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.

Workshop announcement: « measuring high-wind speeds over the ocean »

Category : L2, L3, Ocean

On behalf of the organizing committee, I would like to draw your attention on the SMOS+STORM ESA project workshop

International workshop on measuring high-wind speeds over the ocean

15-17 November 2016, Met Office, Exeter


A three-day workshop on the science and applications of ocean surface winds, with a focus on measuring high or extreme wind speeds. The workshop will be held at the Met Office headquarters in the cathedral city of Exeter.

Topics will include:

  • Satellite measurement techniques including new capabilities from SMOS, SMAP and AMSR2 sensors
  • Ground and airborne measurements
  • Core/operational applications and challenges e.g. hurricane/extreme forecasting, storm surges, waves
  • Air-sea applications and challenges e.g. seasonal/climate, upper ocean dynamics, air-sea interaction, biogeochemistry
  • Numerical Weather Prediction (NWP) applications
  • Numerical Ocean Prediction (NOP) applications

Organisers: Pete Francis (Met Office), James Cotton (Met Office), Nicolas Reul (IFREMER), Craig Donlon (ESA)

Further information on how to register for the workshop and submit an abstract and preliminary program is available from the link below:

Please find also attached the workshop flyer.


SMOS, SMAP, AMSR-2 .. and Colleagues!

Category : Data, Satellite

Michel Dejus from CNES tells us that the three hurricanes monitored by SMOS, AMSR-2 and SMAP (see post  on 05/10/2015) were also detected and seen by Megha-Tropiques though the Channel 5 of its SAPHIR instrument (level 1A2) …. as seen on the orbit below!

Cyclones pacifique aout 2015

Where the three hurricanes are quite visible as well as visible on this zoom below/ Megha-Tropiques is a joint mission between CNES and ISRO.

Saphir 3 Cyclones

SMOS, AMSR-2 and SMAp join forces to track Hurricanes!

Category : L3, L4, Ocean

N Reul and colleagues looked at the signatures of 3 co-evolving 2015 major Hurricanes from 22 Aug to 9 September 2015 in the East and Central tropical Pacific  using SMOS, SMAP and AMSR-2 observations (beyond others)

from Nicolas REUL (IFREMER) from teh SMOS + Storm evolution project (ESA)


Legend: true-colour composite from the MODIS instrument on NASA’s Terra satellite and SMOS surface wind speed amplitude of hurricanes Kilo (left), Ignacio (centre) and Jimena (right) on 29 August 2015. All three were category-4 hurricanes and spanned the central and eastern Pacific basins. The bright bands in the images are sunglint where solar radiation from the Sun has reflected from Earth back to the satellite sensor.

This year sees a strong El Niño event which causes much higher temperatures than normal in the upper layers of the Tropical East Pacific. The available surplus heat favors an increased occurrence of Tropical Cyclones and storms. According to the National Oceanic and Atmospheric Administration (NOAA), this season has been ranked as the fifth most active hurricane season within the Eastern Pacific since 1971.  In a normal season, the average number of Major Hurricanes (Category 3 and plus in the Saffir-Simpson scale) is expected to be 3 whereas this year already 8 Major Hurricanes have occurred.  At the end of August, three Category 4 hurricanes (named Kilo, Ignacio and Jimena) developed in parallel in the vicinity of Hawaii which MODIS and SMOS sensors were well able to capture.

Several types of satellites observations are available to characterize surface winds over the ocean such as active scatterometer radars and passive low-frequency radiometers operating in C and L-band. The advantage of using such radiometers, being an all-weather tool, lies in detecting extreme wind speed (above 33 m/s, i.e., hurricane force) which scatterometers are not able to detect. Three such radiometers, namely, the ESA/SMOS and the recently launched NASA/SMAP L-band and the JAXA/AMSR-2 C-band, are now available to provide such observations.

SMOS surface Tbs and wind speed products along SMOS swaths were determined using the algorithm of  Reul et al., 2012 and updated in Reul et al., 2015. Image Reconstruction based on JRECON (J. Tenerelli, 2011). SMOS Level 1b Tbs are retrieved at antenna level and are further corrected for extra-terrestrial sources contributions, smooth sea surface emission, and atmospheric path effetcs to estimate a storm-surface induced Tb residual. A Quadratic Wind speed GMF is applied to the First Stokes parameter residual  to obtain U, the surface wind speed. Current validation reveals an rms of ~5 m/s up to 50 m/s with respect SFMR flight data or H*Wind analysis at the same spatial resolution than SMOS.

AMSR-2 surface winds are obtained using the  Algorithm developed by Zabolotskikh et al. (2013, 2015a,b) which involves the combined used of highest frequency Chanels (for rain retrieval) and atmosphere corrected 6.925 and 7.3 GHz channels for surface wind inversion.

SMAP: Level 1B data from NSIDC are used, surface first-stokes residual contributions are evaluated  (corrections for atmospheric, cosmic background reflections and smooth ocean surface emission), data with significant galactic reflections are not used  (asc fore beam data are not considered). GMF of Reul et al. 2015 developed for SMOS is applied to retrieved SWS. A systematic offset of -5m/s was added to the retrievals for consistency with ECMWF & NCEP winds for winds <20 m/s)

Thanks to the density of observations available from these 3 missions, the evolution of surface wind speed under tropical storms can now be monitored with an unprecedented spatial and temporal resolution. The example of the 3 co-evolving hurricanes in the East Pacific demonstrate this capability with an ensemble of 121 satellite swath intercepts of the 3 storms obtained by combining the data from the 3 sensors over a period of about 15 days (22 Aug to 7 Sep). The below animation shows the time-series mosaic of surface wind speed measurements under Hurricanes Kilo, Ignacio and Jimena. Data from the three satellite microwave radiometer missions: ESA’s L-band SMOS, NASA’s L-band SMAP and Japan’s C-band AMSR-2 are combined to reveal the track of each hurricane and maximum wind speed measured by each sensor at the ocean surface during the period.

see –> winds from SMOS, AMSR-2 and SMAP


Caption: Contours of the domains showing the maxima of surface winds obtained from the combined multiple observations of SMOS, SMAP and AMSR-2 sensors from 22 Aug to 9 Sep 2015 showing the high wind trails over Hurricanes Kilo and Loke (left), Ignacio (center), Jimena (right).

Comparison of the Maxima of winds from the 3 satellites with Best-Track Maximum winds is shown here below.


Caption: Time series of the Maximum Sustained wind speed as provided by TC’s center (NOAA/HRD and NHC) together with the Maximum wind estimated from SMOS, SMAP and AMSR-2 fro Jimena (top), Ignacio (middle) and Kilo (bottom).

Limitations in the highest wind domains is not due to Tb saturation but to the 30-40 km spatial resolution smoothing effect (Highest-wind generally occur in a narrow region of radius < ~100km around the eyes with very high Wind speed radial gradients).

Key characteristics of hurricanes, such as the radii of wind speeds above a certain threshold (i.e. 34, 50, 64 and 84 knots), can now be captured more often in far more detail. This will greatly improve the representation of initial conditions of Tropical Cyclones in Numerical Weather forecasting systems and hence their prediction.

Interestingly, the higher resolution in space and time of the surface forcing that is gained by the merging of the data from these 3 low MW frequency sensors  will certainly help inferring better understanding of the ocean-atmosphere interactions in TCs:


Caption: Sea Surface Temperature anomalies (in degrees Celcius) reveal cold-water wakes trailing behind hurricanes Kilo, Ignacio, and Jimena highlighting the power of hurricane winds to violently stir the upper ocean and bring cooler waters at depth to the ocean surface.

See as well a recent ESA web-story on the same topic


Reul, N., J. Tenerelli, B. Chapron, D. Vandemark, Y. Quilfen, and Y. Kerr (2012), SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes, J. Geophys. Res., 117, C02006, doi:10.1029/2011JC007474.

Reul, N. et al, A revised L-band radio-brightness sensitivity to extreme winds under Tropical Cyclones: the 5
years SMOS-storm database, submitted to Remote Sensing of Environment, 2015.

E. V. Zabolotskikh, L. M. Mitnik, and B. Chapron, “New approach for severe marine weather study using satellite passive microwave sensing,” Geophys. Res. Lett., vol. 40, no. 13, pp. 3347–3350, 2013.

E. Zabolotskikh, L. Mitnik, N. Reul, and B. Chapron, “New Possibilities for Geophysical Parameter Retrievals Opened by GCOM-W1 AMSR2,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. PP, no. 99, pp. 1–14, 2015a.

E. Zabolotskikh, N. Reul and B. Chapron,  “Geophysical model function for the AMSR2 C- band wind excess emissivity at at high winds“, submitted to GRSL, 2015b.

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