First SPOT5 (Take5) time series

SPOT5 (Take5) images for lake Chad, acquired on the 9th and 14th of April.

 

 

Airbus Defence and Space (Ex Spot Image), produces every week the new data downloaded from the satellite to level 1A (Thanks Myriam !).

 

We have now received nearly two entire cycles of SPOT5 (Take5) images, which means that we have our first time series, although made of only two dates ! Our processing center, MUSCATE, within THEIA Land data center in CNES, is now able to produce systematically the L1C data (ortho-rectified and converted to TOA reflectance). The THEIA teams (including exploitation, ground segment and image quality) did a very good work in chasing the last configuration glitches among the dozens of parameter files necessary to produce the data. A good point for me is that I had nearly nothing to do except writing this post.

 

As an example, the image on the right shows two different dates from the Lake Chad site. Although the footprints are different (SPOT5 footprints are not constant as we had a manoeuvre to avoid a debris between both acquisitions), the data are perfectly registered (see the zoom below) and the TOA reflectances are consistent.

 

To convince you I did not provide twice the same image, you may see in the attached zoom that some clouds are present in the first image (09/04) and not in the second (14/04), and that some floating vegetation crossed the lake in 5 days. The wind was probably blowing from the North on the14th of April or just before.

Click on the images for full resolution

Next steps in our processing will include the first test of Level 2A images, as soon as we get data from the third cycle. I am sorry to tell that users will still have to wait for several weeks, as the Level 2A processor needs one month to be correctly initialized, as we have to perform minimal checks of the quality of the products before releasing them, and as we need to finalize the download site.

Première ortho SPOT5 (Take5)

 

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Voici une copie d'écran de la première image SPOT5 (Take5) du lac Victoria en Ouganda, après ortho-rectification (L1C), superposée à une image similaire acquise par LANDSAT 8. Les deux images se superposent apparemment très bien, même si nous devons procéder à une validation plus rigoureuse. Ceci dit, même si ce n'est pas une surprise, c'est une bonne nouvelle que les processeurs commencent à marcher rapidement.

Première image SPOT5 (Take5) ortho-rectifiée. En couleurs, SPOT5 (Take5), en noir et blanc, LANDSAT 8.

Zoom sur le côté Est de l'image : les routes et la rivière correspondent parfaitement.

Nous commençons à rattraper notre retard dû à la définition tardive des sites. La plupart des données auxiliaires est prête, et la première ortho-rectification a été traitée au sein du processeur nominal, au centre THEIA du CNES, par l'équipe d'exploitation de MUSCATE. Un grand merci à tous ceux qui ont contribué à ce travail, notamment Karl, Dominique Vincent et Laurent !

 

 

 

First SPOT5 (Take5) Ortho

 

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Here is a screen copy of the first SPOT5 (Take5) ortho-rectified image (L1C) from the Lake Victoria image in Uganda, registered with a similar product from LANDSAT 8. Both images match very well, but we need to validate that much further. However, although it is not a surprise, it is good news that processors start to be working quickly.

First SPOT5 (Take5) ortho-rectified image. In colour SPOT5 (Take5) image, in black and white, a LANDSAT 8 image.

Zoom on the east side : the roads and river from SPOT5 (Take5), left, and LANDSAT 8, right, match perfectly

We are starting to catch-up our late start due to the late definition of the sites. Most of the ancillary data for the 150 sites are ready (still missing the most Northern sites), and the first ortho-rectification was processed on the nominal configuration at THEIA (CNES) by the MUSCATE exploitation team. Many thanks to all the colleagues who contributed to this work, especially Karl, Dominique Vincent and Laurent !

 

 

 

SPOT5 (TAKE5) data production schedule

The processing of SPOT5 (Take5) data is based on a prototype ground segment and involves prototype processors, and we have lots of configuration data to produce for each of the 150 sites. Moreover,  to reduce exploitation costs, the processing at Airbus DS for Level 1A, and then at CNES (L1C and L2A) are launched only once a week. And finally the production of level 2A  based on multi-temporal algorithms requires an initialization with one month of data. We also need to set the new distribution server up.

 

That's why we need two months after the first acquisitions to start distributing the data, which means that the data should be released before end of June (with some margins). After this initialization period, we should be able to deliver the data on the website 3 weeks after their acquisition.

 

Given the amount of work needed to do that, delays are possible.

 

 

SPOT5 (Take5) first image

First SPOT5 (Take5), acquired the 8th of April in Ouganda, North of Lake Victoria. To limit download time, it is only provided here at 60m resolution. The colour composite is : (red : SWIR, green : NIR, blue : Red)

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The first set of SPOT5 (Take5) images was acquired yesterday, 8th of April. Here is one of the images, processed by Airbus Defence and Space at Level 1A. This image has nothing special in itself, as it is only one of 12 000 000 SPOT5 images, but it is still the proof that everything went well at all stages, from programming to acquisition and processing, despite the fact that SPOT5 is not on its nominal orbit anymore. Many thanks to all the teams who worked hard these last days to achieve this !
This image will be followed by 29 more images on this site, acquired with a periodicity of five days. And the same will happen on 149 other sites. This is special !

Zoom at full resolution (Rhinoceros Island ? No, Sigulu Island )

We have now to turn the processing lines on, but as you know, the 150 sites were only defined lately, and we still have some configuration and verification work to do, before we start the first qualification production, and at the very end, the operational one. So please be patient, but stay tuned.

Première Image SPOT5 (Take5)

Première Image de SPOT5 (Take5) avec faible couverture nuageuse, acquise le 8 avril en Ouganda. La zone en eau est le lac Victoria. Pour limiter les temps de téléchargement, l'image est fournie à 60 de résolution. Sur la composition colorée, la bande MIR apparaît en rouge, la bande NIR en vert, et la bande rouge en bleu.

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La première série d'images de SPOT5 (Take5) a été acquise hier, le 8 avril. Voici un premier exemplaire de ces images traitées au Niveau 1A par Airbus Defense and Space. Cette image n'a rien de spécial en elle-même, ce n'est que l'une des 12 000 000 d'images acquises par SPOT5, mais elle prouve toutefois que toutes les étapes, de la programmation à l'acquisition et au traitement, se passent bien, alors que le satellite n'est pas sur son orbite nominale. Un grand merci à toutes les équipes qui ont travaillé dur ces dernières semaines pour obtenir ce résultat.

 

Cette image sera suivie par 29 autres acquisitions répétées tous les 5 jours sur ce site, et il en ira de même sur 149 autres sites, et ça, c'est spécial !

Zoom à pleine résolution (L'île du Rhinoceros ? Non, l'île Sigulu. )

Nous devons maintenant mettre en œuvre les chaînes de traitement de niveau 1C, et 2A, mais comme vous le savez si vous suivez ce blog, les sites ont été définis très récemment. Il nous reste encore du travail de configuration et de vérification, avant de lancer les premières production de qualification, suivies, prochainement, des productions opérationnelles. Donc soyez patients, mais suivez ce blog pour les prochaines nouvelles.

Updated SPOT5 (Take5) acquisition calendar

Update : I was wrong, 8th of September is a day #1 (one day error for me, one for the operation team). Calendar has been updated. Sorry !

I have added a calendar page to the SPOT(Take5) menu, which gives you the day number in the take5 cycle for each SPOT5 (Take5) site, and gives you the date the first first day of the cycle for each month.

The acquisitions start on the 8th of April, which is a day #1, until the 8th of September, which is a day #4.

 

J'ai ajouté une page "calendrier" dans le menu SPOT (Take5), qui vous donne le numéro du jour dans le cycle Take5 pour chaque site SPOT5 (Take5), et vous donne la date du premier premier jour du cycle.

Les acquisitions commencent le 8 avril (jour n°1) et se terminent le 8 septembre (jour n°4).

SPOT5 est sur l'orbite Take5/ SPOT5 is on Take5 orbit

 

Tout s'est très bien passé ce matin, et SPOT5 a atteint sa nouvelle orbite, 2,5 km plus bas. Félicitations et un grand merci à toute l'équipe des opérations du CNES ! Les premières images seront acquises la semaine prochaine. Ne quittez pas !

 

On m'a de nouveau demandé comment un petit changement d'altitude de 2 km pouvait produire un changement aussi important de cycle orbital (de 26 à 5 jours). C'est expliqué ici.

 

Everything went well this morning, and SPOT5 is now on its new orbit, 2.5 km lower. Congratulations and many thanks to CNES operations teams ! The first images will be acquired next week. Stay tuned !

 

I had that question again : why does an altitude change by only 2 kms result in an orbit cycle change from 26 days to 5 days ? It is explained here.

 

SAT-IRR: Satellite for Irrigation Scheduling

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Suite à l'expérience de pilotage d'irrigation menée au Maroc lors de l'expérience SPOT4-Take5 (Le Page et al, 2014), un outil Web d'aide à la prise de décision d'irrigation est en cours de développement (http://osr-cesbio.ups-tlse.fr/Satirr). L’outil est fonctionnel sur trois tuiles Landsat8 situées à Marrakech au Maroc, Kairouan en Tunisie, Toulouse en France.

 

L'outil s'adresse à des irrigants :  après avoir dessiné sa parcelle sur un fond cartographique, l’utilisateur répond à 4 questions. Il choisit sa culture parmi 7 options actuellement renseignées (maïs, blé, olivier…), son sol parmi les 12 sols type de l’USDA, sa date de semis et son mode d’irrigation (gravitaire, aspersion ou goutte à goutte). Cette initialisation sommaire est suffisante pour lancer le service, mais l’utilisateur pourra modifier à tout moment les contours de sa parcelle ou affiner la paramétrisation s’il connaît bien le sol, les particularités de sa culture, etc.

 

Dans un premier temps le serveur se charge de faire une approximation d’un comportement

satirr_screenshot

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

moyen de la plante. Pour cela, une climatologie mensuelle est compilée (moyenne multi-annuelle de paramètres météo) puis interpolée au pas de temps journalier, alors que le comportement moyen de la plante est tiré des tables du document FAO-56 « FAO Irrigation and Drainage n°56: Guidelines for Computing Crop Water Requirements » (Allen et al, 1998). Dans un second temps, les images satellites déjà présentes sur le serveur sont examinées puis les relations entre NDVI et Coefficient Cultural de Base (Basal Crop Coefficient, Kcb) et le pourcentage de la couverture du sol par la végétation (Fraction cover, Fc) sont déterminées à chaque date disponible en faisant une moyenne sur la parcelle.

 

La météo passée est renseignée par les mesures effectuées sur la station synoptique de l’Organisation Mondiale Météorologique la plus proche, et synthétisée quotidiennement sous la forme de l’évapotranspiration de référence (ET0) et de la pluie. Enfin, des prévisions météo sont obtenues grâce à l’API de l’Institut Météorologique Norvégien.

 

Finalement, un bilan hydrique très proche de celui décrit dans la méthode FAO-56 est calculé en combinant ainsi comportement cultural et climatologie type, imagerie satellitaire, mesures et prévisions météo ainsi que projection dans le futur du développement de la culture. Le but étant bien évidemment de proposer une date et dose d’irrigation.

 

En plus de mettre à jour la météo (mesures et prévisions), le serveur vérifiera chaque jour la disponibilité de nouvelles images (uniquement Landsat8 pour le moment). Si une nouvelle image est disponible, elle est téléchargée, corrigée des effets atmosphériques en utilisant les informations fournies par le photomètre du réseau Aeronet le plus proche en utilisant le code SMAC (Rahman & Dedieu, 1994), puis un masque de nuage est créé et le NDVI est calculé. Cette image est stockée alors que le fichier original est jeté pour ne pas encombrer le serveur.

 

L’ensemble paramétrisation/mesures/prévision est stocké sur une base postgres/postgis qui fait le lien avec une interface web. L’utilisateur peut consulter les résultats sous forme de tableaux ou de graphes, et rajouter ses propres irrigations dans une autre interface dédiée.

 

Bien que l’interface soit encore un peu fruste, nous envisageons surtout des développements du côté serveur:

  • Adaptation à Sentinel-2 : à priori le passage à S2 ne devrait pas poser de soucis. Il faudra cependant adapter le calcul des tuiles à télécharger, le code de téléchargement, ainsi que la lecture du format.
  • Utilisation de Sentinel-1: Dans l’état actuel, le bon fonctionnement du bilan hydrique repose sur l’information réelle de l’irrigation que doit fournir l’utilisateur. Nous prévoyons de tester l'utilisation d’images S1 pour déterminer les dates d'irrigation.
  • Accès à des stations agro-météo locales : Dans le cadre du développement du Système d’Information Environnemental au Cesbio, la télémétrie de plusieurs stations météo se met petit à petit en place (par exemple voir http://trema.ucam.ac.ma (Jarlan et al, 2015)), nous comptons rendre ces stations accessibles à travers un service web normalisé du type Sensor Web.
  • Introduction de réseau d’irrigation collective. Les travaux de thèse de Kharrou (2013) et Belaqziz (2013, 2014) ont montré que la télédétection spatiale peut servir à optimiser les tours d’eau sur un secteur irrigué. Nous comptons donc offrir la possibilité d’introduire un ensemble de parcelle pour l’associer à un réseau de distribution et proposer in fine un arrangement optimisé du tour d’eau. Cependant, à l’heure actuelle, cet objectif est plutôt de l’ordre du défi !
  • Nous travaillons actuellement sur une procédure d'estimation du rendement du blé avec la télédétection spatiale (Thèse J. Toumi) et espérons ainsi introduire une estimation précoce du rendement dans cet outil.

Si vous souhaitez essayer l'outil, inscrivez-vous, c'est gratuit. Si les régions de test ne vous conviennent pas, contactez-moi!

Références:

  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.

SAT-IRR: Satellite for Irrigation Scheduling

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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 (http://osr-cesbio.ups-tlse.fr/Satirr). 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.

satirr_screenshot

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 http://trema.ucam.ac .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!

 

References:

  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.