Breaking news --> SMOS new LEVEL 2 SM Version in ready! Dear All The long awaited SMOS V650 is now ready for release and thus for you to use! We (ESA and ESLs) have prepared it  tested it, run the reprocessing from beginning to now, and the operational processor is no ready to produce it giving you access to the whole data set! The main features of the new versions are described in the release note made available...

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Training at IISc, Bangalore on the use of Microwave... Are you in Bangalore next week ? and interested on learning about the use of microwave data (radiometers, SARs and Altimeters) then contact Prof. Muddu Sekhar to join us on this training. and the associated program:

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8 candles for SMOS!!!!!!!! (8/8) Happy Birthday SMOS ! (As you have guessed the eight "!" correspond to 8 candles .. upside down as SMOS is looking downward). Yes SMOS has been in space for eight years now and, for a satellite, this is starting to be a somewhat venerable. So....What is next? But first a quick appraisal: We have tried to depict in the previous blogs some of the achievements only...

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(Very) soon 8 candles for SMOS!!!!!!! (7/8) After a look back at oceans, soil moisture and their applications let's have a look at colder areas.... Actually during the SMOS early years we tried to get a cryosphere group  but with very limited success to say the least. Most of them were heavily involved with other missions with little time to spend on an L band radiometer of unfathomed relevance to their science. But...

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

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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.

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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.

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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.

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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

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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.

Soon 8 candles for SMOS!!!!! (5/8)

Category : CATDS, L2, L3, Model, Ocean

Another post from Jacqueline…and Jérôme

Water cycle in the Bay of Bengal

J. Vialard , S. Marchand et al. (LOCEAN)

The Bay of Bengal receives large amounts of freshwater from the Ganges-Brahmaputra river and monsoonal rainfall. The associated very low surface salinities induce a very stable stratification that inhibits vertical mixing of heat and nutrients. This has strong consequences for the climatological rainfall, intensification of tropical cyclones and ocean productivity in this region.

Available climatologies based on in situ data (e.g. World Ocean Atlas, top row) do not resolve the very strong horizontal gradients in this region. SMOS data (middle row) reveal that the narrow, coastal-trapped East-Indian Coastal Current transport the freshwater plume of Ganges-Brahmaputra along the Indian coast from October to December, resulting in large horizontal gradients (typically ~5 pss between coastal and offshore waters). The 8 years-long time series reveals a strong inter-annual variability of the freshwater plume southward extent, which can be related to Indian Ocean climate variability.


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Caption: World ocean atlas (derived from in situ data, top row) and SMOS (middle row) (SSS climatology (altimeter-derived surface current climatology are overlaid on both panels). (Bottom row) Latitude-time section of SMOS SSS along the east coast of India. The southward extent of the freshwater plume varies depending on Indian Ocean climate variability associated with the Indian Ocean Dipole (Akhil et al. in prep.). (SMOS CATDS CPDC L3Q SSS)

To know more about associated work:

Akhil, V.P., F. Durand, M. Lengaigne, J. Vialard, M.G. Keerthi, V.V. Gopalakrishna, C. Deltel, F. Papa and C. de Boyer Montégut, 2014: A modeling study of the processes of surface salinity seasonal cycle in the Bay of Bengal, J. Geophys. Res. Oceans, 119, doi:10.1002/2013JC009632.

Akhil, V. P., M. Lengaigne, J. Vialard, F. Durand, M. G. Keerthi, A. V. S. Chaitanya, F. Papa, V. V. Gopalakrishna, and C. de Boyer Montégut, 2016a: A modeling study of processes controlling the Bay of Bengal sea surface salinity interannual variability, J. Geophys. Res. Oceans, 121, 8471–8495, doi:10.1002/2016JC011662.

Akhil, V.P., M. Lengaigne, F. Durand, J. Vialard, V.V. Gopalakrishna, C. de Boyer Montégut and J. Boutin, 2016b: Validation of SMOS and Aquarius remotely-sensed surface salinity in the Bay of Bengal, IJRS, 37,  doi: 10.1080/01431161.2016.1145362

Boutin, J., J.L. Vergely, S. Marchand, F. D’Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin (2017), Revised mitigation of systematic errors in SMOS sea surface salinity: a Bayesian approach, Remote Sensing of Environment, in revision.

Chaittanya, A.V.S., M. Lengaigne, J. Vialard, V.V. Gopalakrishna, F. Durand, Ch. Krantikumar, V. Suneel, F. Papa and M. Ravichandran, 2014: Fishermen-operated salinity measurements reveal a “river in the sea” flowing along the east coast of India, Bull. Am. Met. Soc., 95, 1897-1908.

Fournier, S., J. Vialard, M. Lengaigne, T. Lee, M.M. Gierach, A.V.S. Chaitanya, Unprecedented satellite synoptic views of the Bay of Bengal “river in the sea”, 2017: J. Geophys. Res., in (minor) revision.

Soon 8 candles for SMOS!! (4/8)

Category : CATDS, Cal/Val, Data, L2, Non classé, ground measurements

Today let’s have a look back on what was done over land… but remember: it is only a quick summary of part of the findings!!

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Of course all the emphasis at the beginning was on the soil moisture retrievals over what as called « nominal surfaces », which meant land surface with moderate vegetation cover (fallow, crop land, savannah etc..) with all the cal val efforts related to it. For this in particular, several sites were dedicated to Cal Val (VAS in Spain, UDB in Germany, AACES/COSMOS/NAFE in Australia, and later HOBE in Denmark, with also sites in France, Poland, Finland, Tibet, etc…). We also relied heavily on the USDA so called « Watershed sites » and various sparse networks. Actually it is for SMOS that ESA and NASA decided to start the International Soil moisture Network.

lewis-faugaAACES 6MELBEX-II EMIRAD Installation 004LEWIS_3IMG_9674ELBARA-Sodankyla

Various pictures SMOSREX, AACES, VAS, Crolles, Mysore, Sodankylä …

Surprisingly enough we obtained good results almost immediately. But this was only the beginning as, in parallel, both level 1 and level 2 made significant progresses, leading to always improved retrievals. Actually with such fast progresses, it has always been a bit of a frustration to see people use not up to date products, as publications looking at SMOS data tended – for obvious reasons – to be a couple of version old (but generally failed to stipulate which version they were looking at!).

The most striking features of these always improved retrievals was, to me, the fact that the range of validity tended to regularly increase. Low to medium topography did not seem to a be a limitation, we managed to make sense in case of flooded areas (see for instance Mississipi floods) and we could get information in case of dense vegetation. The Tor Vergata University for instance related very quickly the vegetation depth to tree height and performed soil moisture retrievals under rainforest. No so accurate of course, but the tendencies are well depicted.

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SMOS opacity vs tree height from ICESat for two season (Rahmoune et al)

The only trouble we had was that the vegetation optical depth was not as satisfactory as we would have expected. It remained noisy in spite of significant overall progresses. To address this problem and also to keep on improving our retrievals (parametrisations) INRA and CESBIO worked on a different approach, the so called SMOS-IC and, lo and behold, first results are rather amazing! We believe we have again struck gold. More about this in the near future!

To finish with the surface soil moisture and vegetation opacity retrievals, we were faced with the fact that the retrieval algorithm is not so fast and thus tests or re-processings are a lengthy and tedious. This was another motivation for SMOS-IC but we also wanted to go a step further and, as soon as enough data was acquired, we developed a global neural network retrieval scheme. It has since been implemented in ECMWF and delivers Soil moisture fields less than 3 hours of sensing, paving the way to many applications…. to be summarised soon: stay tuned!

Further reading

Fernandez-Moran, R.; Al-Yaari, A.; Mialon, A.; Mahmoodi, A.; Al Bitar, A.; De Lannoy, G.; Rodriguez-Fernandez, N.; Lopez-Baeza, E.; Kerr, Y.; Wigneron, J.-P. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product. Remote Sens. 2017, 9, 457.

Kerr, Y. H., et al. (2012), The SMOS Soil Moisture Retrieval Algorithm, IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384-1403, doi:10.1109/tgrs.2012.2184548.

Rahmoune, R., Ferrazzoli, P., Singh, Y., Kerr, Y., Richaume, P., Al Bitar,  A. SMOS Retrieval Results Over Forests: Comparisons With Independent Measurements. J-STARS ,2014

Rodriguez-Fernandez, N.J., Aires, F., Richaume, P., Kerr, Y.H., Prigent, C., Kolassa, J., Cabot, F., Jimenez, C., Mahmoodi, A., & Drusch, M. (2015). Soil Moisture Retrieval Using Neural Networks: Application to SMOS. Ieee Transactions on Geoscience and Remote Sensing, 53, 5991-6007

Vittucci, C., Ferrazzoli, P., Kerr, Y., Richaume, P., Guerriero, L., Rahmoune, R., & Laurin, G.V. (2016). SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates. Remote Sensing of Environment, 180, 115-127

Soon 8 candles for SMOS!! (3/8)

Category : Non classé

Jacqueline Boutin, with Audrey Hasson, sent me this contribution, as part of our series of blog stories, to illustrate each day using a new case example, how it reveals, with unprecedented details, the influence of large scale climate events, like ENSO, Indian Ocean Dipole … on the two key hydrological cycle variables. Actually,the 2010-2017 SMOS measurements time series has allowed an unprecedented and unique monitoring of Sea Surface Salinity (SSS) and Soil Moisture (SM) and Cryosphere using L-band radiometry. here is one example!

The signature of ENSO on equatorial and extra-equatorial SSS in the Pacific Ocean

A. Hasson, J. Boutin and S. Marchand (LOCEAN, Paris)

Nearly 8 years of Sea Surface Salinity retrieved from the SMOS mission has enabled the observation of inter-annual variations associated with the El Niño Southern Oscillation. SMOS was launched just in time for a great two-year long La Niña event from mid 2010 to early 2012. Followed in 2014 a small El Niño event that prepared the Pacific Ocean for a large event from mid 2015 to mid 2016.

1- The equatorial SSS variability:

In the Western Pacific Ocean, a large body of fresh waters called the fresh-pool swings along the equator together with the El Niño Southern Oscillation as observed from in situ observations. SMOS measurements enables the much more precise description of the fresh-pool displacement and its previously unknown extension. In 2011, the equatorial western Pacific fresh pool retracts all the way to the western edge of the Pacific Ocean whereas in 2015 the fresh waters extend well east of the dateline.

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Caption: 2010-2017 longitude-time plot of SMOS SSS averaged between 2ºS and 2ºN. The NINO3.4 index is displayed on top, centered on the dateline, blue during La Niña and red during El Niño https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/nino34.long.anom.data. (SMOS CATDS CPDC L3Q products)

To know more about associated work:

A. Hasson, M. Puy, J. Boutin and E. Guilyardi; Northward Propagation across the Tropical North Pacific Ocean Revealed by Surface Salinity: How El Niño Anomalies Reach Hawaii?, submitted to JGR-Oceans

Boutin, J., J.L. Vergely, S. Marchand, F. D’Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin (2017), Revised mitigation of systematic errors in SMOS sea surface salinity: a Bayesian approach, Remote Sensing of Environment, in revision.


2- The eastern Pacific fresh-pool:

Heavy rain from the Inter-tropical Pacific Convergence Zone is associated with a large area of low surface salinity in the eastern Pacific Ocean. In this very under sampled region of the ocean, SMOS gives us great insight in the ocean variability.

Since launch, an extension of the eastern Pacific freshwaters is observed as shown around 18ºN. Fresh waters are trapped east of 110ºW during the 2011 La Niña and extend to the dateline following the 2015 El Niño.

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Caption: 2010-2017 longitude-time plot of SMOS SSS averaged between 16º and 20ºN. The NINO3.4 index is displayed on top, centred on the dateline, blue during La Niña and red during El Niño https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/nino34.long.anom.data. (SMOS CATDS CPDC L3Q products)

To know more about associated work:

A. Hasson, M. Puy, J. Boutin and E. Guilyardi; Northward Propagation across the Tropical North Pacific Ocean Revealed by Surface Salinity: How El Niño Anomalies Reach Hawaii?, submitted to JGR-Oceans

Boutin, J., J.L. Vergely, S. Marchand, F. D’Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin (2017), Revised mitigation of systematic errors in SMOS sea surface salinity: a Bayesian approach, Remote Sensing of Environment, in revision.

Guimbard S., N. Reul, B. Chapron, M. Umbert and C. Maes, 2017. Seasonal and interannual variability of the eastern tropical Pacific fresh pool, J. Geophys. Res. Oceans, doi: 10.1002/2016JC012130.

Alory, G., C. Maes, T. Delcroix, N. Reul, and S. Illig, 2012. Seasonal dynamics of sea surface salinity off Panama: the far eastern Pacific fresh pool. J. Geophys. Res., 117, C04028, doi:10.1029/2011JC007802.

3- The extra-equatorial anomalies

The unprecedented spatial and temporal coverage of the SMOS mission reveals poleward pathways of equatorial SSS anomalies as shown for the 2011 La Niña (Hasson et al. 2014) and for the 2014-2015 El Niño events (Hasson et al. 2017 submitted). Anomalies created at the equator by the displacement of the western Pacific fresh-pool and off the equator are exported poleward by the Ekman drift in a complex system of tropical currents.

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Caption: 2010-2017 latitude-time plot of SMOS SSS anomalies produced by the CEC-LOCEAN averaged between 150º and 170ºW. The NINO3.4 index is displayed on top, centered on the equator, blue during La Niña and red during El Niño https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/nino34.long.anom.data. (SMOS CATDS CPDC L3Q products)

To know more about associated work:

A. Hasson, M. Puy, J. Boutin and E. Guilyardi; Northward Propagation across the Tropical North Pacific Ocean Revealed by Surface Salinity: How El Niño Anomalies Reach Hawaii?, submitted to JGR-Oceans

A. Hasson, T. Delcroix, J. Boutin, R. Dussin, and J. Ballabrera-Poy (2014); Analyzing the 2010–2011 La Niña signature in the tropical Pacific sea surface salinity using in situ data, SMOS observations, and a numerical simulation, Journal of Geophysical Research: Oceans, 119(6), 3855-3867, doi:10.1002/2013JC009388.

Boutin, J., J.L. Vergely, S. Marchand, F. D’Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin (2017), Revised mitigation of systematic errors in SMOS sea surface salinity: a Bayesian approach, Remote Sensing of Environment, in revision.

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