(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.

domeClegend

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.

ft

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.

anim

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

SMOS helps discriminating water sources in cold seas

Category : L2, Ocean

Discriminating water sources from space in the Arctic Ocean: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data

A recent paper by Matsuoka et al. (2016), using SMOS ESA L2 SSS, found nice correlations between the interannual variability of SMOS SSS and ocean color CDOM (see fig 1 and 2 below) in the Mackenzie river mouth. Using this region as a case study, they derive an algorithm using this two sets of data for getting reasonable fractions of river water. As stated by the authors ‘Application of this algorithm may lead to the discrimination of water sources in the surface layer of the Arctic Ocean in various environments where seawater, ice melt water, and river water are intermingled,which might be useful to improve our understanding of physical and biogeochemical processes related to each water source’.

babin-1

Fig.1 : Satellite images of CDOM absorption coefficient at 443 nm [aCDOM(443),m−1] using MODIS ocean color data in July to August 2010 to 2012 (from Matsuoka et al. (2016))

babin-2

Fig. 2 : Same as Fig. 1 for SMOS SSS (from Matsuoka et al. (2016)).

Atsushi Matsuoka, Marcel Babin, Emmanuel C. Devred, A new algorithm for discriminating water sources from space: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data, Remote Sensing of Environment, Volume 184, October 2016, Pages 124-138, ISSN 0034-4257, http://dx.doi.org/10.1016/j.rse.2016.05.006. (//www.sciencedirect.com/science/article/pii/S0034425716301997)

SMOS confirms that Winter is NOT coming

Category : Non classé

SMOS Monitors a gigantic early melt in Greenland in quasi real time

As reported here (credit Ruth Mottram, DMI) and here, just a few days ago on April 11 and 12 more than 10% of Greenland surface melted. This is very significant at this time of year (see cutting below from here).

greenland melt

From the late seventies with SMMR, passive microwave sensors have already shown their ability to monitor ice caps.

SMOS, thanks to its L-band capacity is sensitive to layering in the snow/ice pack but liquid water on the surface will also change drastically brightness temperatures  and this is exactly what happened.

SMOS sees thus the brightness temperatures over Western Greenland decrease due to heavy surface melting.

The videos below show an animation of SMOS measurements over Greenland.

The the South Eastern part of Greenland, along the coast line, the brightness temperatures suddenly drop from relatively high values (> 240 K. orange colour) to values lower than 220 K. (light blue, green yellow).

HFigure : Animation showing SMOS brightness temperatures (L3 TB from CATDS) in H polarization, for an incidence angles ranging between 40 to 45°.

By Arnaud Mialon


Brightness temperature time series can be created interactively through the Live Access Server (LAS)

Category : Cal/Val, Data, L1

The Team in Hamburg (Institut für Meereskunde at KlimaCampus / Universität Hamburg) is working on thin sea ice detection and monitoring (see our previous blog posts on the subject). Dr. Lars Kaleschke and his team has now produced a Live access Server enabling to create interactively time series as shown on the examples below. Also check: http://icdc.zmaw.de/l3b_smos_tb.html?&L=1 and http://www.arctic.io/2013/2/smos-brightness-temperature

SMOS_TB_v505_I_north_20121201_01

Figure 1: Mean daily brightness temperature (TB) intensity in the Northern hemisphere over open water, sea ice, and some of the adjacent land masses for December 1, 2012.

SMOS_TB_v505_I_uncertainty_north_20121201_01

Figure 2: Corresponding standard deviation (uncertainty) calculated as the variation of TB data pairs of the respective grid cell.

TB_timeseries
Figure 3: Example brightness temperature time series of the sea ice north of Greenland as generated with the Integrated Climate Data Center (ICDC) Live Access Server (LAS).

Link to ICDC: http://icdc.zmaw.de/

download wordpress themes