the drivers for millet in Western Africa. They then compared their local estimates with FAO global maps and found excellent correlation. It is interesting to see that departures are linked to local events


Caption: Soil moisture anomalies during two key stages and FAO Millet yield anomalies (F. Gibon)

Examples like this can be multiplied, I just picked some low hanging fruit. One can say that such applications an science results could be expected  and were delivered in record time. This blog is probably already way too long and I did not cover very interesting and promising results on evapotranspiration for instance, or hydrology, not to mention cryosphere … I keep the latter for tomorrow!

Stay tuned !

Further reading:

Brocca, L., Pellarin, T., Crow, W.T., Ciabatta, L., Massari, C., Ryu, D., Su, C.H., Rudiger, C., & Kerr, Y. (2016). Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia. Journal of Geophysical Research-Atmospheres, 121, 12062-12079

Molero, B., Merlin, O., Malbeteau, Y., Al Bitar, A., Cabot, F., Stefan, V., Kerr, Y., Bacon, S., Cosh, M.H., Bindlish, R., & Jackson, T.J. (2016). SMOS disaggregated soil moisture product at 1 km resolution: Processor overview and first validation results. Remote Sensing of Environment, 180, 361-376

Reul, N., Chapron, B., Zabolotskikh, E., Donlon, C., Quilfen, Y., Guimbard, S., & Piolle, J.F. (2016). A revised L-band radio-brightness sensitivity to extreme winds under tropical cyclones: The 5 year SMOS-Storm database. Remote Sensing of Environment, 180, 274-291

Roman-Cascon, C., Pellarin, T., Gibon, F., Brocca, L., Cosme, E., Crow, W., Fernandez-Prieto, D., Kerr, Y.H., & Massari, C. (2017). Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX. Remote Sensing of Environment, 200, 295-310.

Towards a Flood risk alert system with SMOS?

Category : CATDS, L3, L4

SMOS gives almost real time information on soil moisture. At CESBIO / CATDS we had the idea to investigate with Capgemini how such a piece of crucial information could be used to anticipate flood risks. Using the Capgemini « Rodger » platform, we merged SMOS historical records of Soil Moisture, SMOS actual surface soil moisture information and rainfall forecasts to build a flood risk indicator.

Even though our approach is still in infancy and there are still a number of open issues to sort out (not mentioning when the extreme rainfall forecast are inadequate) we score  several successes during the past months and most notably during the recent floods in Morocco which occurred a few days ago.


flood risks as obtained on November 22nd, 2014


Same for November 25, 2014

So some work is still to be done but we are getting there!

Stay tuned!

Ahmad, Audrey, Julien, Sat and Yann

SMOS monitors Phailin in almost real time

Category : CATDS, L3, Non classé

As you might be aware a tropical super typhoon (PHAILIN) has been hitting India earlier this month.

Almost immediately, Nicolas Reul from IFREMER and Joe Tenerelli from CLS detected the tracks on the ocean as SMOS can very effectively see the wind speed underneath hurricanes and SMOS spatial and temporal resolution very adequate to monitor such events (see previous post on the topic by Nicolas and Joe and « see » the wind speed (sea state) trough rain and clouds).

To our surprise during a short time span (October 10 to 15) three such typhoons where « seen » by SMOS namely  and from East to West WIPHA NARI and PHAILINTEST_image_webstory_3storms_v2(2)Shortly after the typhoon Phailin did hit India s reported here by the Figaro paper


Here again SMOS was able to monitor it and Philippe Richaume from CESBIO did track the Typhoon as it traveled through the continent


Click to start animation

The animation shows how the typhoon impacts land before it disappears and this in spite of the monsoon period (see the Bangal Delta area for instance with water logged areas) The orbits ‘ascending and descending are added as time passes and are materialized by the red dotted lines. The SMOS retrieval algorithm find sometimes that there is too much water and changes the classification from land to wetland giving different retrievals.

The Typhoon position and trajectory are indicated for clarity (source NOAA Pearl Harbor Hurricane center). Note that the expected trajectory forecast stops when the typhoon hits land; It appears that it went more NE -ENE rather than NNW

We will provide you with updated information very soon


Cyclone Oswald over Australia monitored by SMOS

Category : CATDS, L3

In January 2013, ex-Tropical Cyclone Oswald passed trough parts of Queensland and New South Wales, Australia, causing widespread turmoil including storms, at least six tornadoes over south-eastern Queensland, and severe flooding in many areas within 200km of the coast. Coastal regions of Queensland were the most impacted with Mundubbera, Eidsvold, Gayndah and Bundaberg in the Wide Bay-Burnett hit severely.

For more information about this event:

The Australian Bureau of Meteorology recorded the amount of precipitations (as shown in the following figure), which is the cumulative precipitation between the 23rd and the 29th of January.

Rainfall over Australia at the end of January

At the CATDS, the 10-day product covering the end of January shows a large difference in soil moisture during this period. In the figure below, the left plot shows the minimum value of soil moisture recorded between the 21st and the 31st of January, and the right plot shows the maximum value of soil moisture over the same period.

Minimum and maximum values of Soil Moisture in Australie at the end of January 2013

The Weather Bureau said that the flooding could have been worse if the region wasn’t so dry before the rain started. The CATDS saw both conditions: a dry spell in the middle of January, and a significant increase in soil moisture after the cyclone.

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