Post-doctoral position Water extent mapping from data fusion of L-Band radiometry and radar

Category : CATDS, Data, L3, L4, position opening

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Post-doctoral position

Water extent mapping from data fusion of L-Band radiometry and radar

Location: Centre d’Etudes Spatiale de la Biosphère (CESBIO), Toulouse, France

Duration: 18 months start before November 2018

Description:

Interested in creating state of the art remote sensing products in hydrology for existing and future satellite mission, then join us ! In the framework of the future Surface Water and Ocean Topography (SWOT), a joint mission from French (CNES), American (NASA), Canadian (CSA), and British (UKSA) space agencies, CNES is developing a down-stream application service that will also integrate products from contemporary satellites. In 2008 the CNES had already developed a downstream centre (CATDS www.catds.fr) for the Soil Moisture Ocean Salinity (SMOS) mission which is providing today high level products for the mission. You will join the Microwave-SMOS group in the Observation Systems team at CESBIO. You will be under the supervision of Ahmad Al bitar (Ph.D.) who si in charge of high end (L4) products for CATDS and member of the Science Team of SWOT. You will contribute to existing collaborations with other institutions: LEGOS, GET, ECOLAB, Univ. Purpan.  You will be in charge of developing operationally ready products for the monitoring of inland water surfaces (wetlands, floodplains…) by extending the domain of application of existing algorithms and improving their resolution. These areas represent a great challenge and raise important scientific questions related to the water cycle, biodiversity and carbon cycle. Recent studies at CESBIO demonstrated the advantage of the use of L-Band for the monitoring of water surfaces under dense tropical forests. The final objective is to enrich the existing databases at global scale (CCI Water bodies, GSWO, GIEMS) and to make available a validation dataset for the future SWOT mission.

Actions:

> Adapting the retrieval algorithms for global application using L-Band brightness temperatures.
> Writing scientific papers, presenting results at conferences and project key-points.

Required Skills:

> Advanced knowledge of one programming language Matlab, Python using geospatial datasets.
> Proven writing and communication skills.
> Motivated, innovative and team-player.

Links:

https://www.researchgate.net/publication/317012733_Mapping_Dynamic_Water_Fraction_under_the_Tropical_Rain_Forests_of_the_Amazonian_Basin_from_SMOS_Brightness_Temperatures

Contact:

e-mail to : ahmad.albitar@cesbio.cnes.fr & santiago.penaluque@cnes.fr

Interesting stuff from Davos (Laret) (for once?!)

Category : Data, ground measurements

Davos is well known for some meetings taking place up there. But our Colleagues from WSL and Gamma Remote sensing are carrying out a very interesting experiment as detailed below by Mike and Reza!

Davos-Laret Remote Sensing Field Laboratory: Latest Results and Progress

Reza Naderpour and Mike Schwank

The Davos-Laret Remote Sensing Field Laboratory (Switzerland) was established in October 2016 as the only currently-operating Alpine test site dedicated to the development of novel retrieval approaches for the estimation of snow properties from passive/active microwave remote sensing data.

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Caption: ELBARA in Davos-Laret: before winter (the wire mesh is visible on the ground) and when snow is present

The second winter campaign started on November 18, 2017 simultaneously with the publication of the first paper based on Winter 2016/17 campaign:

Naderpour, R.; Schwank, M.; Mätzler, C. Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis. Remote Sens. 20179, 1185.

Accessible here:

With two more upcoming papers, the research conducted in the Winter 2016/17 campaign has mainly investigated the retrieval of snow liquid water content from L-band radiometry as well as disturbing impacts of snow liquid water on the ground permittivity and snow density retrievals. Additionally, several data processing improvements, including a refined RFI mitigation approach, are suggested which can be of high interest to ELBARA users. See the PDF file for more information on the 2017/18 campaign and close-up details of the campaign practicalities!

NewsLetter_2017

(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!! (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.

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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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