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.

blogyhk3

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.

Was 2015 a « DRY » year? and what about 2016?

Category : CATDS, L4

Several extreme drought events occurred in 2015 around the globe. At CESBIO, combining hydrological modelling and remotely sensed surface soil moisture from SMOS, we monitored a number of them. We used CATDS (Centre Aval de Traitement des données SMOS) products.

The aproach was to use our root zone soil moisture information derived from SMOS to infer a water scarcity index. Water scarcity in the root zone (0-1.5m) is actually an efficient early warning system for agricultural droughts.

droughts_2015_albitar

The figure above shows 5 of the major droughts which occurred in 2015. The small focus maps show the drought index during the drought events in each of the regions of interest. The losses caused by these droughts amount to billions.

So the next question is: are we facing long drought events that can impact food security at global scale?

In 2016 we may see even worse conditions. Our drought index seem to provide an alarming forecast. This was showcased by ESA during the Living Planet Symposium LPS2016 with this post using our latest root zone soil moisture map (see Water for crops – the SMOS root zone soil moisture).

We also produced the drought index map over North America for 2016 and it seems that after the Alberta fires and last year drought in the West coast of the US, the Eastern coast is now at risk. This forecast may change but it is clear that extremes conditions are breaking very old records, beyond the contribution of the El-Nino effect.

drought_index_north_america_2016

Using SMOS Soil Moisture Data for Global Food Security Monitoring

Category : Data, L4

From Wade CROW USDA

food security-wade

(left) Within Southern Africa, mid-April 2014 soil moisture fields derived via the USDA FAS water balance model. Data gaps and lack of spatial variability reflect very poor rain gauge coverage in the region.

(right) The analogous field after the assimilation of SMOS L2 surface soil moisture retrievals into the USDA FAS water balance model.

Agricultural drought plays a major role in driving inter-annual variations in agricultural productivity. Therefore, monitoring the availability of root-zone soil moisture is critical for predicting trends in agricultural markets and food availability in food-insecure regions. Recently, the United States Department of Agriculture (USDA) Foreign Agricultural Service (FAS) has begun to assimilate SMOS L2 surface soil moisture retrievals into their global soil moisture monitoring system. USDA FAS analysts use this system to make pre-harvest predictions of variations in global agricultural productivity. In the past, the USDA FAS root-zone soil moisture monitoring system relied on ground-based observations of precipitation and air temperature in order to indirectly estimate root-zone soil moisture via water balance principles. This approach is adequate in data-rich areas of the world like North American and Europe but was known to fail in data-poor (and food insecure) areas of Africa and Central Asian. The assimilation of SMOS surface soil moisture retrievals has, for the first time, given the USDA FAS the ability to accurate track root-zone soil moisture variations within data-poor regions. For example, the image below demonstrates the increase in soil moisture detail within Southern Africa associated with the assimilation of SMOS L2 soil moisture retrievals into the USDA FAS water balance model.

For specific examples of SMOS-based soil moisture products: 1) go here , 2) click on a geographic region of interest, and 3) select “SMOS Surface and Sub-Surface Soil Moisture” from the “Soil Moisture and Crop Models” pull-down tab.

For more information contact: Wade Crow (Wade.Crow@ars.usda.gov) or John Bolten (John.Bolten@nasa.gov) or see http://www.esa.int/Our_Activities/Observing_the_Earth/SMOS/Water_mission_boosts_food_security.

This project was based on funding from the NASA Applied Science Water Resources Program and application-orientated research by the USDA Agricultural Research Service, NASA Goddard Space Flight Center and the National Oceanic and Atmospheric Administration Satellite and Information Service.

SMOS Drought Monitor – CATDS L4

Category : CATDS, L3, L4

A new drought index has been developed at CESBIO by including SMOS surface soil moisture to a double bucket model. The index is computed operationally. You can access it on this (here) dedicated page or by clicking on « Drought Monitor » in the SMOS blog header tab


link

This new product is part of the level 4 SMOS products. These are end-level products, obtained by the combination of the SMOS data to physical models (hydrological, statistical with/without data assimilation…) or by using remote sensing data from other sensors (Disaggregation, data fusion, synergism …).

[map maptype=WMS kml="http://www.cesbio.ups-tlse.fr/SMOS_blog/wp-content/uploads/SDI_L4/kml/SDI.kml" style="width:600px; height:350px; border:1px solid gray; margin-right:20px; float:left;"]

You can also access the SMOS Africa Root zone soil moisture Index (SARI) in the Princeton Africa Drought Monitor (here) (look at 02 April 2012, this will be operational soon)

SMOS L4 team

(version Française)

Un nouvel indice de sécheresse issu de l’intégration des données SMOS dans un modèle hydrologique double réservoirs a été développé au CESBIO. Il est produit de façon operationnelle. Vous pouvez y accéder via le site dédié du produit en appuyant (içi)

Cet indice de sécheresse est un produit CATDS de niveau 4 SMOS. Les produits de niveau 4 issus de la mission SMOS sont des produits de niveau supérieur obtenus en combinant les produits SMOS de niveau 1,2 ou 3 à des modèles physiques ou en utilisant d’autres capteurs.

Vous pouvez aussi accéder à l’indice SARI (SMOS African Root zone soil moisture Index) dans le système  de suivi de la sécheresse  en Afrique mis en place à l’Université de Princeton (içi) (voir le 02/04/2012, cet indice sera mis en mode operationel dans les jours à venir)

africa

SMOS L4 team
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