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.

anim-1KM_Morocco

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.

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_AFRICA_SM

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

SMOS Soil Moisture data available in Near real Time !!

Category : L2

We would like to inform you that we have now released the SMOS soil moisture product available in near-real time (NRT) based on a neural network approach.

The netcdf product is disseminated within four hours from sensing. For the moment it is available from ESA’s SMOS data portal (https://smos-ds-02.eo.esa.int/oads/access/). From mid-April this product will also be available through EUMETCAST and GTS.

For further information on the product and access to data please see https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/smos/news/-/article/smos-soil-moisture-product-in-nrt-based-on-neural-network-is-now-available

Kind regards,
Susanne Mecklenburg

SMOS L2 data is reprocessed!

Category : Data, L2, Satellite

At long last and after several glitches L2 Soil moisture reprocessing is over!
As you may be aware, ESA reprocessed level 1C with a fixed calibration (finished in January 2011) but could not reprocess level 2 in a timely fashion (typically November 2011) as it would have required on their system – assuming it was operational- anything between 4 and 6 months, i.e., too late wrt the general reprocessing. Hence the idea to wait for the official reprocessing in LTA facilities once they are operational, which implies a end of 2011 start.

So it was decided with ESA that the reprocessing would be done by CESBIO, using the reprocessed level 1C and the V4 of le Level 2 soil moisture algorithm configured exactly as it would be at DPGS (to ensure same format and consistent data set) and this on the CNES computing facilities. Ocean group was not interested in reprocessing just now incidentally, so it concerns only soil moisture.

François was fully in charge of this mammoth task!

Here is were we are today:
1) all data from DPGS L1C has been ingested.
* L1C data has been tested over DOME C and corresponds extremely well to ground measurements (see post on January 30 2011)
* the L2 SM V4 processor worked like a charm and all data has been processed as of yesterday evening

2) A few numbers…

We have processed
* 9377 L2 products (including 7394 Fully polarised) for the period ranging from 12/01/2010 to 26/12/2010.
* which corresponds to 714345863 nodes with roughly 77% leading to a retrieval
* this used up 38858cpu hours  (1600 days!!), and 46TB of memory(on average 4h and 5Go per product).
* input data takes up 3.2TB and outputs 1.4TB

3) We started the processing on 07/01/2011, (47 days overall) with 3 stops
- from 20/01 to 25/01 (5d) test in the CNES Computing building
- from 02/02 to 04/02 (3d) link to mass storage access issue (space where all the data was stored)
- from 09/02 to 15/02 (7d) as we were still waiting for the DPGS reprocessed L1c  data  finished on 18/01 at ESAC, which also disrupted the parallel processing
So the whole task took 31 days and,should the last batch from DPGS had been delivered in January (so as to have the 6 processes running at the same time but only shifted slightly), we can reasonably say that it would have taken 25 days (François’  first estimate!).

Now while François is resting a little (well .. doing something else), we are now intensively checking the outputs
In parallel Philippe used the Prototype to run tests on smaller data sets (USA, Africa and Australia) with a large number of algorithm versions and / or configurations  (309, 410 , dual in full vs full , with and without currents etc) we are comparing all the stuff (and it takes time and people!)
conclusions -very preliminary we still have lots to do, are very encouraging and we can safely state now that we realised that we had made a couple of blunders at the beginning (to much haste!) which means that the results are probably slightly less good than they could be, but up to now (we still have a fair bit to analyse) results look fine. A bit more « noisy » maybe, especially Tau but closer to field measurements and more realistic.

To make a long story short improvements!

We are now very busy checking everything and hope to be able to show many things at the quality working group getting the green light for dissemination to you all through ESA’s usual accesses of this ESA/CNES CESBIO reprocessed levels 1C and 2 SM.

More on this next week with results … stay tuned.

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