High spatial and temporal resolution optical remote sensing data to estimate maize biomass and yield

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Climate variability has a strong impact on maize yield. For example, the strong drought that occurred in 2016 led to lower yields across France, even for irrigated fields. Yield estimates have a significant strategic and economic importance. High spatial and temporal resolution remote sensing data are a valuable tool for providing yield estimates at a large scale.

 

In a recent study (Battude et al. 2016) based on optical image time series (combination of Formosat-2, Landsat-8, SPOT4-Take5 and Deimos-1, about two images per month), CESBIO researchers have developed a new method for the estimation of maize yield. A new formulation of SAFY agro-meteorological model taking into account of the observed seasonal variation of the specific leaf area (SLA) and the effective light use efficiency (ELUE) was proposed.

 

Results show that these modifications improve biomass estimates at local scale.

 

Comparison of measured and simulated Dry Aboveground Mass (DAM) with the original version of SAFY (left) and the new model version (right)


Yield estimates are compared to annual statistical values (Agreste) on two departments in the southwest of France : the Gers and the Haute-Garonne. Results show that the model reproduces well yields (R = 0.96; RRMSE = 4.6%), even if it sometimes overestimates the values for rainfed fields.

 

Comparison of simulated yield and Agreste values [t.ha-1] for the Gers and Haute-Garonne departments in 2013 (left) and 2014 (right), with the distinction between irrigated and rainfed fields. Standard errors associated to simulated values are reported.


GAI thus seems to be a good indicator for estimating the irrigated maize yield at regional scale. For rainfed fields, coupling SAFY with a water balance module simulating the soil water content  may improve yield estimates. Sentinel-2 mission offers new perspectives and its data should improve the model estimates.

 

Reference : Battude M., Al Bitar A., Morin D., Cros J., Huc M., Marais Sicre C., Le Dantec V., Demarez V. (2016) Estimating maize biomass and yield over large area using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment 184, 668-681 DOI: 10.1016/j.rse.2016.07.030

La télédétection optique à haute résolution spatiale et temporelle au service de l’estimation de la biomasse et du rendement du maïs

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La variabilité climatique a un fort impact sur le rendement du maïs. Par exemple, les fortes sécheresses de 2016 ont conduit, même pour les parcelles irriguées, à une baisse des rendements à travers la France. Les estimations des rendements présentent un enjeu stratégique et économique important. La télédétection à haute résolution spatiale et temporelle est un outil précieux pour l’estimation à large échelle de ces rendements.

 

Dans une étude récente (Battude et al. 2016) basée sur des séries temporelles d'images optiques (combinaison d'images Formosat-2, Landsat-8, SPOT4-Take5 et Deimos-1, environ deux images par mois), les chercheurs du CESBIO ont mis en place une  nouvelle méthode d’estimation du rendement de mais. Une nouvelle formulation du modèle agro-météorologique SAFY prenant en compte la variation saisonnière observée de la surface spécifique foliaire (SLA) et de l’efficience de conversion de la lumière (ELUE) a été proposée.

 

Les résultats montrent que ces modifications améliorent les estimations de la biomasse à l’échelle locale.

 

Comparaison de la biomasse (DAM pour Dry Aboveground Mass) simulée et  mesurée avec à gauche la version d’origine du modèle SAFY  et à droite la nouvelle version proposée.

 

Les estimations de rendement sont comparées à des valeurs statistiques annuelles (Agreste) sur deux départements du Sud-ouest de la France : le Gers et la Haute-Garonne. Les résultats montrent que le modèle reproduit bien les rendements (R = 0.96; RRMSE = 4.6%), même s’il surestime parfois les valeurs pour les parcelles non irriguées.

 

Comparaison du rendement simulé et des données Agreste [t.ha-1] pour les départements du Gers et de la Haute-Garonne en 2013 (à gauche) et en 2014 (à droite), avec la distinction entre les parcelles irriguées et non irriguées. L’erreur standard associée aux valeurs simulées est reportée.

 

Le GAI s’avère donc être un bon indicateur pour l’estimation du rendement du maïs irrigué à l’échelle régionale. Pour les parcelles non irriguées, le couplage de SAFY avec un module de bilan hydrique simulant le contenu en eau du sol pourrait permettre d’améliorer les estimations de rendement. La mission Sentinel-2 offre de nouvelles perspectives et les données devraient permettre d'améliorer les estimations du modèle.

 

Référence : Battude M., Al Bitar A., Morin D., Cros J., Huc M., Marais Sicre C., Le Dantec V., Demarez V. (2016) Estimating maize biomass and yield over large area using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment184, 668-681 DOI: 10.1016/j.rse.2016.07.030

Irrigated crop maps for a better water management in agriculture

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In a previous post, i have briefly presented:

  • issues related to the inherent water use in irrigated maize growing in France;
  • research projects related to this thematic where Cesbio is involved.

To classify irrigated farmland, within the growing period and at the scale of a territory, we focused on the use of optical remote sensing images. In the lines below, I will introduce the work to generate maps of irrigated crops usin Landsat-8 time series level 2A made available by Theia Land data center..

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An overview of irrigation evolution in Central Asia with Landsat

In Central Asia former soviet republics, pre-independence water allocation and irrigation system infrastructure were well maintained and operated with massive funding from the central government of the Former Soviet Union. Since independence, the situation has changed dramatically politically, institutionally and technically. Political transition from a planned to a market economy has introduced ‘new’ concepts such as land tenure, water rights and different kinds of ownership. The institutional changes are described as a transition from former state collective farms – kholkhoz and sovkhoz – to smaller private farms. (FAO report #39)
The Kyrgyz Republic is a landlocked country in Central Asia with a total area of 198 500 km2 and about 6 million inhabitants. It became independent from the Soviet Union in August 1991. Most of the land formerly controlled by the 195 kolkhoz (collective farms) and 275 sovkhoz (state farms) has been distributed to their employees and dependants in the form of certificates extending 99 years of land-use rights. 1 million hectares of fields are irrigated : almost all irrigation uses surface water, and only 4.4% of the water comes from groundwater (FAO report #39). FAO Aquastat surveys show that the Kirghiz consumption of water for agriculture has dropped from 9486Mm3 in 1994 to 7447 Mm3 in 2006 (-21%), but they also say that « These data should be used with caution, since the reason for this is not clear. It may be the result of computation methods, data quality, changed cropping pattern or improved irrigation techniques. »

 

We (1) have been looking at the evolution of an irrigated scheme on the southern bank of the Issyk Kul lake. The water which is coming from Karak Batak glacier melt, snow melt, and other rain runoff flows along the Chon Kyzyl Suu river which is then diverted to the Bolshoi and Polanski canals.

 

The statistics of water diversion (2) to those two canals show a huge drop between 1996 and 2004, followed by a flat evolution until 2012. The volume diverted in the 2000s is half the volume of the 1990s, so we would expect that the cropped area of the 90’s was much bigger than in the 2010’s.

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