SAT-IRR: Satellite for Irrigation Scheduling


Following the irrigation scheduling experiment in Morocco during the SPOT4-Take5 experiment (Le Page et al, 2014), a Web tool owing to help the irrigation decision making is under development ( The tool is functional on three Landsat8 tiles: Marrakech in Morocco, Kairouan in Tunisia, Toulouse, France.


As the tool is addressing irrigators, the idea is to set an irrigated plot of the simplest and fastest possible way. After drawing his plot on a base map, the user answers to 4 questions. He chose his culture among 7 options currently parameterized (corn, wheat, olive ...), its soil among the 12 USDA typical soils, the sowing date and irrigation method (flooding, sprinkler or drip). This rough initialization is adequate launch the service although, at any time, the user can change the plot contours or refine parameterization if he knows the soils, the peculiarities of its crop, etc.


Screenshot from SAT-IRR web interface. The four icons allow modifying the plot parameters and contours, input irrigation, and consulting the results as graphs or tables. The graph results show a small Openlayers window with the last NDVI image, the sequence of NDVI thumbnail images, and 4 graphics: The “atmospheric part (rainfall, Reference Evapotranspiration and actual evapotranspiration), the second graph shows the status of the soil water content separated in three layers, the third graph shows the evolution of Basal Crop Coefficient and Fraction Cover, and the last graph is NDVI. The blue square at the right of the graphs are projections for the next month, including the green bars which are irrigation recommendations

Initially the server makes an approximation of an average behavior of the plant. For this, a monthly climatology is compiled (multi-annual average of weather parameters) and then interpolated to daily values, while the average behavior of the plant is extracted from FAO-56 Tables "FAO Irrigation and Drainage No. 56 : Guidelines for Computing Crop Water Requirements" (Allen et al, 1998). In a second step, the satellite images already on the server are examined and the relationship between NDVI and Basal Crop coefficient (Kcb) and percent of ground covered by vegetation (Fraction cover, Fc) are determined at each date by averaging on the plot.
Past weather is populated by measurements on the nearest synoptic station of the World Meteorological Organization and synthesized in the form of daily reference evapotranspiration (ET0) and rainfall. Forecasts are obtained by the API of the Norwegian Meteorological Institute.
Finally, a water balance very close to the one described in the FAO-56 is calculated by combining typical crop behavior and climate, satellite imagery, weather data and forecasts and projection into the future of crop development. The goal is to propose a date and dose of irrigation.


In addition to updating the weather (measures and forecasts), the server will check every day for the availability of new images (only Landsat8 for the time being). If there is availability, the tile is downloaded, it is then corrected for atmospheric effects using the information provided by the nearest photometer from the Aeronet network using the SMAC code (Rahman & Dedieu, 1994), then a cloud mask is created and NDVI is calculated. This image is stored as the original file is discarded to not overload the server.
All parameterization / measures / prediction are stored in a postgres / postgis database that links with a web client interface. The user can view the results in tables or graphs, and add its own irrigation in another dedicated interface.
While the interface is still a little rough, we are essentially considering developments on the server side:

  • Adaptation to Sentinel-2: the transition to S2 should not be a hassle. However, it will be necessary to adjust the calculation of the tiles to download, the download code and format reading.
  • Use of Sentinel-1: In the current state, the well-performance of water balance is based on the actual information of irrigation provided by the user. We plan to test the use of S1 images to determine the irrigation dates.
  • Access to local agro-weather stations: As part of the development of the Environmental Information System in Cesbio, telemetry of several weather stations has been settled up (eg, see .ma) (Jarlan et al, 2015), we have to make these stations accessible through a standardized web service like Sensor Web.
  • Introduction of collective irrigation network. The PhD work of Kharrou (2013) and Belaqziz (2013, 2014) have shown that remote sensing can be used to optimize water rotations of an irrigated command. We plan to offer the possibility of introducing a set of plots to associate it with a distribution network and ultimately offer an optimized arrangement of the water rotation. However, at present, this goal is more into the order of a challenge!
  • We are currently working on a procedure to introduce wheat yield using remote sensing data (J. Toumi PhD Thesis) and further expect to input an early wheat yield prediction into the tool.

If you want to try it out, be my guest, it's free. If you want to try it out on other regions, please contact me!



  1. Le Page M., J. Toumi, S. Khabba, O. Hagolle, A. Tavernier, M. Kharrou, S. Er-Raki, M. Huc, M. Kasbani, A. Moutamanni, M. Yousfi, and L. Jarlan, “A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco,” Remote Sens., vol. 6, no. 11, pp. 11182–11203, Nov. 2014.
  2. Allen R., L. Pereira, D. Raes, and M. Smith, FAO Irrigation and Drainage n°56: Guidelines for Computing Crop Water Requirements, no. 56. FAO, 1998, pp. 273–282.
  3. Rahman H. and G. Dedieu, “SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum,” Int. J. Remote Sens., vol. 15, no. 1, pp. 123–143, 1994.
  4. Kharrou M.H., M. Le Page, A. Chehbouni, V. Simonneaux, S. Er-Raki, L. Jarlan, L. Ouzine, S. Khabba, and A. Chehbouni, “Assessment of Equity and Adequacy of Water Delivery in Irrigation Systems Using Remote Sensing-Based Indicators in Semi-Arid Region, Morocco,” Water Resour. Manag., vol. 27, no. 13, pp. 4697–4714, Sep. 2013.
  5. Belaqziz S., S. Mangiarotti, M. Le Page, S. Khabba, S. Er-Raki, T. Agouti, L. Drapeau, M. H. Kharrou, M. El Adnani, and L. Jarlan, “Irrigation scheduling of a classical gravity network based on the Covariance Matrix Adaptation – Evolutionary Strategy algorithm,” Comput. Electron. Agric., vol. 102, pp. 64–72, Mar. 2014.
  6. Belaqziz S., S. Khabba, S. Er-Raki, L. Jarlan, M. Le Page, M. H. Kharrou, M. El Adnani, and A. Chehbouni, “A new irrigation priority index based on remote sensing data for assessing the networks irrigation scheduling,” Agric. Water Manag., vol. 119, pp. 1–9, Mar. 2013.
  7. Jarlan L., S. Khabba, S. Er-raki, M. Le Page et al, “Remote sensing of water resources in semi-arid Mediterranean basins: The Joint International Laboratory TREMA,” Int. J. Remote Sens., vol. (under review), 2015.

Production d'un nouveau lot de données LANDSAT8 sur la France

Image typique de cette période du mois d’août 2014 (ici, la région de Champagne), mais en cherchant bien, on peut trouver quelques trous au milieu des nuages...

Les collègues du centre de production MUSCATE travaillant au CNES pour le centre de données THEIA rajoutent un nouveau lot de données LANDSAT 8 tous les 15 jours.Les dernières données produites vont du 3 au 16 août 2014, elles sont donc produites avec un délai d'un mois à un mois et demi par rapport à leur acquisition.


Nous pourrions faire un peu mieux, avec un risque de ne pas traiter certaines images si l'USGS ne les met pas à disposition rapidement. Cela arrive assez rarement, mais cela arrive. Faites nous savoir (en commentant ce blog) si vous avez vraiment besoin d'une mise à disposition plus rapide.


Les données sont disponibles ici, sans licence à signer, il suffit de s'inscrire :

Je vous rappelle qu'il est possible d'en télécharger un grand nombre d'un coup, comme expliqué ici

Choix des sites pour SPOT4 (Take 5)

(English Version)

Choisir les sites pour l'expérience SPOT4(Take5) ne fut pas de tout repos. Le délai de choix était très court, nous sentions que la demande serait forte, nous ne voulions pas être accusés de favoritisme et de copinage (et nous n'avons même pas accepté de cadeaux (1)).


Nous avons procédé de deux manières différentes pour les sites destinés aux laboratoires et organismes français d'une part, et pour les sites destinés aux agences spatiales d'autre part.


Emprise approximative des sites observés par SPOT4(Take5) en France et en Belgique

Pour les sites français, nous avons lancé un appel à proposition de sites, avec un délai de réponse très court (de l'ordre du mois), et c'est le comité TOSCA qui a procédé à la sélection. Cet appel d'offres a connu un grand succès, puisque 20 propositions ont été reçues, avec la participation de 81 laboratoires et organismes.


Les sites de SPOT4 (Take 5) en Afrique

Pour les sites internationaux, nous nous sommes rapprochés des agences spatiales ou de recherche impliquées dans le programme Sentinel-2, à savoir l'ESA, le JRC, la NASA ou le CCRS. Ici, l'accusation de copinage pourrait être retenue (mais nous n'avons pas reçu de cadeaux), nous n'avions pas le temps d'émettre un appel à proposition et de constituer un jury pour le choix. Cependant, la participation de ces agences implique de leur part une contribution financière pour l'achat des images de niveau 1A au fournisseur privé : Astrium Geo (anciennement Spot Image).


Bien évidemment, nous souhaitons que les images acquises pour l'expérience SPOT4(Take5) soient le plus largement diffusées. Les images seront donc disponibles pour toute personne en faisant la demande, pour une utilisation non commerciale dans le cadre de la préparation à Sentinel-2. Les demandes d'accès seront à adresser au Pôle Thématique Surfaces Continentales (PTSC)

(1) Par contre, maintenant que les sites sont choisis...