SMOS retrieves salinity closer to the coast line

Category : L2, L3, Ocean

From J Boutin, and colleagues from LOCEAN

Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the SMOS mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts.

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With the new systematic error mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico (see Figure 1 below). The standard deviation of the differences between SMOS and SMAP weekly SSS is less than 0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean (see Figure 2 below). Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS remains more impacted by RFI than SMAP. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico.

The new SMOS SSS products are available at CATDS (’CEC LOCEAN debias v2′ produced by LOCEAN/ACRI expertise center and ‘CPDC L3Q’ produced by the near real time CATDS chain). The paper is available here (the link is freely active during 2 months).

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Figure 1: SMOS SSS corrected according to (a,d) Kolodziejczyk et al. (2016) methodology, (b, e) the method described in this paper (CEC); (c, f) SMAP SSS, in two areas : (a, b, c) : Bay of Bengal - August 21st 2015; (d, e, f) : Gulf of Mexico – August18th 2015.SMOS and SMAP SSS is averaged over a SMOS repetitive orbit sub-cycle (18 days) and two SMAP repetitive orbit cycles (16 days) respectively. Striking fresh SSS features in better agreement with SMOS (new version) and SMAP are indicated with white arrows.

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Figure 2: Statistics of ship comparisons (May 2010-August 2016) binned as a function of the distance from the nearest coast: top) mean difference; bottom) standard deviation of the differences; the black line indicates the standard deviation of ship SSS in each class. Ship and SMOS SSS are integrated over 100 km. Orange: monthly SMOS L3P SSS (without error mitigation) ; pink : monthly SMOS L3Q (with error mitigation; near real time processing); light blue : 18-day SMOS CEC (with error mitigation; LOCEAN/ACRI expertise center processing); green : ISAS (Argo optimal interpolation).

SMOS: A new tool to monitor the carbon budget of vegetation: first application to the African continent

Category : L3

How do the vegetation carbon stocks change at the continental scale? What are the drivers of these changes? These are central questions for the sciences of Climate and for the application of international agreements on climate. A study coordinated by the University of Copenhagen1 has developed a new approach to investigate this issue. In collaboration with scientific teams2 from CEA, CNES and CNRS, INRA has coordinated the development of the new data set derived from SMOS microwave observations which is used to quantify vegetation carbon stocks. The study demonstrates that over the African continent and during the 2010-2016 period, the net carbon balance is negative (corresponding to a decrease of the quantity of carbon stored in the vegetation biomass) and that most of the carbon losses occurred in dryland savannahs. These results were published on 9th April 2018 in the journal Nature Ecology and Evolution.

Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands, Martin Brandt, et al., Nature Ecology & Evolution, Apr 9, 2018, doi:10.1038/s41559-018-0530-6

Changes in aboveground vegetation carbon stocks in sub-Saharan Africa over 2010–2016. Regions with significant negative (carbon source) or positive (carbon sink) carbon changes are shown, respectively, in red or green.

© M. Brandt – Université de Copenhague

Scientists from INRA and their colleagues have produced a new data set of the vegetation index referred to as L-VOD (L-band vegetation optical depth), retrieved from space-borne observations of the SMOS satellite over 2010-2016. The data have been used to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa stocks over the time period. The L-VOD index computed from SMOS observations allows sensing the whole canopy layer, while remote sensing observations made so far (including VOD indexes from high frequency microwave observations) were limited to sensing the top of the canopy, especially over relatively dense forests. In this study, the L-VOD index brings a temporal dimension to global, but static, maps of the above ground biomass.

An new tool to carry out an innovative monitoring of the dynamics of aboveground vegetation carbon stocks

This new method allows to monitor the seasonal dynamics of vegetation carbon losses and gains and also to link them with the impact of climate. The results demonstrate the key interest of SMOS L-VOD as a complementary data source for the quantification and monitoring of carbon stocks for national reports and large scale efforts in the framework of international initiatives such as the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC). The SMOS L-VOD index is particularly relevant to study relatively dense vegetation ecosystems where the signal measured from other remotely-sensed system saturates. It is also very relevant for semi-arid regions with little inventory data.

First applications of the new L-VOD tool to monitor carbon changes in sub-Saharan Africa

The study demonstrates there is an overall negative net carbon budget for sub-Saharan Africa (−0.10 PgC yr−1) over 2010-2016 and that the majority of the net losses occurred in drylands. In the latter regions, the gross loss per year represents approximately

5% of the dryland total carbon stocks in Africa. Overall, most of the detected decreases in carbon stocks in drylands were related to abnormally low soil moisture and rainfall conditions. The analyses showed there is a high inter-annual variability in the vegetation carbon stocks with gains during very wet years in (2011) and losses during in 2015 and early 2016, following a severe El Niño episode.

This study questions the general understandin

g that drylands may serve as carbon sinks on the long term. Indeed, the authors have found that dry years have partly reversed this trend and that drylands (especially in southern Africa) turned from being a carbon sink into a source over 2010-2016, demonstrating that climate controls short-term variations in carbon stocks at large scales.

So, in the long term we need to reassess whether woody vegetation in African savannahs will continue to be a carbon sink.


1 Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

2 List of French teams involved:

Unité « Interaction Sol Plante Atmosphère » (Inra, Bordeaux Sciences Agro), centre Inra Nouvelle-Aquitaine

Laboratoire Evolution et Diversité Biologique (CNRS – IRD – UT3 Paul Sabatier – ENFA)

Laboratoire des sciences du climat et de l’environnement (CEA-CNRS-UVSQ)

Centre d’études spatiales de la biosphère (Cnes-CNRS-IRD-UT3 Paul Sabatier)

NEW PRODUCTS on CATDS

Category : CATDS, L2, L3

I am very pleased to announce that the new SMOS-IC soil moisture product is now available as a science product on the CATDS:

The SMOS INRA-CESBIO (SMOS-IC) algorithm was designed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d’Etudes Spatiales de la BIOsphère) to perform global retrievals of SM and L-VOD using some simplifications with respect to the Level 2 ESA algorithm. The SMOS-IC algorithm and dataset is described in Fernandez-Moran et al. (2017). SMOS -IC was designed on the same basis as the level 2 SM algorithm, i.e., L-MEB (Wigneron et al, 2007). However, one of the main goals of the SMOS-IC product is to be as independent as possible from auxiliary data so as to be more robust and less impacted by potential uncertainties in the afore mentioned auxiliary data sets. It also differs from the SMOS Level 2 product in the treatment of retrievals over regions with a heterogeneous land cover (partially forested areas). Specifically, SMOS-IC does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous.

The current version is 105 and it is provided in the 25km EASEv2 grid, as netcdf format. SMOS IC is a scientific product delivered by the CATDS, i.e. meaning it is not updated on a daily basis as an operational product for the time being.

We re looking forward to receiving your feed back as we intend to make it an operational product soon.

We will soon deploy the companion  SMOS-IC VOD (vegetation Optical Depth) product as well as a corresponding Level 3 for both SM and VOD obtained with SMOS-IC

Also Note that very soon we will deploy another new product (yes), i.e., SMOS brightness temperature in polar projection

(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

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