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)


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

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.


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.


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.


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


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.

See Africa Breathing!

Category : CATDS, Data, L3

Simon Gascoin from CESBIO just sent me this animation he made using CATDS SMOS L3 SM over Africa


SMOS MONTHLY SM Fields over Africa (click to activate)

It is fascinating to see not only the pulsating effect of ITCZ over Africa, but also its counterpart in South Africa, the Euphrates  or the rainy seasons over the Maghreb, the internal Niger delta or the Okavango and many more … Enjoy

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