(Very) soon 8 candles for SMOS!!!!!!! (7/8)

Category : Data, L2, L3, L4

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 some had ideas and looked at the data very quickly… and the number of research topics rapidly grew! I will try below to give a few examples.

Of course there were some basic uses. Considering the L Band penetration depth in dry ice it was expected to ave a very stable signal in Antarctica suitable for vicarious calibration. While G. Macelloni and colleagues at IFAC implemented a radiometer at Dome Concordia, F Cabot used the site to verify SMOS calibration and sensitivity and after used it to inter-compare with Aquarius and SMAP (using SMOS capability to reconstruct their main lobe characteristics through reconstruction). He routinely monitors the L band radiometers in orbit and with M. Brogioni follows the absolute calibration through the ground radiometer.

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Caption: Temporal evolution of all sensors over Dome C (F. Cabot)

Over Antarctica several studies were performed (also funded by ESA) and products were made (available at CATDS) on estimation of internal ice-sheet temperature, estimation of ice thickness, indicator of the origin of ice-shelves variability, surface melting occurrences. But for me the most mind boggling result was obtained right at the beginning by Giovanni who identify definite signatures over lake Vostok which is some 3.7 km below the surface, while models indicate at best a 900 m penetration depth (G. Picard and M. Leduc Leballeur). Several potential explanations have been suggested but are yet to be validated.

Freeze thaw was expected to be a potential products and colleagues at FMI used the Elbara measurements in Sodankylä to devise a Freeze thaw algorithm. It is now quasi operational.

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Caption: Example for final soil freezing date on 2014 calculated from SMOS freeze/thaw data (K Rautiainen)

More novel the idea put forward by several scientists (G. Heygster, L. Kaleschke) to estimate thin sea ice thickness with SMOS. Now an operational product is being produced in Hamburg. It relies on the complementarity between Smos (sensitive below 75 cm thickness) and CryoSat only sensitive above a meter) the synergisms enable to track sea ice thickness globally whatever the thickness in a way, but also thin sea ice monitoring is a boon for ship routing around the Arctic (optimising between distance and ice to be broken through) and is of course very relevant for sea atmosphere exchanges.

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Caption: Temporal evolution of sea ice cover over the Arctic (L. Kaleschke)

Another ice cap of great interested is that of Greenland. The L band signatures are somewhat intriguing and several scientists are investigating it. But can already mention capturing significant melt event (as depicted by Mialon and Bircher on this blog) and some preliminary explanations for the different features seen.

Over land the first issue to tackle was that of the thick layers of organic soils whose dielectric constant are quite different from that of mineral soils (even the probes, if not calibrated properly, give wrong estimates). S Bircher and colleagues tackled the issue and developed both an improved dielectric model but also an adapted soils map to make good use of it. This constitutes a major step forward for the analysis of high latitudes. It will also lead to more adequate permafrost monitoring projects.

Finally I believe we are on the verge of another dramatic improvement with the very recent work done at WSL /Gamma by M. Schwank and colleagues and at FMI (K. Rautiainen and J. Lemmetyinen) as they found a way to infer snow density from SMOS data and then they are on the verge of extracting snow water content from L band radiometry.

For the cryosphere, these achievements and notably thins sea ice an snow density / water content are I believe very significant steps forward!

Stay tuned!

For further reading:

Bircher, S., Andreasen, M., Vuollet, J., Vehvilainen, J., Rautiainen, K., Jonard, F., Weihermuller, L., Zakharova, E., Wigneron, J.P., & Kerr, Y.H. (2016). Soil moisture sensor calibration for organic soil surface layers. Geoscientific Instrumentation Methods and Data Systems, 5, 109-125

Bircher, S., & Remote Sensing Editorial, O. (2017). L-Band Relative Permittivity of Organic Soil Surface Layers-A New Dataset of Resonant Cavity Measurements and Model Evaluation (vol 8, 1024, 2016). Remote Sensing, 9

Bircher, S., Demontoux, F., Razafindratsima, S., Zakharova, E., Drusch, M., Wigneron, J.P., & Kerr, Y.H. (2016). L-Band Relative Permittivity of Organic Soil Surface LayersA New Dataset of Resonant Cavity Measurements and Model Evaluation. Remote Sensing, 8

Kaleschke, L., Tian-Kunze, X., Maass, N., Beitsch, A., Wernecke, A., Miernecki, M., Muller, G., Fock, B.H., Gierisch, A.M.U., Schlunzen, K.H., Pohlmann, T., Dobrynin, M., Hendricks, S., Asseng, J., Gerdes, R., Jochmann, P., Reimer, N., Holfort, J., Melsheimer, C., Heygster, G., Spreen, G., Gerland, S., King, J., Skou, N., Sobjaerg, S.S., Haas, C., Richter, F., & Casal, T. (2016). SMOS sea ice product: Operational application and validation in the Barents Sea marginal ice zone. Remote Sensing of Environment, 180, 264-273

Lemmetyinen, J., Schwank, M., Rautiainen, K., Kontu, A., Parkkinen, T., Matzler, C., Wiesmann, A., Wegmuller, U., Derksen, C., Toose, P., Roy, A., & Pulliainen, J. (2016). Snow density and ground permittivity retrieved from L-band radiometry: Application to experimental data. Remote Sensing of Environment, 180, 377-391

Naderpour, R., Schwank, M., Matzler, C., Lemmetyinen, J., & Steffen, K. (2017). Snow Density and Ground Permittivity Retrieved From L-Band Radiometry: A Retrieval Sensitivity Analysis. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 3148-3161

Pellarin, T., Mialon, A., Biron, R., Coulaud, C., Gibon, F., Kerr, Y., Lafaysse, M., Mercier, B., Morin, S., Redor, I., Schwank, M., & Volksch, I. (2016). Three years of L-band brightness temperature measurements in a mountainous area: Topography, vegetation and snowmelt issues. Remote Sensing of Environment, 180, 85-98

Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M., Drusch, M., & Pulliainen, J. (2016). SMOS prototype algorithm for detecting autumn soil freezing. Remote Sensing of Environment, 180, 346-360

Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., & Haas, C. (2017). A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. Cryosphere, 11, 1607-1623

Schwank, M., Matzler, C., Wiesmann, A., Wegmuller, U., Pulliainen, J., Lemmetyinen, J., Rautiainen, K., Derksen, C., Toose, P., & Drusch, M. (2015). Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: A Synthetic Analysis. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3833-3845

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

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